Angshul Majumdar

A knowledge graph-guided framework for multitask single-cell analysis

scKNIFE integrates gene expression with functional annotations to identify tumor programs and treatment-related functional variation in breast cancer.

Graph Regularized Probabilistic MatrixFactorization for Drug-Drug Interactions Prediction

This paper introduces a graph-regularized probabilistic matrix factorization method for predicting drug-drug interactions and shows it outperforms existing techniques on the DrugBank dataset.

Marker-free characterization of single live circulating tumor cell full-length transcriptomes

The paper introduces unCTC, an R package for unbiased identification and characterization of CTCs from scRNA-Seq data, validated on breast CTCs.

Computational prediction of Drug-Disease association based on Graph-regularized one bit Matrix completion

This paper predicts drug-disease associations using a graph-regularized one-bit matrix completion framework, showing superior performance over existing methods.

A visual atlas of genes tissue-specific pathological roles

The paper constructs a visual atlas of gene-pathology relationships by mining PubMed abstracts and using NLP to identify tissue-specific gene roles in disease.

FITs: Forest of imputation trees for recovering true signals in single-cell open chromatin profiles

FITs recovers true signals from sparse and noisy single-cell open-chromatin profiles by using a forest of imputation trees to avoid bias and preserve cell-type-specific activity.

deepMc: deep Matrix Completion for imputation of single cell RNA-seq data

deepMc uses deep matrix factorization to impute missing values in single cell RNA-seq data, improving clustering, differential expression analysis, and cell type separability.

Drug-Target Interaction prediction using Multi-Graph Regularized Deep Matrix Factorization

This paper introduces a deep learning framework for drug-target interaction prediction that incorporates multiple similarity matrices to improve performance on benchmark datasets.

McImpute: Matrix completion based imputation for single cell RNA-seq data

McImpute improves single cell RNA-seq data by imputing dropouts, enhancing clustering, differential expression, and visualization.

Drug-Target Interaction prediction using Multi Graph Regularized Nuclear Norm Minimization

This paper introduces a method to predict drug-target interactions using multi graph regularized nuclear norm minimization, showing improved performance over existing methods.