Department Details

Bioinformatics Centre

1)    Joshi, M., Nikte, S.V., Sengupta, D. (2021). Molecular determinants of GPCR pharmacogenetics: Deconstructing the population variants in β2-adrenergic receptor. In R. Donev(Eds.), Advances in Protein Chemistry and Structural Biology (pp.361-396). Netherland: Elsevier. ISBN: 9780323988957.
2)    Sengupta, D., Sonar, K., Joshi, M.S. (2017). Characterizing clinically relevant natural variants of GPCRs using computational approaches. In A. K. Shukla(Eds.), Methods in Cell Biology (pp.187-204). Netherland: Elsevier. ISBN: 9780128133194.
3)    Bagchi, M.C., Ghosh, P.D. (2016). Anti-Tubercular Drug Designing Using Structural Descriptors. In S.C. Basak(Ed.), Advances in Mathematical Chemistry and Applications (pp.179-190). Amsterdam, Netherlands: Elsevier. ISBN: 9781681080529.
4)    Kadam, K., Sawant, S., Jayaraman, V., Kulkarni-Kale, U. (2016). Databases and Algorithms in Allergen Informatics. In I. Y. Abdurakhmonov(Ed.), Bioinformatics—Updated Features and Applications (pp.53-73). United Kingdom: IntechOpen. ISBN: 9789535125464.
5)    Sengupta, D., Joshi, M., Athale, C.A., Chattopadhyay, A. (2016). What can simulations tell us about GPCRs: Integrating the scales. In A.K. Shukla(Ed.), G Protein-Coupled Receptors-Signaling,Trafficking and Regulation(Series in Methods in Cell Biology: Volume 132) (pp.429-452). Netherlands: Academic Press. ISBN: 9780128035955.
6)    Kasibhatla, S.M., Waman, V.P., Kale, M.M., Kulkarni-Kale, U. (2015). Analysis of Next Generation Sequencing Data in Virology: Opportunities and Challenges. In J.K. Kulski(Ed.), Next Generation Sequencing - Advances, Applications and Challenges. Australia: InTech publishers. ISBN: 9789535122401.
7)    Kulkarni-Kale, U., Raskar-Renuse, S., Natekar-Kalantre, G., Saxena, S.A. (2014). Antigen-Antibody Interaction Database (AgAbDb): A compendium of antigen-antibody interactions. In R.K. De, N. Tomar(Eds.), Immunoinformatics (pp.149-164). New York, United States: Springer. ISBN: 9781493911141.
8)    Kulkarni-Kale, U., Raskar-Renuse, S., Natekar-Kalantre, G., Saxena, S.A. (2014). Antigen–Antibody Interaction Database (AgAbDb): A Compendium of Antigen–Antibody Interactions. In R.K. De, N. Tomar(Eds.), Immunoinformatics (pp.149-164). New York, United States: Springer. ISBN: 9781493911141.
9)    Kadam, K., Sawant, S., Kulkarni-Kale, U., Jayaraman, V.K. (2014). Prediction of Protein Function Based On Machine Learning Methods: An Overview. In iConcept Press Ltd, Introduction to Sequence and Genome Analysis (pp.1-38). New Delhi, India: iConcept Press Ltd. ISBN: 9781922227096 .
10)    Desai, D.V., Kulkarni-Kale, U. (2014). T-cell epitope prediction methods: an overview. In N. Tomar(Eds.), Immunoinformatics (pp.333-364). New York, United States: Springer. ISBN: 9781493911158.
11)    Mande, S.C., Kumar, A., Ghosh, P. (2013). Analysis of Dihedral Angle Variability in Related Protein Structures. In M.Bansal(Eds.), Biomolecular Forms and Functions (pp.107-115). USA: World Scientific Publishing . ISBN: 9789814449137.
12)    Kolekar, P.S., Kale, M.M., Kulkarni-Kale, U. (2011). Molecular Evolution & Phylogeny: What, When, Why & How?. In L.M. Cruz(Eds.), Computational Biology and Applied Bioinformatics (pp.3-27). Croatia - EUROPEAN UNION: InTech. ISBN: 9789533076294.