Madhavi Ganapathiraju's Lab
Application of Signal Processing and Machine Learning to study Problems in Computational Biomedicine. Focus areas of application: Membrane Protien Structure and Genome Wide Analysis for Genotype-Phenotype Relations
Madhavi Ganapathiraju
Assistant Professor
Computational Biomedicine
Open Positions for Graduate Students
1. Genome Wide Association Studies
2. Machine Learning
3. Protein Sequence Analysis
4. Genome Sequence Analysis
5. Translational Bioinformatics
Current NLM scholars, as well as future students may apply
TMpro
Transmembrane Helix Prediction using Latent Semantic Analysis and Amino Acid Properties
BLMT
Statistical N-gram Analysis to Discover Genome Signatures: Biological Language Modeling
Knowledge Discovery for Computational Biomedicine
Department of Biomedical Informatics, University of Pittsburgh School of Medicine
DNA Methylation Prediction
Sequence based differentiation of genes that escape activation in autosomal chromosome
Machine Learning
Active learning for membrane protein structure prediction from multiple, possibly incomplete, sources of information.
Genome wide association studies
Association studies of genome wide SNPs and phenomic information pertaining to disease
