Biotech is a field that thrives on data. In fact, pharma and biotech companies collect about 10x more data than other industries, creating the ideal conditions for AI and machine learning adoption. But with so much data available, how do biotech companies know where to begin when it comes to implementing AI and ML? These are some of the most common use cases for machine learning in biotechnology. Newer firms and fields like AI or ML may not be as applicable to your business right now, but keep these in mind if you have plans to grow or pivot in the future.
Before you begin any machine learning project, it’s important to have a clear understanding of the business problem you’re trying to solve. This will help you determine a few key factors when selecting the right algorithms. You’ll want to know: - What data is available? How fresh is it? - What are your goals? What is the desired outcome? - What are your current processes? What can you automate? These questions will help determine what type of algorithm will work best for your use case and how to begin the process.
One way to future-proof your company for AI and ML is to build a data platform that can scale with your company. This should include data storage, data ingestion, and data discovery and visualization layer. Data storage - Depending on the size and sensitivity of your data, you may want to consider a hybrid or public cloud option. This will allow you to expand your capacity as you scale. Alternatively, on-premise storage may be more secure depending on your data. - Data ingestion - Data ingestion is the process of preparing data for analysis. If your data is in silos across multiple databases, files, or applications, it will be nearly impossible to combine or analyze. Data ingestion is the process of collecting, normalizing, and storing your data in a centralized location. - Data discovery and visualization - It’s important to have a centralized location for data discovery and visualization. This will allow you to search your data by keywords or categories, then visualize the findings in a variety of ways. You’ll be able to use this data to make informed business decisions.
For the healthcare industry, detecting and preventing fraud is a top priority. AI can help improve fraud detection by searching for keywords and data points that indicate fraudulent behavior. - For example, if a doctor submits a claim for a patient that’s been deceased for 10 years, that should raise a red flag. AI can be programmed to look for anomalies like these and flag them for further review. - AI can also be programmed to look for patterns in billing data. If a doctor regularly submits unusually high claims for a certain procedure, this could indicate fraud. - With access to the right data points, AI has the ability to spot trends and patterns that humans would miss. This can help improve efficiency and effectiveness while reducing false positives.
Machine learning can help scientists find data points in their research that would take humans a lot of time to find. One recent example of this concept in action was reported by the University of California where new research sheds new light on how benzoyl peroxide treats acne.
By training an algorithm to recognize key data points, a computer can comb through large amounts of data in a matter of seconds. - For example, a pharmaceutical company may have a large library of chemical compounds. Trained AI can search through this library to quickly find compounds with similar chemical structures. This can help scientists identify new drug candidates. - Advanced algorithms can also be programmed to look for specific data points in large volumes of data. For example, an AI can be programmed to find patterns in gene sequencing data to identify new therapeutic targets for cancer research.
Drug discovery can be a lengthy, expensive process. AI can help speed things up by identifying key data points to help scientists predict which compounds are most likely to succeed in human trials. This will also help reduce false positives, which are common in drug discovery. - AI can be trained to identify which compounds are most likely to lead to successful human trials. Scientists can feed this information into the algorithm to help speed up the process. - This is especially helpful when scientists are searching through millions of compounds. AI can be programmed to recognize patterns in the compounds and decide which are worth further investigation. - The algorithm can also be programmed to identify patterns in failed experiments. This can help scientists avoid repeating mistakes and find their next lead.
AI can also be used to look for patterns in gene sequencing data. This can help uncover new information about disease genes, drug target genes, and other genetic markers. - By training an AI to look for certain patterns in gene sequencing data, scientists can uncover new information about genetic diseases and pathways. This will help them develop new drugs and treatment plans. - AI can also be programmed to predict the function of genes. This will help scientists narrow down their search for new drug targets or treatment plans based on their gene sequence.
Machine learning and AI are already transforming the biotech industry. By implementing AI at the earliest stages of your business, you can reduce costs and increase productivity. You can also use AI to solve complex problems that scientists and researchers have been struggling with for years. One of the biggest challenges expected ahead remains the lack of machine learning talent. Tech recruiting firm located in Austin, Texas, Razoroo reports that nearly four machine learning openings currently exist for every machine learning professional. As more and more companies adopt AI, the latest and greatest algorithms are not cheap. Your business model and budget will determine how far you can go with AI adoption.