The amount of biological data is increasing rapidly. This opportunity is giving to analyze more relevant biological problems using data science. Experience in data mining and extracting essential features of the data to implement machine learning and deep learning models provide how to answer those biological problems.
English, Bengali, Hindi
Upon request
1. Shi W, Lemoine JM, Shawky MA, Singha M, Pu L, Yang S, Ramanujam J, Brylinski M. (2020) BionoiNet: Ligand-binding site classification with off-the-shelf deep neural network. Bioinformatics 36 (10): 3077-3083.
2. Govindaraj RG, Naderi M, Singha M, Lemoine J, Brylinski M. (2018) Large-scale computational drug repositioning to find treatments for rare diseases. NPJ Syst Biol Appl 4: 13.
3. Singha M, Pu L, Busch K, Wu HC, Ramanujam J, Brylinski M. GraphGR: A graph neural network to predict the effect of pharmacotherapy on the cancer cell growth. Submitted (Bioarchive)
Research Assistantship is funded by National Institute of General Medical Sciences for the R35 Maximizing Investigators Research Award (MIRA)