Prakash Chourasia

Compression and k-mer based Approach For Anticancer Peptide Analysis

The paper introduces a compression and k-mer based method for anti-cancer peptide classification, achieving better performance than deep learning models without requiring custom features or pre-trained models.

DeepPWM-BindingNet: Unleashing Binding Predictionwith Combined Sequence and PWM Features

This paper introduces DeepPWM-BindingNet, a deep-learning framework that improves DNA-protein binding prediction by integrating PWM features with CRNNs and attention mechanisms.

Preserving Hidden Hierarchical Structure: Poincare Distance for Enhanced Genomic Sequence Analysis

This paper introduces Poincare distance measures for genomic and proteomic sequence analysis, showing improved performance in classification tasks compared to existing methods.

Advancing Protein-DNA Binding Site Prediction: Integrating Sequence Models and Machine Learning Classifiers

This study develops a model combining protein language models and machine learning classifiers to predict protein-DNA binding sites, showing improved performance on benchmark datasets.

Enhancing t-SNE Performance for Biological Sequencing Data through Kernel Selection

This paper evaluates different kernels for t-SNE to improve its performance on biological sequencing data, finding that the cosine similarity kernel provides the best results for SARS-CoV-2 spike protein sequences.

Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification

Hist2Vec is a kernel-based embedding method for biological sequence classification that outperforms existing methods with >76% and >83% accuracy on DNA and protein datasets, respectively.