Rajiv Narayan

Improving Deconvolution Methods in Biology through Open Innovation Competitions: An Application to the Connectivity Map

This study evaluates deconvolution methods for gene expression data using an open innovation competition and finds that random forest regression outperforms other approaches in accuracy and reproducibility.

Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions

The paper demonstrates how open innovation competitions improve computational biology methods for antibody clustering, gene expression imputation, and CMap querying.

Assessment of network module identification across complex diseases

The paper evaluates module identification methods across disease-relevant networks using GWAS data to identify top-performing algorithms and disease-associated modules.

GeNets: A unified web platform for network-based analyses of genomic data

GeNets provides a platform to compare genomic networks using machine learning and allows analysis of genetic and RNA sequencing data.

The GCTx format and cmap{Py, R, M} packages: resources for the optimized storage and integrated traversal of dense matrices of data and annotations

The paper introduces the GCTx format and associated software packages for efficiently storing and analyzing large dense matrices of perturbation data and annotations.

Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map

The paper evaluates RNAi and CRISPR technologies by analyzing gene expression profiles in the Connectivity Map, finding that RNAi has strong off-target effects while CRISPR is more accurate.

A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles

The paper introduces the L1000 platform, a high-throughput gene expression profiling method, and reports 1.3 million perturbational profiles expanding the Connectivity Map for drug mechanism discovery and functional annotation.

Gene expression inference with deep learning

This paper introduces a deep learning method to infer gene expression from landmark genes, showing significant improvements over linear regression in multiple datasets.