Research

Unpublished Work

Protein Solubility Prediction

Supervisor: Dr. Yang Zhang

Developed a deep learning-based pipeline to predict the solubility of protein sequences, utilizing a range of protein-related features.
Incorporated advanced protein language models (LLMs) for feature extraction and sequence processing.

Comprehensive Feature Selection and Biological Relevance Analysis in Multi-Disease Gene Expression Data

Supervisor: Dr. Mohammad Saifur Rahman

In this work, I have undertaken a comprehensive gene expression analysis of microarray cancer datasets, employing advanced techniques such as feature engineering and machine learning ensembles.
By integrating SHAP (SHapley Additive exPlanations) and conducting enrichment analysis, I have provided a robust framework for interpreting the results, contributing valuable insights into the complex landscape of cancer genetics

Github repository

Conference Papers

Is It Really Dead? Digging into Dead Brains through Analyzing Its Behavior in Response to Inducing External Impulses

Published in 8th NSysS, 2021

Supervisor: A. B. M. Alim Al Islam

Even though live brains are widely studied in the literature, dead brains remain little explored as perhaps it is generally believed to have a dead brain not workable anymore.
In contrary to such general perception, in this work, we explore the possibility of making a dead brain work again.
Explored outputs from a dead brain in response to applying external stimuli (in the form of electrical signals) to it.