Aanchal Mongia

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics data, showing improved performance over existing methods in diabetic tissue analysis.

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

The paper uses single-cell RNA-seq data to predict drug responses and combinations by analyzing intra-tumor transcriptomic heterogeneity in cancer cell lines.

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

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

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

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