How Big Data Can Help Build Biotech Products

New methods and discoveries, such as next-generation genome sequencing, generate vast amounts of data and transform the scientific landscape. They lead to unimaginable breakthroughs that could completely change biotechnology. Today, Big data is already changing medicine, bioinformatics, agriculture, and the energy industry. Let's take a closer look at how this happens.

What is Big data

"Big data" refers to the processing and obtaining of previously inaccessible information from a massive array of heterogeneous data. The volume of such data can take thousands and hundreds of thousands of terabytes, while the data may be unstructured, incomplete, repetitive, and with errors. Such data must be stored somewhere and quickly and efficiently processed.

All this is the technical side of the issue.

Without its development, the very phenomenon of Big data would not have arisen. But, in addition to hardware, there must also be algorithms that can get new information from this ocean of numbers. Big data technologies are now playing the role of a lever and a fulcrum, with the help of which previously inaccessible things have become possible.

Big Data in Medicine

In healthcare, Big data has enormous potential and can help hospitals, clinics, and medicine in general. Big data has changed the way data is managed, analyzed, and used in any industry, including medical care. The digitalization of medicine is a promising direction that not only unifies the work of clinics or laboratories but can also save human lives. The processing and storage of Big data make it possible to make accurate diagnoses, check medical data without burdening doctors, and integrate the results of studies performed on different devices into a standard system.

Here are the opportunities already provided by the use of Big data in medicine:

1. Medical gadgets. There are many wearable medical devices, each of which allows you to collect information about the patient and transfer it to single data storage. After that, the data can be analyzed.
2. Complex diagnostics. There is a well-known problem in diagnosing rare diseases and preventing epidemics. In both cases, the technological solution can significantly speed up the process of determining the critical state.
3. Comprehensive prevention. Carrying out complex treatment against the background of the patient taking several drugs, possibly surgical intervention, requires processing a huge array of data on all their life processes. Of course, in this case, you can use specialized reports that aggregate its indicators.
4. Emergency medicine. Big data helps to diagnose critical conditions in time, notify the ambulance on time or warn against the deadly effects of the environment.
5. Telemedicine. A large area with the possibility of not only remote examination but a timely treatment appointment.

Big data in bioinformatics

Biotechnologies open the horizon to human genetic changes and the development of specialized tools for preventing congenital diseases. Bioinformatics is one of the world's most actively developing interdisciplinary scientific fields. It originated in the middle of the last century, but the real boom occurred at the beginning of this century.

Bioinformatics is based on the analysis of Big data, the solution of fundamental problems, and the development of computational methods for biology and medicine. The primary trend in developing bioinformatics is the work with large amounts of data. There is a search for tools that allow you to navigate faster and easier in a giant array of information. Deep learning and artificial intelligence methods are increasingly being introduced in developing various bioinformatics tools.

For example, at the end of 2020, Google DeepMind developed a program based on deep neural networks that predict a protein's three-dimensional structure. Scientists had been trying to solve this problem for half a century, and a significant breakthrough was entirely unexpected.

What benefits can bioinformatics bring to medical research? Diseases or predispositions to baldness, obesity or poor eyesight can be associated with specific genes. The task of computational genetics is to determine which part of the genome from many "suspects" is guilty of changes. To do this, researchers typically compare the genetic information of two groups: patients with a disease, such as schizophrenia or Crohn's disease, and healthy people. Then, according to the found differences in the activity and expression of genes, they are ranked according to the level of possible influence on the disease, and biologists confirm or refute these relationships.

Now laboratories and medical centers are accumulating the genetic data of individual patients, and there are international projects aimed at creating databases of the genomes of thousands and even millions of people. This amount of data can become a source of entirely new information about the functions and work of our genetic code.

Big Data in Agriculture

New technologies based on Big data can make the industry greener or more profitable. Such farming offers cost savings and new business opportunities:

• Increasing agricultural productivity. Big data analytics has shown excellent results in predicting crop production and rising yields.
• Improving the efficiency of operations. Along with the increase in productivity, the consumption of resources (water, electricity, materials) is reduced.
• Stopping labor migration. Technology can make the industry more attractive to professionals.
• Reducing the amount of food waste. Between 20% and 30% of food is lost at various supply chain stages today. Solving this problem will save the industry from $155 to $405 billion annually by 2030.
• Attracting investments. The use of Big data justifies investments in technology

After introducing new technologies, the agricultural sector collects an enormous amount of data available for farmers to use to improve the efficiency of crop production. Big data tools and methods help in extracting meaningful information from this data. The correct interpretation of this data should add value to agribusiness.