The Translational Bioinformatics group focuses on the development and application of computational biology tools to address research problems in the post-genomic era.
The Group extensively exploited artificial intelligence based techniques for solution of various bioinformatics and cheminformatics research problems.
The group has recently developed a Deep Learning based tool for the prediction of basmati rice seed variety on the basis of images (iRSVPred). A smartphone version of the tools is available too.
The group has also developed a Deep Learning based tool for the prediction of COVID-19, TB, pneumonia, and other diseases, on the basis of Chest X-ray images.
It has developed several web servers for sequence based prediction for important protein families, members of which do not have obvious sequence similarities, conserved motifs and domains- this includes Cyclins (server: CyclinPred), Lipocalins (LipocalinPred), CDK inhibitor proteins (CDKIPred), virulent proteins (VirulentPred) and Fungal Adhesions (FaaPred).
The group has extended the use of SVMs (Support Vector Machine, an Artificial Intelligence-based method), and molecular modeling methods to develop a target-oriented focused library of compounds active against novel P. falciparum PfHslV and 20S proteasome, the newly identified drug targets against the parasite. The laboratory developed SVM based cheminformatics method to predict proliferation inhibitors of P. falciparum.
Bioinformatics Web servers and databases (https://bioinfo.icgeb.res.in)
Prediction method for fungal adhesins
A database of protozoan virulent proteins
Prediction method for Lipocalins
Homology modelling of P. falciparum proteins.
For further information
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