MLBio+Laboratory Machine Learning in Biomedical Informatics

Software and Web Servers

My research has lead to the development of several software and web servers. These are made available to the academic research community.

svmPRAT: svm-Based Protein Residue Annotation Toolkit
svmPRAT is a general purpose protein residue annotation toolkit to allow biologists to formulate residue-wise prediction problems. svmPRAT formulates annotation problem as a classification or regression problem using support vector machines. The key features of svmPRAT are its ease of use to incorporate any user-provided information in the form of feature matrices. For every residue svmPRAT captures local information around the reside to create fixed length feature vectors. svmPRAT implements accurate and fast kernel functions, and also introduces a flexible window-based encoding scheme that allows better capture of signals for certain prediction problems.
MONSTER: Minnesota prOteiN Sequence annoTation servER
MONSTER is a server for predicting the local structure and function properties of protein residues. MONSTER provides residue-wise annotation services, that include secondary structure, transmembrane-helix region, disorder region, protein-DNA binding site, \red {ligand-binding site}, local structure alphabet, solvent accessibility surface area, and residue-wise contact order prediction. MONSTER uses sequence-derived information (in the form of PSI-BLAST profiles), a window-based encoding scheme with an accurate kernel function to perform the classification or estimation. The user provides an amino acid sequence and selects the desired predictions, and submits a job to the MONSTER server. The results are emailed to the user as a link directing the user to a well formatted HTML output page.
MARINER: MinnesotA pRotein modelINg servER
MARINER is a server for predicting the three-dimensional structure of proteins using homology modeling based techniques. This server is always under development, and was used for participation in the CASP 8 protein structure prediction competition. Watch this space for a future version of this server. Also students at George Mason interested in the competition, please get in touch with me.
Profile-based Kernel Compute Package
kernel-compute is a package that computes pairwise profile-based similarity matrix. This matrix can
then be converted into a valid kernel matrix with an eigen value transformation. This scoring matrix has shown to be the best performing method for developing remote homology detection and fold recognition models.

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