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<title>Devansh Pancholi | Computational Biology, Multi-omics & AI</title>
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<p class="eyebrow">Computational biology · Multi-omics · AI</p>
<h1 id="hero-title">DNA. RNA. Cells.<br><em>Connected by computation.</em></h1>
<p class="hero-intro">I’m Devansh, a computational biologist applying computational methods to understand <strong>cancer across molecular scales—how genomic alterations connect with RNA programs, epigenetic regulation, cellular states, and disease behavior.</strong></p>
<p class="hero-description">My research spans single-cell and bulk transcriptomics, somatic-variant analysis, cfDNA biomarkers, and developing spatial integration. I’m particularly interested in how molecular and cellular programs change after treatment, and how these shifts relate to resistance, disease progression, and survival. Alongside this work, I develop reproducible multi-omics workflows and adapt AI models for RNA structure prediction, connecting biological questions with interpretable analysis.</p>
<div class="actions"><a class="button primary" href="#research">Explore my research <span aria-hidden="true">↓</span></a><a class="button secondary" href="assets/Devansh_Pancholi_Resume.pdf" download>Download résumé <span aria-hidden="true">↗</span></a></div>
<div class="hero-footnote"><span>M.S. Bioinformatics · Saint Louis University</span><span>Open to computational research roles</span></div>
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<div class="portrait-frame"><img src="assets/devansh_profile.jpg" alt="Devansh Pancholi" width="460" height="510" fetchpriority="high"><div class="portrait-caption"><span>Devansh Pancholi</span><span>Research portfolio / 2026</span></div></div>
<div class="focus-note"><span class="mono-label">MOLECULES → CELLS → BIOLOGY</span><p>Connecting molecular measurements with biological interpretation.</p><a href="#interests">Explore my research interests <span aria-hidden="true">↗</span></a></div>
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<div class="research-strip container" aria-label="Research areas"><span>Single-cell & spatial transcriptomics</span><span>DNA & epigenetic regulation</span><span>Structural biology & AI</span><span>Multi-omics & biomarkers</span></div>
<section class="section container" id="research" aria-labelledby="research-title">
<div class="section-heading"><div><p class="eyebrow">01 / Selected research</p><h2 id="research-title">Biological questions.<br>Computational evidence.</h2></div><p>From tumor ecosystems to molecular structure: projects grounded in interpretable analysis, cohort context, and reproducible workflows.</p></div>
<article class="featured-project" id="hnscc">
<div class="project-main"><div class="project-meta"><span class="pill">Ongoing research</span><span>01 / Immuno-oncology</span></div><h3>Resolving immune states<br>in head & neck cancer.</h3><p class="project-lead">Characterizing macrophage-associated programs in HNSCC and developing their connection to treatment response and spatial tissue context.</p><ul class="clean-list"><li>Analyze scRNA-seq profiles through quality control, cell-state annotation, myeloid subclustering, and pathway/module scoring.</li><li>Compare SPP1-enriched programs with CXCL9/CXCL10 inflammatory and interferon-responsive signatures.</li><li>Develop integration of scRNA-seq references with spatial transcriptomics to map immune states, explore neighborhoods, and infer candidate cell–cell interactions.</li></ul><div class="tags"><span>Scanpy</span><span>Immune-state analysis</span><span>Spatial integration · developing</span></div><details><summary>Research scope & next steps</summary><p>Ongoing extensions include treatment-versus-control comparisons, human–mouse ortholog mapping, independent-cohort evaluation, and projection into TCGA-HNSC bulk data. Further directions include exRNA detectability and LINCS/Connectivity Map perturbational signatures. These are research analyses and developing directions, not clinically validated biomarkers.</p><p>Cell-type interpretation depends on each dataset’s sampling design; immune-enriched datasets are analyzed as immune references rather than complete tumor ecosystems. Spatial proximity and expression-based communication estimates generate hypotheses, not proof of functional interaction.</p></details></div>
<div class="project-concept"><span class="mono-label">ANALYTICAL FOCUS</span><div class="state-card"><span>Macrophage-associated programs</span><strong>SPP1</strong><p>Immunosuppression-associated<br>Matrix remodeling · Angiogenesis</p></div><div class="state-divider">Comparative cell-state analysis</div><div class="state-card immune"><span>Inflammatory / interferon programs</span><strong>CXCL9 / CXCL10</strong><p>Immune activation-associated<br>Antigen presentation · Inflammation</p></div><p class="concept-caption">Programs being investigated—not a binary classification of macrophage biology.</p></div>
<div class="published-gallery compact-context"><section class="project-gallery" data-gallery aria-label="HNSCC cell populations and workflow" aria-roledescription="carousel"><div class="gallery-heading"><span>Cell populations & analysis context</span><span class="gallery-count" aria-live="polite"></span></div><div class="gallery-slides"><figure class="gallery-slide"><div class="gallery-image"><img src="assets/hnscc_cell_umap.png" alt="Cell populations in the HNSCC microenvironment" loading="lazy"></div><figcaption><strong>Cell populations in the HNSCC microenvironment</strong><p>UMAP separating epithelial, myeloid, lymphoid, and stromal populations.</p><a href="assets/hnscc_cell_umap.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/hnscc_cell_composition.png" alt="Cell composition across study groups" loading="lazy"></div><figcaption><strong>Cell composition across study groups</strong><p>Relative group contributions within each cell lineage.</p><a href="assets/hnscc_cell_composition.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/hnscc_sequencing_workflow.png" alt="Single-cell sequencing workflow" loading="lazy"></div><figcaption><strong>Single-cell sequencing workflow</strong><p>From isolated immune and non-immune cells to reverse transcription, amplification, and sequencing.</p><a href="assets/hnscc_sequencing_workflow.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure></div><div class="gallery-controls" hidden><button type="button" data-direction="-1" aria-label="Previous image in HNSCC cell populations and workflow">← Previous</button><span class="gallery-hint">Explore the figures</span><button type="button" data-direction="1" aria-label="Next image in HNSCC cell populations and workflow">Next →</button></div></section></div></article>
<div class="project-grid">
<article class="project-card atlas"><div class="project-meta"><span>02 / Single-cell genomics</span><span class="project-number">72</span></div><h3>A multi-sample<br>breast-cancer atlas</h3><p>Integrated 72 scRNA-seq samples spanning normal tissue, ER-positive, HER2-positive, triple-negative, and metastatic disease.</p><ul class="clean-list"><li>Performed multi-sample QC, batch-aware integration, clustering, and cell-type annotation.</li><li>Examined malignant, immune, and stromal populations through differential-expression and pathway analysis.</li></ul><div class="tags"><span>scRNA-seq</span><span>Scanpy</span><span>Cell-state heterogeneity</span></div><a class="text-link" href="https://github.com/Daark-Devil/SCRNA_Breast_Cancer_Atlas">Explore repository <span aria-hidden="true">↗</span></a><section class="project-gallery" data-gallery aria-label="Breast cancer atlas gallery" aria-roledescription="carousel"><div class="gallery-heading"><span>Project figures</span><span class="gallery-count" aria-live="polite"></span></div><div class="gallery-slides"><figure class="gallery-slide"><div class="gallery-image"><img src="assets/atlas_project_overview.png" alt="Breast-cancer single-cell atlas" loading="lazy"></div><figcaption><strong>Breast-cancer single-cell atlas</strong><p>Project overview: 72 tissue samples spanning normal, ER-positive, HER2-positive, triple-negative, and metastatic contexts. The overview graphic summarizes the project; it is not a measured UMAP.</p><a href="assets/atlas_project_overview.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/atlas_marker_panel_1.png" alt="Cell-type marker expression — panel 1" loading="lazy"></div><figcaption><strong>Cell-type marker expression — panel 1</strong><p>Marker-expression review across the displayed epithelial, macrophage, fibroblast, and endothelial groups. Dot size represents the fraction of expressing cells; color represents mean expression.</p><a href="assets/atlas_marker_panel_1.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/atlas_marker_panel_2.png" alt="Cell-type marker expression — panel 2" loading="lazy"></div><figcaption><strong>Cell-type marker expression — panel 2</strong><p>Marker-expression review including T-cell and myeloid programs. Compare canonical markers across groups to assess annotation consistency.</p><a href="assets/atlas_marker_panel_2.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/atlas_marker_panel_3.png" alt="Cell-type marker expression — panel 3" loading="lazy"></div><figcaption><strong>Cell-type marker expression — panel 3</strong><p>Additional marker-expression panel for reviewing assigned cell labels. Mixed lineage-marker signals warrant follow-up before biological interpretation.</p><a href="assets/atlas_marker_panel_3.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure></div><div class="gallery-controls" hidden><button type="button" data-direction="-1" aria-label="Previous image in Breast cancer atlas gallery">← Previous</button><span class="gallery-hint">Explore the figures</span><button type="button" data-direction="1" aria-label="Next image in Breast cancer atlas gallery">Next →</button></div></section></article>
<article class="project-card"><div class="project-meta"><span>03 / Liquid-biopsy epigenomics</span></div><h3>Reading cancer signals<br>in plasma cfDNA</h3><p>Combined colorectal-cancer 5hmC profiling and low-pass WGS fragmentomics across discovery and independent validation cohorts at WashU.</p><ul class="clean-list"><li>Quantified gene-body 5hmC, fragment lengths, end motifs, and nucleosome/TF-binding-site-associated features.</li><li>Evaluated progression-associated signals and pre-analytical differences between EDTA and Streck collection.</li></ul><div class="tags"><span>5hmC</span><span>Fragmentomics</span><span>Regulatory analysis</span></div><a class="text-link" href="https://github.com/Daark-Devil/Fragmentomics_Biomarker">Explore repository <span aria-hidden="true">↗</span></a><section class="project-gallery" data-gallery aria-label="cfDNA research gallery" aria-roledescription="carousel"><div class="gallery-heading"><span>Project outputs</span><span class="gallery-count" aria-live="polite"></span></div><div class="gallery-slides"><figure class="gallery-slide"><div class="gallery-image"><img src="assets/cfdna_hallmark_pd_ned.png" alt="Hallmark enrichment: PD vs NED" loading="lazy"></div><figcaption><strong>Hallmark enrichment: PD vs NED</strong><p>Normalized enrichment scores for the PD versus NED comparison, with FDR < 0.05 as labeled in the supplied figure.</p><a href="assets/cfdna_hallmark_pd_ned.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/cfdna_hallmark_crc_non_cancer.png" alt="Hallmark enrichment: CRC vs non-cancer" loading="lazy"></div><figcaption><strong>Hallmark enrichment: CRC vs non-cancer</strong><p>Pathway-level comparison grouped by biological process. The displayed significance threshold is FDR < 0.05.</p><a href="assets/cfdna_hallmark_crc_non_cancer.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/cfdna_cdx2_coverage.png" alt="Coverage around CDX2 sites" loading="lazy"></div><figcaption><strong>Coverage around CDX2 sites</strong><p>Coverage profiles for CRC (n = 66) and non-cancer (n = 147), as labeled in this figure. These counts apply to this panel’s analysis.</p><a href="assets/cfdna_cdx2_coverage.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/cfdna_cdx2_tracks.png" alt="CDX2 genomic tracks" loading="lazy"></div><figcaption><strong>CDX2 genomic tracks</strong><p>Supplied locus-level comparison of CRC and non-cancer tracks around CDX2. Track heights should be interpreted with their original scaling.</p><a href="assets/cfdna_cdx2_tracks.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/frag_short_long_ratio.png" alt="Fragment-length signal" loading="lazy"></div><figcaption><strong>Fragment-length signal</strong><p>Original project figure comparing short-to-long fragment ratios. An exploratory molecular feature, not a clinical diagnostic.</p><a href="assets/frag_short_long_ratio.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure></div><div class="gallery-controls" hidden><button type="button" data-direction="-1" aria-label="Previous image in cfDNA research gallery">← Previous</button><span class="gallery-hint">Flip through outputs</span><button type="button" data-direction="1" aria-label="Next image in cfDNA research gallery">Next →</button></div></section></article>
<article class="project-card"><div class="project-meta"><span>04 / Somatic cancer genomics</span></div><h3>From tumor–normal reads<br>to variant interpretation</h3><p>A reproducible breast-cancer WGS workflow for somatic SNV and indel detection, filtering, annotation, and pathway-level interpretation.</p><ul class="clean-list"><li>Connected alignment and BAM processing with GATK Mutect2 somatic variant calling.</li><li>Used SnpEff annotations to prioritize candidate cancer-associated genes for biological interpretation.</li></ul><div class="tags"><span>BWA-MEM</span><span>Mutect2</span><span>SnpEff</span></div><a class="text-link" href="https://github.com/Daark-Devil/Breast_Cancer_Somatic_Mutation_Analysis">Explore repository <span aria-hidden="true">↗</span></a><section class="project-gallery" data-gallery aria-label="Somatic variant analysis gallery" aria-roledescription="carousel"><div class="gallery-heading"><span>Project figures</span><span class="gallery-count" aria-live="polite"></span></div><div class="gallery-slides"><figure class="gallery-slide"><div class="gallery-image"><img src="assets/variant_project_overview.png" alt="Tumor–normal somatic variant analysis" loading="lazy"></div><figcaption><strong>Tumor–normal somatic variant analysis</strong><p>Project overview: alignment, somatic calling, functional annotation, and cancer-gene prioritization. The overview artwork summarizes the workflow.</p><a href="assets/variant_project_overview.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/variant_candidate_tlod.png" alt="Prioritized variant candidates — sample 11871" loading="lazy"></div><figcaption><strong>Prioritized variant candidates — sample 11871</strong><p>Candidate variants ranked by tumor log-odds (TLOD), including PIK3CA p.His1047Arg and CDH1 p.Arg335*. “11871” identifies the sample. TLOD measures evidence for a variant, not whether it is a functional driver.</p><a href="assets/variant_candidate_tlod.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/variant_gene_effect_matrix.png" alt="Cancer-gene variant consequences" loading="lazy"></div><figcaption><strong>Cancer-gene variant consequences</strong><p>Gene-by-effect matrix from the project repository, summarizing annotated consequences across selected cancer-associated genes. <a class="source-link" href="https://github.com/Daark-Devil/Breast_Cancer_Somatic_Mutation_Analysis/blob/HEAD/main_figures/11871_cancer_gene_effect_matrix.png">View repository figure</a>.</p><a href="assets/variant_gene_effect_matrix.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure></div><div class="gallery-controls" hidden><button type="button" data-direction="-1" aria-label="Previous image in Somatic variant analysis gallery">← Previous</button><span class="gallery-hint">Explore the figures</span><button type="button" data-direction="1" aria-label="Next image in Somatic variant analysis gallery">Next →</button></div></section></article>
<article class="project-card"><div class="project-meta"><span>05 / RNA structural informatics</span></div><h3>RNAMotifDB:<br>sequence to template</h3><p>An RNA motif database and template-search workflow connecting sequence, secondary structure, and tertiary-template information.</p><ul class="clean-list"><li>Built motif extraction, sequence grouping, RMSD-based clustering, and representative-template selection workflows.</li><li>Connected template search with motif-guided RNA three-dimensional structure assembly.</li></ul><div class="tags"><span>Python / C++</span><span>Structural alignment</span><span>RNA motifs</span></div><a class="text-link" href="https://github.com/Daark-Devil/RNAMotifDB">Explore repository <span aria-hidden="true">↗</span></a><details><summary>View the template-search workflow</summary><figure><img src="assets/rnamotifdb_template_search.svg" alt="RNAMotifDB template-search example showing motif decomposition and structural template matches" loading="lazy" width="900" height="600"><figcaption>Original RNAMotifDB project illustration: motif decomposition and template search.</figcaption></figure></details></article>
</div>
<article class="rna-project"><div><div class="project-meta"><span>06 / Machine learning & RNA biology</span></div><h3>Better structural inputs.<br>More informative predictions.</h3><p>Modified OpenFold3 to accept RNA structural templates and integrated synthetic multiple-sequence alignments derived from RNA secondary structure, then benchmarked predictions against baseline models.</p><ul class="clean-list"><li>Developed parallel Nextflow/HPC workflows for preprocessing, inference, PDB generation, and structural evaluation.</li><li>Achieved over 70% lower C1′ RMSD in selected benchmark cases: for 1P5P, the combined approach reduced RMSD from 22.452 Å to 3.565 Å (approximately 84%). Performance varied across the 22-RNA comparison, with non-improving cases retained.</li></ul><div class="tags"><span>Modified OpenFold3</span><span>RNA templates / synthetic MSA</span><span>Nextflow</span></div><a class="text-link" href="https://github.com/Daark-Devil/RNA-Structure-AI">Explore repository <span aria-hidden="true">↗</span></a><p class="benchmark-note"><a href="https://github.com/Daark-Devil/RNA-Structure-AI/blob/HEAD/models/openfold3/results/Csvs/4cond_results.tsv">View per-RNA benchmark results ↗</a></p></div><section class="project-gallery" data-gallery aria-label="RNA structure research gallery" aria-roledescription="carousel"><div class="gallery-heading"><span>Project outputs</span><span class="gallery-count" aria-live="polite"></span></div><div class="gallery-slides"><figure class="gallery-slide"><div class="gallery-image"><img src="assets/rna_template_sequence_comparison.png" alt="Reference, templates, and sequence-only predictions" loading="lazy"></div><figcaption><strong>Reference, templates, and sequence-only predictions</strong><p>Both prediction panels are annotated 1.42 Å in this example. This panel illustrates a comparison, not a template-associated improvement.</p><a href="assets/rna_template_sequence_comparison.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/rna_structure_overlay.png" alt="RNA structural overlay" loading="lazy"></div><figcaption><strong>RNA structural overlay</strong><p>A supplied structural rendering for qualitative comparison of RNA conformations. No additional accuracy score is inferred from the image.</p><a href="assets/rna_structure_overlay.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure><figure class="gallery-slide"><div class="gallery-image"><img src="assets/1E7K_msa_improvement.png" alt="1E7K: baseline and synthetic-MSA comparison" loading="lazy"></div><figcaption><strong>1E7K: baseline and synthetic-MSA comparison</strong><p>Individual example retained from the original portfolio. This figure is separate from the aggregate benchmark summary in the project description.</p><a href="assets/1E7K_msa_improvement.png" target="_blank" rel="noopener">Open full-size image ↗</a></figcaption></figure></div><div class="gallery-controls" hidden><button type="button" data-direction="-1" aria-label="Previous image in RNA structure research gallery">← Previous</button><span class="gallery-hint">Flip through outputs</span><button type="button" data-direction="1" aria-label="Next image in RNA structure research gallery">Next →</button></div></section></article>
</section>
<section class="section container workflow-section" id="workflow" aria-labelledby="workflow-title"><div class="section-heading"><div><p class="eyebrow">How I work</p><h2 id="workflow-title">From a biological question<br>to a reproducible result.</h2></div><p>The same analytical discipline connects my sequencing, biomarker, and structural-modeling projects.</p></div><ol class="workflow-grid"><li><span>01</span><h3>Define the question</h3><p>Identify the biological comparison, appropriate datasets, sample structure, and relevant metadata.</p></li><li><span>02</span><h3>Prepare & quality-check</h3><p>Inspect technical variation; preprocess reads, count matrices, genomic features, or molecular structures.</p></li><li><span>03</span><h3>Build the representation</h3><p>Generate gene programs, regulatory features, variants, structural templates, or learned embeddings.</p></li><li><span>04</span><h3>Compare & evaluate</h3><p>Use statistical analysis, cohort comparisons, interpretable models, or structural benchmarks to test the question.</p></li><li><span>05</span><h3>Communicate & reproduce</h3><p>Document decisions, visualize evidence, retain limitations, and organize code for repeatable analysis.</p></li></ol></section>
<section class="section experience-section" id="experience" aria-labelledby="experience-title"><div class="container"><div class="section-heading"><div><p class="eyebrow">02 / Research experience</p><h2 id="experience-title">From methods<br>to meaningful analysis.</h2></div><p>Research across cancer transcriptomics, clinical-cohort epigenomics, and machine-learning-enabled RNA biology.</p></div>
<div class="timeline">
<article class="experience-row"><div class="experience-date">Apr 2026 — Present<span>Saint Louis University</span></div><div><h3>Bioinformatics Analyst</h3><p class="role-focus">HNSCC tumor–immune states & multimodal RNA biomarkers</p><ul class="clean-list"><li>Analyzing human HNSCC single-cell data with an emphasis on macrophage-associated immune-response programs.</li><li>Developing treatment-response comparisons and single-cell-derived signatures for bulk-cohort analysis.</li><li>Extending the research toward spatial transcriptomic integration and tissue-level immune organization.</li></ul></div></article>
<article class="experience-row"><div class="experience-date">Jan 2026 — May 2026<span>Washington University in St. Louis</span></div><div><h3>Research Assistant</h3><p class="role-focus">Colorectal-cancer epigenomic biomarkers · Advisor: Dr. Maher</p><ul class="clean-list"><li>Integrated plasma cfDNA 5hmC and LP-WGS features across discovery and validation cohorts.</li><li>Implemented and troubleshot Python/R and HPC workflows for alignment, feature generation, regulatory analysis, and cohort comparisons.</li><li>Examined baseline molecular profiles in relation to later clinical outcomes while assessing collection-related confounders.</li></ul></div></article>
<article class="experience-row"><div class="experience-date">Mar 2025 — May 2026<span>Saint Louis University</span></div><div><h3>Research Assistant</h3><p class="role-focus">AI-enabled RNA structure prediction · Advisor: Dr. Jie Hou</p><ul class="clean-list"><li>Built RNAMotifDB and Python/C++ workflows for motif extraction, alignment, clustering, and template-guided assembly.</li><li>Modified nine OpenFold3 source files to support RNA templates and integrated secondary-structure-derived synthetic MSAs for benchmarked structure prediction.</li><li>Engineered parallel inference and evaluation workflows with structural repair and residue-coordinate mapping.</li></ul></div></article>
</div></div></section>
<section class="section container" id="expertise" aria-labelledby="expertise-title"><div class="section-heading"><div><p class="eyebrow">03 / Technical expertise</p><h2 id="expertise-title">A toolkit built<br>around the question.</h2></div><p>Established analysis and engineering experience, with developing methods identified explicitly.</p></div>
<div class="skill-grid"><article><span class="skill-index">01</span><h3>Transcriptomics & cell states</h3><p>Single-cell QC and integration, annotation, differential expression, gene-module scoring, pathway analysis, and bulk-transcriptomic analysis.</p><div class="tags"><span>Scanpy</span><span>STAR</span><span>featureCounts</span><span>DESeq2 / edgeR</span></div><p class="development">Developing: spatial integration, cellular neighborhoods, and expression-based cell–cell communication.</p></article>
<article><span class="skill-index">02</span><h3>Genomics & epigenomics</h3><p>Tumor–normal variant workflows, cfDNA 5hmC, fragmentomics, cohort comparisons, and regulatory-feature interpretation.</p><div class="tags"><span>BWA</span><span>SAMtools</span><span>GATK</span><span>SnpEff</span><span>MACS2</span><span>Griffin</span></div></article>
<article><span class="skill-index">03</span><h3>Machine learning & RNA</h3><p>Biological feature engineering, explainable models, RNA sequence/structure representations, and prediction benchmarking.</p><div class="tags"><span>scikit-learn</span><span>XGBoost / SHAP</span><span>OpenFold3 modification</span><span>Diffusion models</span><span>RiNALMo / ESM-2</span></div></article>
<article><span class="skill-index">04</span><h3>Reproducible computation</h3><p>Workflow development, HPC execution, debugging, automation, version control, and clear analytical reporting.</p><div class="tags"><span>Python / R / C++</span><span>Bash / SQL</span><span>Linux / SLURM / LSF</span><span>Nextflow / Snakemake</span><span>Docker / Git / AWS</span></div></article></div>
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<section class="section about-section" id="about" aria-labelledby="about-title"><div class="container about-grid"><div><p class="eyebrow">04 / Background</p><h2 id="about-title">An engineering foundation.<br>A biological focus.</h2><p class="about-copy">I trained in biomedical engineering before completing my M.S. in Bioinformatics at Saint Louis University. I’m interested in how molecular measurements reveal cancer biology—and in building the computational workflows that make those measurements interpretable.</p><p class="about-copy">My research connects DNA variation, RNA expression, epigenetic regulation, and molecular structure. Cancer and biomarker studies are important applications of this broader interest in multi-omics analysis and biological machine learning.</p><a class="text-link" href="assets/Devansh_Pancholi_Resume.pdf">Read my full résumé <span aria-hidden="true">↗</span></a></div><div class="education"><article><span class="mono-label">MAY 2026</span><h3>M.S. Bioinformatics</h3><p>Saint Louis University, USA</p><p><strong>GPA: 3.97</strong></p></article><article><span class="mono-label">JUNE 2023</span><h3>B.E. Biomedical Engineering</h3><p>L.D. College of Engineering, India</p><p><strong>GPA: 3.63</strong></p></article></div></div>
<div class="container additional-work"><h3>Earlier work, still part of the story.</h3><div class="additional-grid"><article><span class="mono-label">PUBLICATION / IEEE ASIANCON 2023</span><h4>Smart Stethoscope</h4><p>IoT-enabled medical-device work connecting biomedical signal acquisition, hardware, and computation.</p><a class="text-link" href="https://ieeexplore.ieee.org/document/10270283/">View publication <span aria-hidden="true">↗</span></a></article><article><span class="mono-label">APPLIED MACHINE LEARNING</span><h4>Protein representations & interpretable models</h4><p>Earlier ESM-2-based classification work using biological embeddings and SHAP interpretation, retained alongside my current cancer research.</p><a class="text-link" href="https://github.com/Daark-Devil">Browse GitHub <span aria-hidden="true">↗</span></a></article></div></div></section>
<section class="section container interests-section" id="interests" aria-labelledby="interests-title"><div class="section-heading"><div><p class="eyebrow">Research interests</p><h2 id="interests-title">Across molecular layers.<br>Across biological questions.</h2></div><p>Areas I want to keep building in, combining established experience with new analytical directions.</p></div><ul class="interest-grid"><li>Single-cell & bulk transcriptomics</li><li>Spatial transcriptomics & tissue organization</li><li>DNA variation & cancer genomics</li><li>Epigenomics, 5hmC & gene regulation</li><li>RNA sequence, structure & function</li><li>Biomarker discovery & liquid biopsies</li><li>Multi-omics integration</li><li>Machine learning & AI for biology</li><li>Tumor–immune microenvironments</li><li>Clinical & translational data analysis</li><li>Bioinformatics tools & databases</li><li>Scalable, reproducible workflows</li></ul></section>
<section class="contact-section container" id="contact" aria-labelledby="contact-title"><p class="eyebrow">05 / What’s next</p><h2 id="contact-title">Let’s turn complex data<br>into biological insight.</h2><p>I’m interested in computational biology and bioinformatics research spanning single-cell and spatial transcriptomics, genomics, epigenetic regulation, biomarkers, structural biology, and machine learning.</p><a class="contact-email" href="mailto:pancholidevansh29@gmail.com">pancholidevansh29@gmail.com <span aria-hidden="true">↗</span></a><div class="contact-links"><a href="https://github.com/Daark-Devil">GitHub ↗</a><a href="https://www.linkedin.com/in/devansh-pancholi-59560433a/">LinkedIn ↗</a><a href="assets/Devansh_Pancholi_Resume.pdf" download>Download résumé ↓</a></div></section>
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