Swakkhar Shatabda

Figbird: A probabilistic method for filling gaps in genome assemblies

Figbird fills gaps in draft genome assemblies using a probabilistic method with EM algorithm, outperforming existing tools in accuracy and error reduction.

PanClassif: Improving pan cancer classification of single cell RNA-seq using machine learning

PanClassif improves cancer classification from single-cell RNA-seq using machine learning by selecting effective genes and applying smoothing and oversampling techniques, achieving better performance than 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.

A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome

The paper develops a CNN-based tool to identify 6mA sites in rice genomes and shows it generalizes to other plant genomes.

SubFeat: Feature Subspacing Ensemble Classifier for Function Prediction of DNA, RNA and Protein Sequences

SubFeat predicts functionalities of DNA, RNA, and protein sequences using an ensemble classifier, outperforming existing methods on four datasets.

iPromoter-BnCNN: a novel branched CNN based predictor for identifying and classifying sigma promoters

The paper introduces iPromoter-BnCNN, a branched CNN model for identifying and classifying six types of sigma promoters, showing superior performance on benchmark and independent test datasets.

DeepDBP: Deep Neural Networks for Identification of DNA-binding Proteins

This paper introduces two deep learning methods, DeepDBP-ANN and DeepDBP-CNN, to identify DNA-binding proteins from protein sequences, achieving state-of-the-art accuracy on benchmark datasets.