Mohammad Saifur Rahman

The paper develops a workflow combining XGBoost rank aggregation and SVM-RFECV to identify compact, interpretable biomarker panels from small-sample microarray datasets.

The paper uses sparse autoencoders to analyze BiRNA-BERT, revealing biologically meaningful features and improving RNA-type classification.

This paper introduces scPSS, a method to quantify pathological shifts in single-cell data by comparing diseased cells to healthy references, showing it can predict individual health conditions.

This paper introduces a supervised NMF framework to link spatial transcriptomics data with molecular events, identifying genes associated with disease progression in Alzheimer's and myocardial infarction.

StackGlyEmbed predicts N-linked glycosylation sites using a stacking ensemble model, achieving high performance metrics and outperforming existing methods.

GraFusionNet improves graph classification by integrating node, edge, and semantic features, achieving better performance on toxicity prediction tasks.

This paper introduces StackCBEmbed, a machine learning model that predicts protein-carbohydrate binding interactions from primary sequence, achieving better performance than existing methods.

SAINT-Angle improves protein backbone torsion angle prediction using a self-attention network and transfer learning, outperforming existing methods on benchmark datasets.

The paper proposes deep learning models for predicting succinylated lysine residues using biochemical and physicochemical properties, achieving improved sensitivity and specificity through threshold tuning.

This paper introduces NoVaTeST, a framework to detect genes with location-dependent noise variance in spatial transcriptomics data, showing it provides complementary insights to existing tools.

The paper introduces wQFM, a method for estimating species trees from weighted quartets, showing it matches or improves upon existing methods in accuracy.

The paper develops a pipeline to classify SARS-CoV-2 genome sequences for virulence and predict mutations using deep learning.

SAINT improves Q8 protein secondary structure prediction using self-attention and Deep3I network, outperforming existing methods on benchmark datasets.