Alok K. Sharma

The human RNA-DNA interactome is cell type-specific and dynamic

The paper maps RNA-DNA interactions in 16 human cell types, showing cell-type-specific and dynamic networks that correlate with gene regulation and disease traits.

vDeepInsight: an injective three-dimensional voxel carrier for tabular-feature neighborhood learning

vDeepInsight introduces a 3D voxel carrier for tabular data that improves feature neighborhood representation, leading to better performance in gene expression tasks.

Dynamic multimodal survival prediction in multiple myeloma integrating gene expression, longitudinal laboratories, and treatment history

This paper develops a dynamic multimodal model for survival prediction in multiple myeloma using gene expression, lab data, and treatment history, achieving higher accuracy than existing methods.

scHDeepInsight: A Hierarchical Deep Learning Framework for Precise Immune Cell Annotation in Single-Cell RNA-seq Data

scHDeepInsight improves immune cell classification in scRNA-seq by using a hierarchical deep learning framework with adaptive loss and image-based features, achieving 93.2% accuracy across seven datasets.

Deep Learning for Protein Peptide bindingPrediction: Incorporating Sequence, Structural andLanguage Model Features

This paper introduces PepCNN, a deep learning model for predicting protein-peptide binding that improves specificity, precision, and AUC over existing methods.

Multi-representation DeepInsight: an improvement on tabular data analysis

The paper introduces MRep-DeepInsight, a method that improves tabular data analysis by using multiple representations, showing better accuracy than traditional methods on various datasets.

scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning

scDeepInsight improves cell-type annotation in scRNA-seq by using supervised deep learning, achieving higher accuracy than existing methods.

DRPBind: prediction of DNA, RNA and protein binding residues in intrinsically disordered protein sequences

DRPbind predicts DNA, RNA, and protein-binding residues in disordered protein sequences using SVM and features from evolutionary, physicochemical, and structural data.

DeepInsight-3D for precision oncology: an improved anti-cancer drug response prediction from high-dimensional multi-omics data with convolutional neural networks

DeepInsight-3D predicts patient-specific anticancer drug response using multi-omics data and convolutional neural networks, outperforming existing methods.

ACP-MHCNN: An Accurate Multi-Headed Deep-Convolutional Neural Network to Predict Anticancer peptides

ACP-MHCNN is a deep convolutional neural network for anticancer peptide identification that outperforms existing methods in multiple metrics.

CluSem: Accurate Clustering-based Ensemble Methodto Predict Motor Imagery Tasks from Multi-channelEEG Data

CluSem improves MI-EEG classification accuracy by 5-15% using a clustering-based ensemble method and is evaluated in a brain game called CluGame.

Critical Assessment of Protein Intrinsic Disorder Prediction

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

FEATS: Feature selection based clustering of single-cell RNA-seq data

FEATS improves single-cell RNA-seq clustering by selecting informative features, outperforming existing tools in accuracy and efficiency.

DeepInsight-FS: Selecting features for non-image data using convolutional neural network

DeepInsight-FS uses a convolutional neural network to select clinically relevant gene subsets from non-image data by converting samples into image-form.

An integrative machine learning approach for prediction of toxicity-related drug safety

This paper develops a machine learning method to predict drug toxicity by integrating drug targets, functional impact scores, and biological networks, showing it can identify toxic drugs and support safe combination design.