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mtariqi/README.md
exec-78cb113d-b5dd-4f80-8d23-fe1dce2d1236

🧬 Building AI-driven Computational Methods for Precision Medicine


πŸ‘¨β€πŸ”¬ About Me

I am a Senior Research Scientist specializing in Computational Biology, Bioinformatics, Artificial Intelligence, and Precision Medicine.

My research combines genomics, transcriptomics, multi-omics integration, machine learning, and large language models (LLMs) to transform complex biological data into actionable insights for biomarker discovery, therapeutic target identification, and AI-assisted drug discovery.

Currently, I work as a Bioinformatician at the University of Arkansas for Medical Sciences (UAMS), where I develop scalable NGS pipelines, AI-driven computational workflows, and reproducible bioinformatics systems using TCGA and CPTAC datasets to advance cancer genomics research.

My long-term vision is to build intelligent computational systems that bridge biology and artificial intelligence to accelerate scientific discovery and improve patient outcomes.


πŸ”¬ Research Interests

🧬 Computational Biology

🧬 Cancer Genomics

🧬 Precision Oncology

🧬 Bioinformatics

🧬 Multi-Omics Integration

πŸ’Š Drug Discovery

πŸ€– Artificial Intelligence

🧠 Machine Learning & Deep Learning

πŸ“š Large Language Models (LLMs)

⚑ Agentic AI

☁️ Scientific Computing


Scientific Contributions

Healthcare Data Engineering
β”‚
β”œβ”€β”€ NIH Clinical Trials Lakehouse
β”œβ”€β”€ CDC Healthcare Streaming ETL
β”‚

Bioinformatics
β”‚
β”œβ”€β”€ TCGA CPTAC Kafka
β”œβ”€β”€ Synthetic Variant Calling
β”œβ”€β”€ USAG1 Validation
β”‚

Artificial Intelligence
β”‚
β”œβ”€β”€ Genomic Foundation Models
β”‚

Research
β”‚
β”œβ”€β”€ TNBC AI Drug Discovery
β”œβ”€β”€ RTK NRTK Project

βœ” Hybrid-CORE

AI framework for rational drug combination discovery.

βœ” RTK/NRTK

Discovery of compensatory kinase networks using TCGA.

βœ” Biomedical Agentic RAG

LLM-powered biomedical evidence synthesis.

βœ” Foundation Models

Transformer models for genomic sequences.

βš™οΈ Technology Stack

Programming

Artificial Intelligence

Bioinformatics

Cloud & Infrastructure


πŸš€ Featured Research Projects

πŸ₯ NIH Clinical Trials Lakehouse

Production healthcare data engineering platform

GitHub

https://github.com/mtariqi/nih-clinical-trials-lakehouse-pipeline

🌍 CDC Healthcare Streaming ETL

Healthcare streaming analytics pipeline

https://github.com/mtariqi/cdc-healthcare-streaming-etl-pipeline

🎯 TNBC Drug Discovery Pipeline

AI-driven precision oncology

https://github.com/mtariqi/tnbc-drug-discovery-pipeline

🧬 TCGA-CPTAC Kafka Platform

Large-scale cancer multi-omics

https://github.com/mtariqi/tcga-cptac-kafka-bioinformatics

πŸ§ͺ Synthetic Variant Calling Benchmark

Next-generation sequencing benchmarking

https://github.com/mtariqi/synthetic-variant-calling-benchmark

πŸ€– Genomic Foundation Models

Transformer models for genomics

https://github.com/mtariqi/genomic-foundation-models

🦷 USAG1 Validation

Computational target validation

https://github.com/mtariqi/USAG1-Validation

HYDRO-FLOW-AI

License: MIT Python 3.10+ Stage 1: reconstruction done

Extreme-aware AI framework for streamflow prediction and National Water Model (NWM) bias correction.

An independent research project that extends data-processing workflows from the Alabama Water Institute's NWM-ML project, applied specifically to correcting systematic NWM forecast bias at individual USGS gauges β€” with the model explicitly evaluated on its ability to represent extreme, high-flow events, not just average-case accuracy.

Leakage-safe temporal train/validation/test design (no random splits on time-series data) Evaluation beyond RMSE/MAE/NSE: Q95/Q99 error, peak-magnitude error, extreme-event detection Planned: XGBoost residual correction, quantile-regression uncertainty intervals, Transformer/LSTM and river-network graph neural network extensions

πŸ“Š GitHub Statistics


πŸ“ˆ Current Research Focus

  • 🧬 AI for Precision Oncology
  • πŸ’Š Computational Drug Discovery
  • 🧠 Foundation Models for Genomics
  • πŸ“š Biomedical Large Language Models
  • ⚑ Agentic AI for Scientific Discovery
  • πŸ”¬ Cancer Multi-Omics
  • ☁️ Scalable Scientific Computing
  • Hydrology (Extreme-aware AI framework for streamflow prediction and National Water Model (NWM) bias correction.)

Education

  • Advanced diploma in Data Science and Data Engineering
  • MS Bioinformatics
  • MSc Molecular Biology
  • MS Biotechnology
  • BSc Biotechnology and Genetic Engineering

Certifications

  • Health Informatics (Johns Hopkins University)
  • Business Analytics- Data Driven Decision Making (University of Toronto)
  • Project Management
  • Cloud Computing

🌱 Currently Learning

  • Agentic AI
  • Foundation Models
  • Multi-agent Systems
  • Biomedical LLMs
  • Causal AI

🀝 Let's Collaborate

I welcome collaborations in

  • Computational Biology
  • Bioinformatics
  • Precision Medicine
  • Cancer Genomics
  • Artificial Intelligence
  • Drug Discovery
  • Machine Learning
  • Healthcare AI
  • Scientific Software Development

Contact

LinkedIn

https://www.linkedin.com/in/mdtariqulscired

GitHub

https://github.com/mtariqi

Email

mtiumea@gmail.com

Future Roadmap

Healthcare Data Engineering β”‚ β–Ό Public Health Analytics β”‚ β–Ό Cancer Multi-omics β”‚ β–Ό AI Drug Discovery β”‚ β–Ό Foundation Models β”‚ β–Ό Agentic AI for Precision Medicine

"Transforming Biological Data into Intelligent Therapeutic Discovery"

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