Satish Gupta currently works as Director of AI and Analytics at Cognizant. He provides global support for all R&D, discovery and analysis for the company’s pharmaceutical, life sciences and healthcare customers.
He supported the clinical and agronomic applications of the Bayer Crop Sciences account as a consultant in the field of life sciences at TCS, Delhi. Additionally, he was a member of the team that validated the NGS panels used in oncology to meet the compliance requirements of CAP/CLIA/NABL auditing bodies.
INDIAai interviewed Satish Gupta to get his perspective on AI.
It’s great to see someone with a bioscience degree employed in data science. How it all began ?
Science is an evolving subject that is constantly improving thanks to the implementation of new methods and technologies resulting from research. Bioinformatics is a subject that gives life science students exposure to algorithms, databases, statistics, and programming. All the aspiration to learn new topics and the demand for applying bioinformatics in current scientific research has gradually pushed many of us towards data science. There are many quality universities and institutes offering bioinformatics courses and meeting the demand of the scientific and pharmaceutical sector. The application of third/fourth generation technologies to scientific research has dumped vast amounts of data into our bucket to inspire us to know more and make meaningful interpretation of it. This is called the age of data and life sciences, the healthcare and pharmaceutical industry has exploited it very well.
Who motivated you to pursue a career in AI? What was the driving force?
I would say it was a progressive movement, and “Bioinformatics” was a buzzword during our master’s, and it affected us. I was interested in starting my career in the industry after my master’s degree in biotechnology, but I was not satisfied for several reasons. The hunt to join the industry has made us aware of the upcoming demand for bioinformatics. Bioinformatics course at JNU, New Delhi gave me good exposure to databases, statistics and programming which motivated me to pursue my work later in research institutes and further my career in the industry in different roles. There is a massive demand for resources in the modern way of looking at data. This is called “Explainable AI”, where these mixtures of expertise are well adjusted. As soon as big data is part of its journey, AI must accompany it.
What were the first obstacles you encountered? How did you conquer them?
As mentioned, my current goal was to pursue a career in industry, but I needed help taking a break even after graduation in bioinformatics. So I started working in major research institutes in India to gain experience and break into the industry as they always prefer an experienced candidate over a fresher one. I have also connected with people working in academia and industry through various conferences, workshops and meetings. Proactive networking always works best for me. It also allows you to learn and become aware of new aspects in the scientific field. After a few years of working in a research institute, I broke into the industry, but soon realized the need for higher education for personal growth.
What are your responsibilities as Director of AI and Analytics for Bioinformatics and Life Sciences at Cognizant?
It is quite a challenging role where I have to keep abreast of recent trends in the life sciences, healthcare and pharmaceutical industries. Cognizant is a service provider and as a business unit we are focused on engaging AI and analytics for our business partners based on the required objectives. Therefore, I need to understand the exact requirements from an R&D, discovery and analysis perspective and provide a solution strategy. At the same time, I’m also trying to understand their broader theme of work and collaborations to bring together pain points where we can support, provide a solution, and have a lasting business relationship.
Tell me about your doctoral research. What have been your research contributions?
Research has focused on studying genetic and environmental modifiers of cancer risk. I have been mainly involved in the analysis of the modifying effects of selenium in blood plasma/serum and polymorphism of selenium (Se) metabolizing genes on cancer risk in CHEK2 and patients with lung, laryngeal cancer and colorectal not selected. I also explored the role of methylation in cancer-related genes and of selenoprotein in breast carcinoma. Some of the findings were:
- A higher Se concentration is significantly associated with a lower probability of cancer incidence.
- The Se concentration can be a valuable marker for the early detection of cancers in the group studied.
- The effect of blood serum selenium level on cancer incidence may depend on genotypes in selenoprotein genes.
- BRCA1 promoter methylation in peripheral blood is associated with breast cancer risk in patients with negative germline BRCA1 mutations.
I also collaborated with several research groups and published >10 publications during my PhD.
Is programming expertise essential for bioscience graduates who want to work in artificial intelligence?
I highly recommend exposure to the language of the program if opting for a career in data science. This again depends on the demand for the role and responsibilities. For example, data scientist would need more statistical knowledge with good understanding and experience in programming, and data engineer, additionally, would also need an advanced level of experience in algorithm development, in experimental design and programming. Understanding cloud technologies is essential because everything is deployed in the cloud. One can learn and hone their skills through many online learning platforms.
What advice would you give to someone who wants to work in artificial intelligence research? What do they need to focus on to move forward?
AI is an application that we can implement in different fields, from health, banking, finance, market research, agriculture, climatology, etc. Understanding any area of interest and determining the challenges in that particular area can be tapped using AI. The next approach would be to research the available data and define a problem statement to be solved using data science methods. Here I assume prior experience with programming. Beginners can start by learning the basics of Python or R and data science modules. The flow that I consider appropriate is a good understanding of the area of interest, knowledge of at least one programming language, knowledge of statistics and cloud-based approaches, a good grasp of data and the implementation of data science on problem statement. There are many materials and courses on the web to get you certified.
What scientific articles and publications have had the most impact on your life?
I have always worked on genetics, genomics and bioinformatics throughout my career. I admire articles, blogs, and research papers on implementing AI/ML-based approaches to problem solving in drug discovery and precision medicine. It is interesting to read about the multi-omics process for analyzing and interpreting OMICS data, the integration of data from disparate sources and how we can implement the FAIR guidelines. The post-COVID era has increased the application of AI/ML approaches in clinical sciences. It is interesting to learn more about decentralized trials and the extensive efforts to use real-world data (RWD) for decision-making in patient recruitment, patient stratification, and adverse drug reactions. AI plays an important role in the pharmaceutical industry, and FDA and EMEA regulations on AI would be interesting to watch in the development of medical devices, thus shortening the duration of drug development.
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