Dr. Shandar Ahmad is currently working as a Professor and scientist at the School of Computational and Integrative Sciences, Jawaharlal Nehru University (JNU), New Delhi. He belongs to Old Delhi and is a passionate and enthusiast scientist who also loves poetry (Urdu and English). He was ranked 4 on list of best Bioinformaticians in India
He has also been appointed as a research scientist at National Institute of Biomedical Innovation and has won many awards. He has published more than 100 research articles in high impact reputed international journals. His thrust areas of research are bioinformatics, Big data analytics, and complex systems.
His research group has developed many bioinformatics software for various purposes such as binding site prediction, DNA shape or indirect readout prediction tools, solvent accessibility and general structure structural analysis tools. Some of his developed tools are (i) GIGEASA (ii) DynaSeq (iii) DBS-Pred (iv) DBS-PSSM (v) SDCPred (vi) SRCPred (vii) PPiPP (viii) RVP-Net (ix) HTMOne (x) ReadOut . Currently, his research group is engaged in developing data-driven algorithms and applications of biological data. Their basic research interests are machine learning, deep learning through neural networks, Big data analytics, and Novel architectures.
- BETSY: A new backward-chaining expert system for automated development of pipelines in Bioinformatics
- Homology Modeling of α-Glucosidase Enzyme: 3D Structure Prediction
- Bioinformatics is prediction- and simulation-based: Let’s rephrase the conversation!
- Raccoon2: A GUI facilitating virtual screenings with Autodock and Autodock Vina
- How to install Raccoon plugin on Ubuntu for virtual screening using Autodock?
- McPAS-TCR: A database of TCR sequences associated with pathology and antigens
- Biotite: A bioinformatics framework for sequence and structure data analysis
- How to get super-computing facilities for Bioinformatics analyses?
- Editorial: Need to re-formulate the bioinformatics curricula at undergraduate and postgraduate level
- Algorithm and workflow of miRDB