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Application of Data Science in Medical
Research & Drug Discovery


3 days ago by


Hi everyone, I am Rajkiran, General Manager, SeekACE Solutions, today we will be discussing about application of Data Science in Genomics and Drug Discovery.

Let me start by defining Genomics. It is the study of sequencing and analysis of genomes. In simple language, a genome consists of the DNA and all the genes of any organisms. After, the compilation of the Human Genome Project, the research has been advancing rapidly and it has inculcated itself in the realms of big data and data science. Fifteen twenty years back, when computers where not so powerful, the researching organizations spent a lot of time and money on analyzing the sequence of genes. This was an expensive and tedious process. However, with the advanced data science tools, it is now possible to analyze and derive insights from the human gene in a much shorter period of time and in a much lower cost.

The goal of the research is to analyze the genomic strands and search for irregularities and defects in it. Then find connections between genetics and health of the person. We at SeekACE are developing our own product to help these researchers using data science to analyze the genetic sequences and trying to find a correlation between the parameters contained within it and the disease. Furthermore, researches in genomics also involves finding the right drug which provide a deeper insight in the way a drug reacts to a particular genetic issue. Presently we are trying to work on a recent discipline that combines data science and genetics and it is known as Bioinformatics.

We are using several data science tools like MapReduce, SQL, Galaxy, Bioconductor etc. MapReduce processes the genetic data and reduces the time it takes to process genetic sequences. SQL is a relational database language that we use to perform querying and retrieve data from genomic databases. Galaxy is an open source, GUI based biomedical research application that allows researchers to perform various operations on genomes. And finally, we are in the process of developing a software for the analysis and comprehension of genomic data.

The researches that have been conducted in the field of computational biology and bioinformatics is a huge ocean that still remains uncharted. There are advanced fields that are still to be researched are genetic risk prediction, gene expression prediction etc. For our implementation projects we are actively looking to partner with some field experts. It will be of great help, if you can help us to tie up with any researcher.

The second topic which I wish to discuss today is about our developments wherein we are trying to build applications using Data Science in the field of Drug Discovery.

We all know Drug Discovery is a highly complicated discipline. Pharmaceutical industries have started heavily relying on data science to solve their problems and create better drugs for the people. Drug Discovery is a time-consuming process that also involves heavy financial expenditure and heavy testing. Even at times, when there is an outbreak of particular viral infection or diseases, to quickly address the problem prediction mechanisms with higher rate of success depends on Data Science and Machine Learning algorithms. The process is revolutionizing as it provides extensive insights into optimizing and increasing the success rate of predictions.

Pharmaceutical companies are using the insights from the patient information such as mutation profiles and patient metadata. This information helps the researchers to develop models and find statistical relationships between the attributes. In this way, companies can design drugs that address the key mutations in the genetic sequences. We are working with deep learning algorithms to find the probability of the development of disease in the human system.

Our data science algorithms will also help to simulate how the drugs will act in the human body and take away the long laboratory experimentations. With the advancements in the data-science facilitated drug discovery, it will be possible to improve the collection of historical data to assist in the drug development process. With a combination of genetics and drug-protein binding databases, we are trying to develop new innovations in this field. Furthermore, using data science, researchers will be able to analyze and test the chemical compounds against a combination of different cells, genetic mutations etc. With the usage of machine learning algorithms, researchers can develop models that will compute the prediction from the given variables and can also take care of a large number of variables at a time.

Thank you for reading this article and being with me. I will love to get a feedback. I personally thank all my social media friends who are taking time to read and reshare my post. You are my biggest motivation and driving force to write more & more.

Thanks to Everyone,


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