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Table of Links Abstract and Introduction
SylloBio-NLI
Empirical Evaluation
Related Work
Conclusions
Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables I Prompting LLMs - Few-shot prompts I.1 TASK 1 Context: Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Instructions: Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. Relevance: If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Constraint: The output should be a single word <True> or <False>. Demonstration: "P1: " "Every member of Diseases of hemostasis pathway is a member of Disease pathway" "P2: " "Gene GP1BB is a member of Diseases of hemostasis pathway" "C:" "Gene GP1BB is a member of Disease pathway" The correct answer is: True "P1: " "Every member of Infectious disease pathway is a member of Disease pathway" "P2: " "Gene PKQQ is a member of Infectious disease pathway" "C:" "Gene PKQQ is a member of Infectious disease pathway" The correct answer is: True "P1: " "Every member of SLC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "P2: " "Gene AXZY is a member of SLC transporter disorders pathway" "C:" "Gene AXZY is not a member of Disorders of transmembrane transporters pathway" The correct answer is: False "P1: " "Every member of HIV Life Cycle pathway is a member of HIV Infection pathway" "P2: " "Gene MLLX is a member of HIV Life Cycle pathway" "C:" "Gene MLLW is a member of HIV Infection pathway" The correct answer is: False "P1: " "Every member of ABC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "P2: " "Gene PSMC5 is a member of ABC transporter disorders pathway" "C:" "It is true that Gene PSMC5 is a member of Disorders of transmembrane transporters pathway" I.2 TASK 2 Context: Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Instructions*: Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises.* Relevance: If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Specify the premises you used to determine whether the conclusion logically follows from the premises, and only these premises. Constraint: The output should be a single word <True> or <False> and the numbers of the selected premises after the decimal point, like <True, P1, P2>. "P1: " "Every member of Diseases of hemostasis pathway is a member of Disease pathway" "P2: " "Every member of NS1 Mediated Effects on Host Pathways pathway is a member of Influenza Infection pathway" "P3: " "Gene AABC is a member of Diseases of hemostasis pathway" " C:" "Gene AABC is a member of Disease pathway" The correct answer is: True, P1, P3 "P1: " "Every member of SARS-CoV Infections pathway is a member of Viral Infection Pathways pathway" "P2: " "Every member of Infectious disease pathway is a member of Disease pathway" "P3: " "Gene PKQQ is a member of Infectious disease pathway" "C:" "Gene PKQQ is a member of Infectious disease pathway" The correct answer is: True, P2, P3 "P1: " "Every member of SLC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "P2: " "Gene AXZY is a member of SLC transporter disorders pathway" "C:" "Gene AXZY is not a member of Disorders of transmembrane transporters pathway" The correct answer is: False "P1: " "Every member of HIV Life Cycle pathway is a member of HIV Infection pathway" "P2: " "Gene MLLX is a member of HIV Life Cycle pathway" "C:" "Gene MLLW is a member of HIV Infection pathway" The correct answer is: False "P1: " "Every member of ABC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "P2: " "Gene PSMC5 is a member of ABC transporter disorders pathway" "C:" "It is true that Gene PSMC5 is a member of Disorders of transmembrane transporters pathway" Authors:
(1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom;
(2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom;
(3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I;
(4) Marco Valentino, Idiap Research Institute, Switzerland;
(5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. Table of Links Abstract and Introduction SylloBio-NLI Empirical Evaluation Related Work Conclusions Limitations and References Abstract and Introduction Abstract and Introduction SylloBio-NLI SylloBio-NLI Empirical Evaluation Empirical Evaluation Related Work Related Work Conclusions Conclusions Limitations and References Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details F. Experimental Details G. Evaluation Metrics G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables M Supplementary Figures and N Supplementary Tables I Prompting LLMs - Few-shot prompts I.1 TASK 1 Context: Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Instructions: Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. Relevance: If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Constraint: The output should be a single word <True> or <False>. The output should be a single word <True> or <False>. Demonstration: "P1: " "Every member of Diseases of hemostasis pathway is a member of Disease pathway" "Every member of Diseases of hemostasis pathway is a member of Disease pathway" "P2: " "Gene GP1BB is a member of Diseases of hemostasis pathway" "Gene GP1BB is a member of Diseases of hemostasis pathway" "C:" "Gene GP1BB is a member of Disease pathway" "Gene GP1BB is a member of Disease pathway" The correct answer is: True "P1: " "Every member of Infectious disease pathway is a member of Disease pathway" "Every member of Infectious disease pathway is a member of Disease pathway" "P2: " "Gene PKQQ is a member of Infectious disease pathway" "Gene PKQQ is a member of Infectious disease pathway" "C:" " Gene PKQQ is a member of Infectious disease pathway" Gene PKQQ is a member of Infectious disease pathway" The correct answer is: True "P1: " "Every member of SLC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "Every member of SLC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "P2: " "Gene AXZY is a member of SLC transporter disorders pathway" "Gene AXZY is a member of SLC transporter disorders pathway" "C:" "Gene AXZY is not a member of Disorders of transmembrane transporters pathway" "Gene AXZY is not a member of Disorders of transmembrane transporters pathway" The correct answer is: False "P1: " "Every member of HIV Life Cycle pathway is a member of HIV Infection pathway" "Every member of HIV Life Cycle pathway is a member of HIV Infection pathway" "P2: " "Gene MLLX is a member of HIV Life Cycle pathway" "Gene MLLX is a member of HIV Life Cycle pathway" "C:" "Gene MLLW is a member of HIV Infection pathway" "Gene MLLW is a member of HIV Infection pathway" The correct answer is: False "P1: " "Every member of ABC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "Every member of ABC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "P2: " "Gene PSMC5 is a member of ABC transporter disorders pathway" "Gene PSMC5 is a member of ABC transporter disorders pathway" "C:" "It is true that Gene PSMC5 is a member of Disorders of transmembrane transporters pathway" "It is true that Gene PSMC5 is a member of Disorders of transmembrane transporters pathway" I.2 TASK 2 Context: Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes . Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes Instructions*: Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises.* Relevance: If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Specify the premises you used to determine whether the conclusion logically follows from the premises, and only these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Specify the premises you used to determine whether the conclusion logically follows from the premises, and only these premises. Constraint: The output should be a single word <True> or <False> and the numbers of the selected premises after the decimal point, like <True, P1, P2>. The output should be a single word <True> or <False> and the numbers of the selected premises after the decimal point, like <True, P1, P2>. "P1: " "Every member of Diseases of hemostasis pathway is a member of Disease pathway" "P1: " "Every member of Diseases of hemostasis pathway is a member of Disease pathway" "P2: " "Every member of NS1 Mediated Effects on Host Pathways pathway is a member of Influenza Infection pathway" "P3: " "Gene AABC is a member of Diseases of hemostasis pathway" " "P2: " "Every member of NS1 Mediated Effects on Host Pathways pathway is a member of Influenza Infection pathway" "P3: " "Gene AABC is a member of Diseases of hemostasis pathway" " C:" "Gene AABC is a member of Disease pathway" C:" "Gene AABC is a member of Disease pathway" The correct answer is: True, P1, P3 "P1: " "Every member of SARS-CoV Infections pathway is a member of Viral Infection Pathways pathway" "P1: " "Every member of SARS-CoV Infections pathway is a member of Viral Infection Pathways pathway" "P2: " "Every member of Infectious disease pathway is a member of Disease pathway" "P2: " "Every member of Infectious disease pathway is a member of Disease pathway" "P3: " "Gene PKQQ is a member of Infectious disease pathway" "P3: " "Gene PKQQ is a member of Infectious disease pathway" "C:" "Gene PKQQ is a member of Infectious disease pathway" "C:" "Gene PKQQ is a member of Infectious disease pathway" The correct answer is: True, P2, P3 "P1: " "Every member of SLC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "P1: " "Every member of SLC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "P2: " "Gene AXZY is a member of SLC transporter disorders pathway" "P2: " "Gene AXZY is a member of SLC transporter disorders pathway" "C:" "Gene AXZY is not a member of Disorders of transmembrane transporters pathway" "C:" "Gene AXZY is not a member of Disorders of transmembrane transporters pathway" The correct answer is: False "P1: " "Every member of HIV Life Cycle pathway is a member of HIV Infection pathway" "P1: " "Every member of HIV Life Cycle pathway is a member of HIV Infection pathway" "P2: " "Gene MLLX is a member of HIV Life Cycle pathway" "P2: " "Gene MLLX is a member of HIV Life Cycle pathway" "C:" "Gene MLLW is a member of HIV Infection pathway" "C:" "Gene MLLW is a member of HIV Infection pathway" The correct answer is: False "P1: " "Every member of ABC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "P1: " "Every member of ABC transporter disorders pathway is a member of Disorders of transmembrane transporters pathway" "P2: " "Gene PSMC5 is a member of ABC transporter disorders pathway" "P2: " "Gene PSMC5 is a member of ABC transporter disorders pathway" "C:" "It is true that Gene PSMC5 is a member of Disorders of transmembrane transporters pathway" "C:" "It is true that Gene PSMC5 is a member of Disorders of transmembrane transporters pathway" Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. Authors: Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. This paper is available on arxiv under CC BY-NC-SA 4.0 license. available on arxiv

Few-shot Prompting for Logical Reasoning Tasks in Biological Pathways

Table of Links Abstract and Introduction
SylloBio-NLI
Empirical Evaluation
Related Work
Conclusions
Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables H Prompting LLMs - Zero-shot prompts H.1 TASK 1 prompt = Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. The output should be a single word <True> or <False>. "P1: " + Premise 1 "P2: " + Premise 2 "C:" + Conclusion H.2 TASK 2 prompt = Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Specify the premises you used to determine whether the conclusion logically follows from the premises, and only these premises. The output should be a single word <True> or <False>and the numbers of the selected premises after the decimal point, like <True, P1, P2>. "P1: " + Premise 1 "P2: " + Premise 2 "C:" + Conclusion Authors:
(1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom;
(2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom;
(3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I;
(4) Marco Valentino, Idiap Research Institute, Switzerland;
(5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. Table of Links Abstract and Introduction SylloBio-NLI Empirical Evaluation Related Work Conclusions Limitations and References Abstract and Introduction Abstract and Introduction SylloBio-NLI SylloBio-NLI Empirical Evaluation Empirical Evaluation Related Work Related Work Conclusions Conclusions Limitations and References Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details F. Experimental Details G. Evaluation Metrics G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables M Supplementary Figures and N Supplementary Tables H Prompting LLMs - Zero-shot prompts H.1 TASK 1 prompt = Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. The output should be a single word <True> or <False>. Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. The output should be a single word <True> or <False>. "P1: " + Premise 1 "P2: " + Premise 2 "C:" + Conclusion H.2 TASK 2 prompt = Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Specify the premises you used to determine whether the conclusion logically follows from the premises, and only these premises. The output should be a single word <True> or <False>and the numbers of the selected premises after the decimal point, like <True, P1, P2>. Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Specify the premises you used to determine whether the conclusion logically follows from the premises, and only these premises. The output should be a single word <True> or <False>and the numbers of the selected premises after the decimal point, like <True, P1, P2>. "P1: " + Premise 1 "P2: " + Premise 2 "C:" + Conclusion Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. Authors: Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. This paper is available on arxiv under CC BY-NC-SA 4.0 license. available on arxiv

Zero-shot Prompts for Logical Reasoning Tasks in Biological Pathways

Table of Links Abstract and Introduction
SylloBio-NLI
Empirical Evaluation
Related Work
Conclusions
Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables C Dictionary of gene and pathway membership Reactome[4] (version 88—March 2024) has entries for 11 226 protein-coding genes involved in 15 212 human reactions annotated from 38 549 literature references. These reactions are grouped into 2 698 pathways collected under 29 superpathways (e.g. Immune System) that describe normal cellular functions. Each superpathway is represented as a roughly circular ‘burst,’ with the central node corresponding to the top-level of the Reactome event hierarchy and concentric rings representing increasingly specific levels of the event hierarchy (sub-pathways) (e.g. Disease → Diseases of signal transduction by growth factor receptors and second messengers → Signalling by EGFR in Cancer → Signalling by Ligand-Responsive EGFR Variants in Cancer → Constitutive Signalling by Ligand-Responsive EGFR Cancer Variants). The relationships between these pathways are captured through parent-child arcs, reflecting the ontological "is-a" relationships. The 29 Reactome superpathways group are each organized as a roughly circular ‘burst’. However, we built the corpus based on one, largest group of pathways called Disease. The central node of the Disease burst corresponds to the uppermost level of the Reactome event hierarchy (Table 2). Concentric rings of nodes around the central node represent successive more specific levels of the event hierarchy (e.g. Disease → Diseases of signal transduction by growth factor receptors and second messengers → Signalling by EGFR in Cancer → Signalling by Ligand-Responsive EGFR Variants in Cancer → Constitutive Signalling by Ligand-Responsive EGFR Cancer Variants). The arcs connecting nodes between successive rings within a burst represent parent–child (is-a) relationships in the event hierarchy. When a specific pathway is shared by more than one burst, arcs connect its nodes between bursts. A node’s size is proportional to the number of physical entities (proteins, complexes, chemicals) it contains. The dictionary of entity-names and predicates based on Reactome knowledgebase are available on GitHub. Authors:
(1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom;
(2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom;
(3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I;
(4) Marco Valentino, Idiap Research Institute, Switzerland;
(5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. [4] https://reactome.org Table of Links Abstract and Introduction SylloBio-NLI Empirical Evaluation Related Work Conclusions Limitations and References Abstract and Introduction Abstract and Introduction SylloBio-NLI SylloBio-NLI Empirical Evaluation Empirical Evaluation Related Work Related Work Conclusions Conclusions Limitations and References Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details F. Experimental Details G. Evaluation Metrics G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables M Supplementary Figures and N Supplementary Tables C Dictionary of gene and pathway membership Reactome[4] (version 88—March 2024) has entries for 11 226 protein-coding genes involved in 15 212 human reactions annotated from 38 549 literature references. These reactions are grouped into 2 698 pathways collected under 29 superpathways (e.g. Immune System) that describe normal cellular functions. Each superpathway is represented as a roughly circular ‘burst,’ with the central node corresponding to the top-level of the Reactome event hierarchy and concentric rings representing increasingly specific levels of the event hierarchy (sub-pathways) (e.g. Disease → Diseases of signal transduction by growth factor receptors and second messengers → Signalling by EGFR in Cancer → Signalling by Ligand-Responsive EGFR Variants in Cancer → Constitutive Signalling by Ligand-Responsive EGFR Cancer Variants). The relationships between these pathways are captured through parent-child arcs, reflecting the ontological "is-a" relationships. The 29 Reactome superpathways group are each organized as a roughly circular ‘burst’. However, we built the corpus based on one, largest group of pathways called Disease. The central node of the Disease burst corresponds to the uppermost level of the Reactome event hierarchy (Table 2). Concentric rings of nodes around the central node represent successive more specific levels of the event hierarchy (e.g. Disease → Diseases of signal transduction by growth factor receptors and second messengers → Signalling by EGFR in Cancer → Signalling by Ligand-Responsive EGFR Variants in Cancer → Constitutive Signalling by Ligand-Responsive EGFR Cancer Variants). The arcs connecting nodes between successive rings within a burst represent parent–child (is-a) relationships in the event hierarchy. When a specific pathway is shared by more than one burst, arcs connect its nodes between bursts. A node’s size is proportional to the number of physical entities (proteins, complexes, chemicals) it contains. The dictionary of entity-names and predicates based on Reactome knowledgebase are available on GitHub. Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. Authors: Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. This paper is available on arxiv under CC BY-NC-SA 4.0 license. available on arxiv [4] https://reactome.org

Reactome Knowledgebase: Gene and Pathway Membership in Disease-Related Pathways

Authors:
(1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France;
(2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France;
(3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Table of Links Abstract and 1. Introduction 2. Background & Preliminaries 2.1. Functional Isolation Forest 2.2. The Signature Method 3. Signature Isolation Forest Method 4. Numerical Experiments 4.1. Parameters Sensitivity Analysis 4.2. Advantages of (K-)SIF over FIF 4.3. Real-data Anomaly Detection Benchmark 5. Discussion & Conclusion, Impact Statements, and References Appendix A. Additional Information About the Signature B. K-SIF and SIF Algorithms C. Additional Numerical Experiments 2.2. The Signature Method The signature of a path is a sequence of iterated integrals that captures important information about the path’s geometric and topological features (Lyons et al., 2007; Fermanian, 2021). Furthermore, the signature of X is defined as the infinite collection of coordinate signature One may refer to Lee and Oberhauser (2023) for an account of the untruncated kernel signature. See also Section A in the Appendix for further details about the signature and its properties. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France; (2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France; (3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Authors: Authors: (1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France; (2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France; (3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Background & Preliminaries 2. Background & Preliminaries 2.1. Functional Isolation Forest 2.1. Functional Isolation Forest 2.2. The Signature Method 2.2. The Signature Method 3. Signature Isolation Forest Method 3. Signature Isolation Forest Method 4. Numerical Experiments 4. Numerical Experiments 4.1. Parameters Sensitivity Analysis 4.1. Parameters Sensitivity Analysis 4.2. Advantages of (K-)SIF over FIF 4.2. Advantages of (K-)SIF over FIF 4.3. Real-data Anomaly Detection Benchmark 4.3. Real-data Anomaly Detection Benchmark 5. Discussion & Conclusion, Impact Statements, and References 5. Discussion & Conclusion, Impact Statements, and References Appendix A. Additional Information About the Signature A. Additional Information About the Signature B. K-SIF and SIF Algorithms B. K-SIF and SIF Algorithms C. Additional Numerical Experiments C. Additional Numerical Experiments 2.2. The Signature Method The signature of a path is a sequence of iterated integrals that captures important information about the path’s geometric and topological features (Lyons et al., 2007; Fermanian, 2021). Furthermore, the signature of X is defined as the infinite collection of coordinate signature Furthermore, the signature of X is defined as the infinite collection of coordinate signature X One may refer to Lee and Oberhauser (2023) for an account of the untruncated kernel signature. See also Section A in the Appendix for further details about the signature and its properties. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Unveiling Path Signatures: A Key to Geometric Insights in Data

Onthulling van padhandtekeninge: 'n Sleutel tot meetkundige insigte in data

Descubriendo las firmas de trayectoria: una clave para obtener información geométrica en los datos

Revelación de sinaturas de camiños: unha clave para coñecer os datos xeométricos

Mengungkap Tanda Jalur: Kunci Wawasan Geometris dalam Data

パスシグネチャの解明: データの幾何学的洞察への鍵

Жол қолтаңбаларын ашу: деректердегі геометриялық түсініктердің кілті

경로 서명 공개: 데이터에서 기하학적 통찰력의 열쇠

Famoahana sonia lalana: lakile ho an'ny fahitana geometrika amin'ny angona

د لارې لاسلیکونه افشا کول: په ډیټا کې د جیومیټریک بصیرت لپاره کلیدي

Kuzarura Nzira Masiginicha: Kiyi yeGeometric Insights muData

Zbulimi i nënshkrimeve të rrugës: Një çelës për njohuritë gjeometrike në të dhëna

راستے کے دستخطوں کی نقاب کشائی: ڈیٹا میں جیومیٹرک بصیرت کی کلید

Implementing the Blocked Floyd-Warshall Algorithm for Solving All-pairs Shortest Path Problem in C#

Many people — particularly those outside the tech world — were introduced to OpenAI in November 2022, when the company launched its now-ubiquitous ChatGPT tool. However, it has been around for a lot longer than that. OpenAI captured headlines again more recently due to plans for a corporate restructuring that would mean it leaves behind its nonprofit roots. OpenAI’s Journey OpenAI started in 2015 as a nonprofit research organization dedicated to developing artificial intelligence tools that would benefit humanity as a whole, unrestricted by the need to turn a profit. Elon Musk and current OpenAI CEO Sam Altman were involved then, but Musk left in 2018. A year later, OpenAI added a for-profit subsidiary, turning the company into a hybrid for-profit and nonprofit entity in what it called a “capped profit” structure. Leaders at the time believed this change would keep OpenAI true to its roots while allowing it to use market power to grow its operations and revenue. The “capped” part of the structure’s name refers to limitations whereby the profits returned to the company’s investors can only be roughly 100 times what they put into the organization. Now, OpenAI’s structure seems set to change again, this time to a for-profit benefit corporation. This type of business focuses on sustainability and social responsibility in addition to profitmaking. One of the biggest differences about this development is that it would result in OpenAI’s nonprofit board losing control. For now, the nonprofit board has full control over the company’s for-profit arm. Another change is that Altman would receive equity for the first time — which could be worth billions. Moreover, the transition to a for-profit benefit company would remove the cap on investors’ returns. However, an interesting clarification came from an OpenAI representative who said the company’s nonprofit component would still exist and remain a core part of its goals. When will this restructuring occur? That’s anyone’s guess since those involved are still hashing out the details with legal professionals. However, as soon as the news reached major reporting outlets, some people started thinking about what it could mean for OpenAI and artificial intelligence in general. What Will the Restructuring Mean? Some people who have analyzed the matter said OpenAI’s restructuring might mean it will begin operating more like a startup. Investors would probably like that, but could it result in fewer checks and balances affecting products and processes? Contrary to its name, the company has never been upfront about its operations — employees must sign nondisclosure agreements that stop them from raising concerns to federal regulators. Additionally, a group of people working in the AI industry — including former OpenAI employees — signed an open letter explaining that those working on pioneering innovations in artificial intelligence have strong financial incentives to resist oversight and not share knowledge of what their products could or could not do with governments or civil society entities. Numerous employees have recently left OpenAI, with some, speaking anonymously, saying that the catalyst was a lack of trust in Altman and his ability to make AI work safely. Many have kept quiet because of a reported agreement people must sign when leaving the company that prevents them from speaking disparagingly about it. Some former workers who did not sign such paperwork have spoken out about their worries about the businesses’ increasingly powerful AI and the people who want it built not proceeding carefully enough. If OpenAI’s nonprofit board loses its current control, that may make the company’s leaders feel more equipped to proceed with whatever projects garner the most attention. Rushing to get these offerings on the market could also mean it doesn’t take safety as seriously as it should. OpenAI’s Impact on the Future of Artificial Intelligence Now that many AI tools are widely available, more people are insisting on controls that limit their potential applications. Others worry about the future, not necessarily because they believe that artificial intelligence will become smarter than humans, but due to privacy concerns, the prospect of job losses, and the potential for people to misuse AI. The shift to a for-profit model might also raise questions about priorities between profit and ethical AI development. Because of these concerns, transparency becomes even more crucial for the growing number of for-profit AI companies as they try to attract stakeholders — particularly as 60% of people cite transparency as the most important brand trait, according to one study. Embracing transparency can help build trust and reassure stakeholders that OpenAI remains committed to its foundational goals, even as its business model evolves. However, that sentiment could become problematic since OpenAI already has a reputation for being tight-lipped about its activities. People appreciate transparent companies, and it is arguably even more important to show that trait when the business specializes in a technology that the public views as largely foreign and new. For the average person who is merely curious about AI and how to use it, however, they likely won’t feel the impacts of OpenAI’s likely restructuring — at least not at first. Regular consumers will mostly pay attention to which products the company releases next. If those offerings become extremely profitable, such results will signal to its leaders that they should keep doing more of the same. It’s worth highlighting how OpenAI and the industry, in general, are already highly influential, with economic analysts saying generative AI will cause a 7% increase in global gross domestic product, resulting in a difference worth trillions of dollars. A related aspect is how relatively quickly people have gotten to know some of its tools and used them to do business and everyday tasks differently. Many individuals — even those who do not consider themselves tech-savvy — use AI numerous times throughout a typical week. That is almost certainly because of how accessible ChatGPT has become. Its popularity has helped OpenAI come into the mainstream, which arguably has implications for wider society — regardless of the ultimate impact of the eventual restructuring. An Uncertain But AI-Driven Future It’s too early to know the impacts of OpenAI transitioning to a for-profit benefit corporation. However, people can expect some aspects of its internal operations, stakeholder impact, and leadership to change in the coming months and years. We know with more confidence that artificial intelligence will continue playing a role in many people’s lives and shaping the tools they are now accustomed to using. Since OpenAI has enjoyed so much momentum in an increasingly crowded industry, its main task for the foreseeable future is to maintain it. If that happens, it will continue playing a significant role in artificial intelligence’s future, even if people don’t know what that looks like yet. Many people — particularly those outside the tech world — were introduced to OpenAI in November 2022, when the company launched its now-ubiquitous ChatGPT tool. However, it has been around for a lot longer than that. OpenAI captured headlines again more recently due to plans for a corporate restructuring that would mean it leaves behind its nonprofit roots. OpenAI’s Journey OpenAI started in 2015 as a nonprofit research organization dedicated to developing artificial intelligence tools that would benefit humanity as a whole, unrestricted by the need to turn a profit. Elon Musk and current OpenAI CEO Sam Altman were involved then, but Musk left in 2018. A year later, OpenAI added a for-profit subsidiary, turning the company into a hybrid for-profit and nonprofit entity in what it called a “capped profit” structure. Leaders at the time believed this change would keep OpenAI true to its roots while allowing it to use market power to grow its operations and revenue. The “capped” part of the structure’s name refers to limitations whereby the profits returned to the company’s investors can only be roughly 100 times what they put into the organization. roots while allowing it to use roots while allowing it to use Now, OpenAI’s structure seems set to change again, this time to a for-profit benefit corporation. This type of business focuses on sustainability and social responsibility in addition to profitmaking. One of the biggest differences about this development is that it would result in OpenAI’s nonprofit board losing control. For now, the nonprofit board has full control over the company’s for-profit arm. Another change is that Altman would receive equity for the first time — which could be worth billions. Moreover, the transition to a for-profit benefit company would remove the cap on investors’ returns. However, an interesting clarification came from an OpenAI representative who said the company’s nonprofit component would still exist and remain a core part of its goals. nonprofit component would still exist nonprofit component would still exist When will this restructuring occur? That’s anyone’s guess since those involved are still hashing out the details with legal professionals. However, as soon as the news reached major reporting outlets, some people started thinking about what it could mean for OpenAI and artificial intelligence in general. What Will the Restructuring Mean? Some people who have analyzed the matter said OpenAI’s restructuring might mean it will begin operating more like a startup. Investors would probably like that, but could it result in fewer checks and balances affecting products and processes? Contrary to its name, the company has never been upfront about its operations — employees must sign nondisclosure agreements that stop them from raising concerns to federal regulators. must sign nondisclosure agreements must sign nondisclosure agreements Additionally, a group of people working in the AI industry — including former OpenAI employees — signed an open letter explaining that those working on pioneering innovations in artificial intelligence have strong financial incentives to resist oversight and not share knowledge of what their products could or could not do with governments or civil society entities. Numerous employees have recently left OpenAI, with some, speaking anonymously, saying that the catalyst was a lack of trust in Altman and his ability to make AI work safely. Many have kept quiet because of a reported agreement people must sign when leaving the company that prevents them from speaking disparagingly about it. Some former workers who did not sign such paperwork have spoken out about their worries about the businesses’ increasingly powerful AI and the people who want it built not proceeding carefully enough. businesses’ increasingly powerful AI businesses’ increasingly powerful AI If OpenAI’s nonprofit board loses its current control, that may make the company’s leaders feel more equipped to proceed with whatever projects garner the most attention. Rushing to get these offerings on the market could also mean it doesn’t take safety as seriously as it should. OpenAI’s Impact on the Future of Artificial Intelligence Now that many AI tools are widely available, more people are insisting on controls that limit their potential applications. Others worry about the future, not necessarily because they believe that artificial intelligence will become smarter than humans, but due to privacy concerns, the prospect of job losses, and the potential for people to misuse AI. The shift to a for-profit model might also raise questions about priorities between profit and ethical AI development. Because of these concerns, transparency becomes even more crucial for the growing number of for-profit AI companies as they try to attract stakeholders — particularly as 60% of people cite transparency as the most important brand trait, according to one study. Embracing transparency can help build trust and reassure stakeholders that OpenAI remains committed to its foundational goals, even as its business model evolves. 60% of people cite transparency 60% of people cite transparency However, that sentiment could become problematic since OpenAI already has a reputation for being tight-lipped about its activities. People appreciate transparent companies, and it is arguably even more important to show that trait when the business specializes in a technology that the public views as largely foreign and new. For the average person who is merely curious about AI and how to use it, however, they likely won’t feel the impacts of OpenAI’s likely restructuring — at least not at first. Regular consumers will mostly pay attention to which products the company releases next. If those offerings become extremely profitable, such results will signal to its leaders that they should keep doing more of the same. It’s worth highlighting how OpenAI and the industry, in general, are already highly influential, with economic analysts saying generative AI will cause a 7% increase in global gross domestic product, resulting in a difference worth trillions of dollars. A related aspect is how relatively quickly people have gotten to know some of its tools and used them to do business and everyday tasks differently. Many individuals — even those who do not consider themselves tech-savvy — use AI numerous times throughout a typical week. will cause a 7% increase will cause a 7% increase That is almost certainly because of how accessible ChatGPT has become. Its popularity has helped OpenAI come into the mainstream, which arguably has implications for wider society — regardless of the ultimate impact of the eventual restructuring. An Uncertain But AI-Driven Future It’s too early to know the impacts of OpenAI transitioning to a for-profit benefit corporation. However, people can expect some aspects of its internal operations, stakeholder impact, and leadership to change in the coming months and years. We know with more confidence that artificial intelligence will continue playing a role in many people’s lives and shaping the tools they are now accustomed to using. Since OpenAI has enjoyed so much momentum in an increasingly crowded industry, its main task for the foreseeable future is to maintain it. If that happens, it will continue playing a significant role in artificial intelligence’s future, even if people don’t know what that looks like yet.

Inside OpenAI's Path to Becoming a For-Profit Powerhouse

Embracing the Open-Source Revolution: A New Pathway for MSP Growth

Рэвалюцыя адкрытага кода: новы шлях для росту MSP

Adoptar la revolución del código abierto: un nuevo camino para el crecimiento de los MSP

Adopter la révolution de l'open source : une nouvelle voie pour la croissance des MSP

A nyílt forráskódú forradalom felkarolása: Új út az MSP növekedéséhez

Merangkul Revolusi Open-Source: Jalan Baru untuk Pertumbuhan MSP

Abbracciare la rivoluzione open source: un nuovo percorso per la crescita degli MSP

オープンソース革命の受け入れ: MSP 成長への新たな道

ღია წყაროს რევოლუციის მომხრე: MSP ზრდის ახალი გზა

Përqafimi i Revolucionit me Burim të Hapur: Një rrugë e re për rritjen e MSP

ምሕቋፍ ክፉት ምንጪ ሰውራ፡ ሓድሽ መንገዲ ንዕብየት MSP

Açyk çeşme ynkylaby kabul etmek: MSP ösüşi üçin täze ýol

اوپن سورس انقلاب کو اپنانا: MSP گروتھ کے لیے ایک نیا راستہ

Case Study: Evaluating GNN Performance Using Shortest-Path Distance for Generalization and Fairness

Nobel Prize Winner Geoffrey Hinton Explores Two Paths to Intelligence in AI Lecture

How to Find the “Routes” of All-Pairs Shortest Paths With the Floyd-Warshall Algorithm in C#

Cómo encontrar las “rutas” de los caminos más cortos de todos los pares con el algoritmo Floyd-Warshall en C#

Nola aurkitu bikote guztietako bide laburrenen "ibilbideak" Floyd-Warshall algoritmoarekin C#-n

چگونه با الگوریتم فلوید-وارشال در سی شارپ «مسیرها» کوتاه‌ترین مسیرهای تمام جفت را پیدا کنیم

כיצד למצוא את ה"מסלולים" של כל הזוגות הקצרים ביותר עם אלגוריתם פלויד-ורשל ב-C#

C# でフロイド・ワーシャル アルゴリズムを使用して全ペア最短経路の「ルート」を見つける方法

C# тилинде Флойд-Уоршалл алгоритми менен бардык жуптардын эң кыска жолдорунун "маршруттарын" кантип тапса болот

ວິທີການຊອກຫາ "ເສັ້ນທາງ" ຂອງທຸກຄູ່ເສັ້ນທາງທີ່ສັ້ນທີ່ສຸດດ້ວຍ Floyd-Warshall Algorithm ໃນ C#

Jak znaleźć „trasy” najkrótszych ścieżek dla wszystkich par za pomocą algorytmu Floyda-Warshalla w języku C#

Mawaniro e "Nzira" dzeVese-Pairi Pfupi Nzira Neiyo Floyd-Warshall Algorithm muC#.

C#'da Floyd-Warshall Algoritması ile Tüm Çiftlerin En Kısa Yollarının "Rotaları" Nasıl Bulunur

Як знайти «маршрути» найкоротших шляхів усіх пар за допомогою алгоритму Флойда-Воршалла в C#

Cách tìm "Tuyến đường" của tất cả các đường dẫn ngắn nhất của cặp với thuật toán Floyd-Warshall trong C#

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