How a Biologist Became a Data Scientist

So before we begin, perhaps a little bit about myself. I have been working in data science since 2004 when I was in my second year of PhD studies. By the year 2006, I had completed my PhD research thesis entitled Computer-aided molecular design for biological and chemical applications: Quantum chemical and machine learning approach. In a nutshell, my research lies at the interface of biology, chemistry and computer science whereby the fusion of these fields make it possible for me to explore the underlying origins of protein function and its modulation (i.e. inhibition or activation). Such understanding holds great implication for drug discovery, particularly in the discovery of novel bioactive compounds with therapeutic activity.

Fast forward to 2020, I am still using data science to make sense of data from biology, chemistry and medicine. Much of my work revolves around the discovery of drugs that exert promising modulatory property against diseases by specifically targeting target proteins of interest. This is performed by using machine learning to understand and reveal what specific features of drug-like molecules give rise to promising modulation of the target protein.

My data science journey started back in 2004. It was a time when the field was not yet called data science and the more popular term was data mining. I can vividly recall that the first data science project that I was working on was to predict the DNA splice junction sites. The primary data mining tool that I first started out using was a program called WEKA, which was developed by the University of Waikato. This software is a GUI software where we could click various buttons in order to import data, perform feature selection, normalize the data, remove missing data and also to build machine learning models. Some of the machine learning algorithms that I used include decision trees, linear regression, artificial neural network and support vector machine.

I initially started out as a user of the GUI-based data mining software and over time I began to gain awareness on some of the limitations and hurdles. Particularly, I noticed that the time that it takes to run the data mining workflow took a lot of time, specifically when I want to optimize learning parameters I would have to manually modify the parameter values in the program (i.e. imagine doing this for 100–1000 different parameter settings). After a few years, I felt the urge to learn how to automate these manual and mundane tasks.

The natural next step would be to grow to a programming language such as Python or R.

Learning to code is indeed a challenging endeavor and quite difficult when coming from a non-technical background.