Journal of Psychiatry and Psychological Sciences

Amaan Arif is a researcher in Computational Biology and Healthcare AI. His work focuses on applying Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI to bioinformatics, precision medicine, and healthcare data analysis.

His research primarily involves multi-omics data analysis, including transcriptomics, metagenomics, and genome mining, with a strong focus on understanding disease mechanisms through computational approaches. He has experience in developing and implementing bioinformatics workflows for RNA-Seq analysis, differential gene expression analysis, biomarker identification, functional annotation, and pathway enrichment studies using biological resources such as KEGG and Reactome.

Amaan has also worked on AI-enabled healthcare informatics projects involving predictive modeling and medical data analysis for disease screening and diagnosis. His projects include developing machine learning-based frameworks for predicting oral cancer and pulmonary disease using algorithms such as XGBoost, Random Forest, and LightGBM.

In addition to conventional AI approaches, his recent work explores the integration of Generative AI into biomedical and drug discovery workflows. He has contributed to the development of generative chemical language models, including the “Exo-Gen” framework for computational drug discovery and molecular design. He is proficient in Python and R and has experience in building scalable pipelines for biological data processing, automation, and computational analysis.

His current research interests include cancer bioinformatics, CRISPR-based computational studies, AI-assisted target discovery, and the integration of advanced computational methods into translational healthcare research. Through interdisciplinary research combining biology, data science, and artificial intelligence, Amaan aims to contribute toward the development of accessible and data-driven healthcare solutions.

• Computational Psychiatry
• Artificial Intelligence in Mental Health
• Neuroinformatics
• Machine Learning for Psychiatric Disorder Prediction
• Deep Learning in Mental Health Diagnostics
• AI-Assisted Neurodevelopmental Disorder Analysis
• Digital Psychiatry and Healthcare Analytics
• Cognitive Computing in Psychiatry

Reviewer, PLOS ONE

Review Committee Member, ICECCME 2026

Review Committee Member, ICECET 2026

Editor, Artificial Intelligence and Computational Biology in Healthcare Genomics, Public Health and Microbiome

Participant and presenter at multiple international conferences in AI, healthcare, genomics, and neuroinformatics

Member, American Society for Microbiology

Google Cloud Arcade Facilitator

Mentor, Reliance Foundation Postgraduate Scholars Mentorship Program

Workshop participant in AI, computational biology, drug discovery, and scientific writing programs

1. Challenges and Future Direction in Millets Research
2. Navigating the Landscape of Antibiotic Safety: Traditional Approaches and Emerging Strategies to Overcome Host RNA Off-Target Toxicity
3. Adversarial Deep Generative Techniques for Early Diagnosis of Neurological Conditions and Mental Health Practises
4. Current Applications of Artificial Intelligence and Machine Learning in Plant Functional Genomics
5. Artificial Intelligence Models for Analysing Plant Transcriptomics
6. Transcriptome Network Biology Reveals the Neuroprotective Potential of Rosmarinic Acid Against Frontotemporal Dementia
7. Computational Approaches to Fight Against Wheat Rust
8. Machine Learning-Driven Framework for Precision Crop and Fertilizer Recommendation Using Soil and Climatic Parameters
9. AI-Powered Toxicity Profiling of Veterinary Drugs and Feed Additives
10. AI-Driven Linguistic Modelling for Neurodevelopmental Disorders
11. Deep Learning in Neuroomics: Advances in Brain Mapping and Disease Prediction
12. Machine Learning-Based Dementia Prediction Using Clinical Data: Non-Invasive Approach for Early Detection and Management