A comprehensive collection of my research projects, statistical tools, and computational applications in bioinformatics, genomics, and machine learning.
In silico platform for predicting intramolecular RNA triple helix structures with applications in understanding X chromosome inactivation and gene regulation mechanisms.
Developed an innovative three-layer computational pipeline to decipher repair factor requirements in different DNA break scenarios using single-cell RNA sequencing data.
Computational analysis of RNA-seq data from human forebrain organoids to understand interneuron migration disorders and neurodevelopmental conditions.
Investigated how G-quadruplex structures regulate CCN1 gene expression through computational analyses and proteomics data analysis.
AI-powered RAG assistant transforming laboratory knowledge management with intelligent question-answering, enterprise security, and sub-5-second response times.
Interactive educational application for understanding confidence intervals through visualizations, simulations, and practical applications.
Advanced statistical analysis platform with user-friendly interface designed for researchers without extensive statistical knowledge.
Interactive web application for principal component analysis with customizable visualization options and detailed explanations.
Educational app for exploring statistical distributions through interactive visualizations and real-world examples.
Analyzed the efficacy of Ashwagandha and Yogaraj Guggulu for osteoarthritis treatment using a hybrid Proteomics-Cheminformatics-Network medicine approach.
Master's thesis project analyzing transcripts susceptible to Nonsense Mediated mRNA Decay (NMD) in UPF3b KO HEC293 Cells.
Bachelor's thesis project creating a platform that integrates NCBI BLAST and PDB databases for protein image retrieval and detailed analysis.
Evaluated various optimization algorithms (SGD, Momentum, RMSProp, Adam) on a Saddle Point and MNIST dataset, comparing their efficacy and convergence properties.
Implemented recurrent neural networks to classify DNA sequences into categories based on intronic, exonic regions, and intron-exon boundaries.
Developed models using CNN and LSTM for predicting N6-Methyladenine sites in the Rice Genome, comparing algorithmic performances and accuracies.
Currently leading multiple research initiatives
Peer-reviewed papers and preprints
Active open-source contributor
Statistical and educational applications
I'm currently working on several exciting projects:
Developing specialized modules for experimental design, power analysis, and biostatistics applications.
Enhancing the functionality of our RNA triple helix prediction tool with improved algorithms and expanded database support.
Creating a tool that can predict the repair pathway choice based on DNA break characteristics and cellular context.
I'm always open to discussing new projects, research collaborations, and opportunities in bioinformatics and computational biology.