Machine Learning, Data Science, Ngs Data Analysis, Molecular Doking

Dhanus Chinnakalipatti Thilagaa Ramesh

R ***************.*@************.*** dhanus-ramesh dhaunscodehub Ó +1-857-***-**** Boston, MA EDUCATION

Northeastern University, Boston, MA Jan 2025 – Dec 2027 Master of Science in Bioinformatics

Relevant courses: Genomics, Transcriptomics, Computational Methods, Programming, Statistics, Machine Learning KMCH College of Pharmacy, Tamil Nadu, India Sep 2019 – Jan 2024 Bachelor of Pharmacy

Relevant courses: Biotechnology, Cell & Molecular Biology, Computer-Aided Drug Design, Medicinal Chemistry SKILLS

Programming Languages: Python, R-Studio, SQL, BASH, Matlab Libraries: BioPython, Seurat, PyTorch, TensorFlow, DESeq2, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn Bioinformatic Tools: GATK, STAR, HISAT2, BWA, SAMtools, bcftools, Trimmomatic, Cutadapt, FastQC, Salmon, IGV, Kallisto, featureCounts, VCFtools

Databases: TCGA, CPTAC, NCBI, Ensembl, BLAST, GEO, UniProt, GO, KEGG, PDB, dbSNP, ClinVar Workflow & DevOps: Docker, GitHub, Conda, HPC (Slurm, SSH, Job Scheduling), Bash, Snakemake, Nextflow, Jupyter Notebook, Galaxy

AI/ML & Data Science: Machine Learning (Classification, Regression, Clustering), Deep Learning (CNNs, RNNs), Computer Vision (Image Analysis & Preprocessing), Data Mining CADD Tools: Autodock Vina, Schrodinger, Gromacs, Amber, PyMOL, SwissADME, OpenBabel, Chimera Soft & Professional Skills: Project Management, Critical Thinking, Collaboration, LaTeX, MS Office, R Markdown EXPERIENCE

Research Intern - Ethical Edufabrica Pvt. Ltd.–Variant Calling & Genomic Analysis Jun 2024 - Jul 2024

• Conducted variant calling on Homo sapiens and Bos taurus NGS datasets using FastQC, Trimmomatic, BWA

• Aligned reads to reference genomes, visualized mapped regions in IGV, and analyzed VCF to identify mutation. Research Assistant - Medicinal Chemistry Lab - Coimbatore, India Jun 2022 – Dec 2022

• Designed & synthesized 6 novel flavone derivatives; performed Molecular Docking,ADMET Prediction, and Molecular Dynamics . Experimentally confirmed antibacterial potency using Disc Diffusion method and UV Spectrophotometry Chief of Editor & Event Co-ordinator - KMCH College of Pharmacy, India Sep 2022 – Dec 2023

• Directed the college magazine publication and successfully coordinated the International Pharmaceutical Con- ference and Medifest (intercollegiate festival), leading teams, managing logistics, and event execution. PROJECTS

Deep Learning-Based Multi-omic Biomarker Prediction from Histopathology Images Jan 2025 – Apr 2025

• Trained 25 CNN models to predict 400+ multi-omic biomarker across 32 cancer types from H&E images.

• Achieved up to 0.909 AUC, validated on large-scale cancer datasets (TCGA and CPTAC) to ensure generalizability

• Applied deep learning to integerate Genomics, Transcriptomics, and Proteomic for biomarker discovery in cancer. Transcriptomic Profiling via RNA-seq: From Raw Reads to Pathway Insights Apr 2025 – Jun 2025

• Developed & executed RNA-seq workflow using FastQC, Salmon, and R based packages for downstream interpretation.

• Identified 500 DEGs with adjusted p-value <0.05, and revealed key enriched pathway from GEO dataset.

• Generated biological insights and visualizations including PCA, volcano, and heatmapsfor transcriptomic exploration. Pipeline-Based Evaluation of Variant Detection Accuracy in Clinical Exome Datasets Jun 2025 – Aug 2025

• Implemented a fully automated pipeline using nf-core/variant calling to process NA12878 Whole Exome Sequencing

(WES) data, generating high-confidence variant calls with GTAK HaplotypeCaller, FreeBayes, and DeepVariant.

• Performed comprehensive benchmarking of VCF using nf-core/variant benchmarking against GIAB truth set, achiev- ing: > 99.2% SNV precision, 98% recall, and F1-score >98.5% in exonic region using DeepVariant.

• Delivered an end-to-end, pipeline using Nextflow, Docker, and Conda, ensuring cross-platform compactibility. Computational Design of selective CA IX/XII Inhibitors as Anti-Cancer Agents Jun 2023 - Dec 2023

• Screened over 400 ligands using AutoDock Vina, identifying top hits ( −9.5kcal/mol) against CA IX/XII isoforms

• Assessed drug likeness and pharmacokinetic properties via SwissADME and predicted activity with QSAR (R2 0.85)

• Conducted 50 ns Molecular Dynamics simulations (GROMACS); prioritized top 3 compounds based RMSD < 2.5 A.

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