Pranab Das | Bioinformatics in Pharmaceuticals | Best Scholar Award

Mr Pranab Das, NIT Nagaland, India

Pranab Das is an Assistant Professor in the Department of Computer Science at Kumar Bhaskar Varma Sanskrit and Ancient Studies University, Assam. He holds an M.Tech in Computer Science from NIT Meghalaya and has submitted his Ph.D. thesis at NIT Nagaland. His research focuses on computational biology, drug discovery, and machine learning 🧬🤖. With multiple SCIE and SCI-indexed publications in top journals, he has made significant contributions to predicting drug functions and adverse reactions. Additionally, he has presented at international conferences and authored book chapters 📚. He has qualified JRF, NET, and SLET, showcasing his academic excellence.

Publication Profile

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Education

Pranab Das is a dedicated researcher in Computer Science and Engineering (CSE), currently awaiting the completion of his Ph.D. (2021-2024) from the National Institute of Technology (NIT) Nagaland, where he achieved an impressive 9.23 CGPA 🎓. He holds an M.Tech in CSE (2019-2021) from NIT Meghalaya with a 9.27 CGPA 🏆 and a BE in CSE (2015-2018) from Jorhat Engineering College, securing 73.89% 📚. His academic journey began with a Diploma (70%) from Assam Engineering Institute and 10th grade (66.17%) from Barnibari Milan High School. Additionally, he has cleared JRF, NET, and SLET 🔬✅.

Project Work

Pranab Das is an accomplished researcher specializing in computational biology and drug discovery 🔬💊. His latest work, “MLCNNF: A Multi-Label Convolutional Neural Network Framework for Predicting Adverse COVID Drug Reactions”, is published in IEEE Transactions on Computational Biology and Bioinformatics (2025) 📄. He has also co-authored “BRMCF: Binary Relevance and MLSMOTE-Based Computational Framework” (2022) and “Advances in Predicting Drug Functions” (2023). His research extends to machine learning applications in pharmacology, including drug classification and side-effect prediction 🧠📊. With publications in high-impact journals (IF: 2.4-23.8) 📈, Pranab contributes significantly to AI-driven drug discovery 🚀.

Area of Intrest

Pranab Das is deeply passionate about cutting-edge technologies in Artificial Intelligence (AI) 🤖, Machine Learning (ML) 📊, and Deep Learning (DL) 🧠, with a strong focus on their applications in Bioinformatics 🔬. His research explores innovative AI-driven solutions for drug discovery, predictive modeling, and computational biology. By leveraging ML and DL techniques, he aims to enhance drug function prediction, side-effect analysis, and disease modeling. His interdisciplinary approach bridges technology and life sciences, driving impactful advancements in healthcare and pharmaceuticals 💊. Pranab continues to push the boundaries of AI in biomedical research, shaping the future of intelligent drug discovery 🚀.

Research Focus

Pranab Das is a researcher specializing in computational biology, bioinformatics, and artificial intelligence in drug discovery 🧬💻. His work primarily focuses on predicting drug functions, adverse drug reactions, and drug-disease associations using multi-label machine learning and deep neural networks 🤖🧪. He has contributed extensively to the field through innovative frameworks like MLCNNF, K1K2NN, and BRMCF, integrating chemical structures, gene ontology, and biological properties for enhanced drug prediction accuracy 🏥🔬. His research advances COVID-19 drug safety, pharmacology, and computational chemistry, making significant contributions to the intersection of AI and healthcare 🏥📊.

Publication Top Notes

MLCNNF: A Multi-Label Convolutional Neural Network Framework for Predicting Adverse COVID Drug Reactions From the Chemical Structure

A novel neural network-based nearest neighbor approach for drug function prediction from chemical structures

BRMCF : Binary Relevance and MLSMOTE based Computational Framework to Predict Drug Functions from Chemical and Biological Properties of Drugs.

A Comprehensive Survey of Studies on Predicting Anatomical Therapeutic Chemical Classes of Drugs

Inceptionv3‐LSTM‐COV: A multi‐label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short‐term memory

K1K2NN: A novel multi-label classification approach based on neighbors for predicting COVID-19 drug side effects

Advances in Predicting Drug Functions: A Decade-Long Survey in Drug Discovery Research

An Extensive Survey on the Use of Supervised Machine Learning Techniques in the Past Two Decades for Prediction of Drug Side Effects

MLCNN‐COV: A multilabel convolutional neural network‐based framework to identify negative COVID medicine responses from the chemical three‐dimensional conformer

Kanwaljit Rana | Bioinformatics in Pharmaceuticals | Best Researcher Award

Dr Kanwaljit Rana,Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab, India, India

Dr. Kanwaljit Rana 🧬📊 is a health researcher specializing in computational biology and big data analytics for disease diagnosis and health outcome prediction. Holding a Ph.D. in Biotechnology from GADVASU, India (2022), Dr. Rana integrates large-scale clinical, biological, and omics data using machine learning for precision medicine. With research experience at ICGEB and PGIMER, he has developed bioinformatics pipelines for genomics and predictive health analytics. His expertise spans AI-driven diagnostics, wearable health tech, and big data automation. A published scientist and GATE-qualified scholar, Dr. Rana continues to innovate in health data science and computational medicine. 🚀

Publication Profile

Google scholar

Education

Dr. Kanwaljit Rana 🎓 holds a Ph.D. in Biotechnology from Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), India (2022). With a strong academic foundation, he previously earned a Master’s degree in Biotechnology from HNB Garhwal University, Srinagar. His expertise lies in cutting-edge biotechnological research, contributing to advancements in veterinary and animal sciences 🧬🔬. Passionate about innovation and scientific discovery, Dr. Rana continues to explore new frontiers in biotechnology, striving to make a meaningful impact in the field. His dedication to research and development reflects his commitment to scientific excellence and progressive learning

Professional Experience

Dr. Kanwaljit Rana 🎓 is a dedicated Research Associate at the International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi (Feb 2024 – Aug 2024), where he conducts advanced bioinformatics analysis 🧬, integrating genomics and metagenomics data for disease characterization. Previously, at PGIMER, Chandigarh (Sep 2022 – Jan 2023), he developed automated workflows for tuberculosis meningitis diagnosis 🏥. His research journey includes roles at Punjab Agricultural University (PAU), Ludhiana, and Swami Vivekanand Faculty of Technology & Management, specializing in molecular breeding 🌱 and agricultural biotechnology. He is also a QC Freelancer at Cactus Communications since October 2024 ✍️.

Awards

Dr. Kanwaljit Rana 🎓 is a highly accomplished researcher, having qualified GATE Biotechnology in both 2020 and 2022 🏆, showcasing his expertise in the field. His dedication to innovative research earned him the Best Poster Award 🥇 for his work on “Boon of Cow Curd Consumption” at the National Webinar on Cowpathy and Animal Health (2021) 🐄🧪. His contributions to biotechnology and health sciences continue to make a significant impact, blending traditional knowledge with modern scientific advancements. Dr. Rana remains committed to groundbreaking research and advancements in biotechnology and health sciences 🔬📚

Research Focus

Dr. Kanwaljit Rana’s research primarily focuses on computational genomics 🧬, bioinformatics 🖥️, and veterinary genetics 🐶. His work involves biocomputational analysis of microRNAs, genome assembly, and ancestry studies, particularly in indigenous dog breeds like the Gaddi dog. His expertise extends to genome sequencing, phylogenetics, and the application of programming languages like Python, R, and MATLAB for data analysis. His contributions to veterinary science include genetic insights into animal breeding, evolution, and conservation. Through his interdisciplinary approach, Dr. Rana advances animal genomics, computational biology, and veterinary research, impacting both genetic conservation and bioinformatics applications in animal sciences.

Publication Top Notes

Differential Perspectives Between miRNA and lncRNA in Light of Biogenesis and Functions: A Review

Molecular characterization of indigenous dogs through whole genome analysis

Biocomputational identification of microRNAs from indigenous Gaddi dog genome

Introduction to Python, R, and MATLAB