Bioinformatics - To Be Added

Machine Learning for Bioinformatics/Computational Biology - To Be Added

Application of machine/deep learning to single cell RNA sequence (scRNAseq), Assay for Transposase-Accessible Chromatin using sequence (ATAC-seq), etc. Analysis of high throughput sequencing genomics data (ChIP-Seq, DNase-Seq, and/or ATAC-Seq). Deep learning, explainable AI for computational biology problems - quality control (QC) clustering, denoising, imputation, trajectory inference, immunogenicity prediction, RNA binding protein prediction, transcription factor binding prediction. Analysis of multiplexed imaging, mass spectrometry imaging (MSI), CODEX imaging, spatial transcriptomics in tandem with single-cell genomics data and in general multi-omics analysis. Exploration of latent structures in single cell data, finding common embeddings/integration for mutimodal biological data.

Project pages:

  1. Imaging-based: Clustering

  2. Single-cell: DeepImmuno, scTriangulate, MaxATAC (coming soon)

  3. Metagenomics


Selected publications:


  • J. Zhang, Q. Wu, C. B. Johnson, G. Pham, J. M. Kinder, A. Olsson, A. Slaughter, M. May, B. Weinhaus, A. D'Alessandro, J. D. Engel, J. X. Jiang, J. M. Kofron, L. F. Huang, V. B. S. Prasath, S. S. Way, N. Salomonis, H. L. Grimes, D. Lucas. In situ mapping identifies distinct vascular niches for myelopoiesis. Nature, 590, 457-462, February 2021. [Clustering]

  • G. Li, B. Iyer, V. B. S. Prasath, Y. Ni, N. Salomonis. "DeepImmuno: Deep learning-empowered prediction and generation of immunogenic peptides for T cell immunity". Briefings in Bioinformatics, 2021. Code, online app. [DeepImmuno]

  • T. Cazares, F. Rizvi, B. Iyer, X. Chen, M. Kotliar, L. C. Kottyan, A. Barski, V. B. S. Prasath, M. T. Weirauch, E. R. Miraldi. MaxATAC: A suite of user-friendly, deep neural network models for transcription factor binding prediction from ATAC-seq. GLBIO, 2021. [MaxATAC]