Hire the Best Bioinformatics Developers

Turning your complex biological data into drug discovery insights. I'm a computational biologist with a PhD in Structural Bioinformatics and Biophysics, 10+ peer-reviewed publications, and years of hands-on research at the intersection of molecular modeling, OMICS, and cancer biology. I help biotech startups, pharma teams, CROs, and academic spin-offs turn raw data into clear, actionable results — without the cost and commitment of a full-time hire. My background spans both the structural world (proteins, RNA, molecular simulations) and the data-intensive world of genomics and transcriptomics. This dual expertise means I can follow the biology end-to-end: from a patient's genome to the 3D structure of the drug target to the atomic-level simulation of drug binding. Very few consultants cover this full spectrum. ── GENOMICS & VARIANT ANALYSIS ── WGS and WES analysis to identify disease-causing mutations, pharmacogenomic markers, and risk variants. Fully annotated variant reports with pathogenicity scoring, population frequency filtering, and clinical significance assessment. ── TRANSCRIPTOMICS & RNA-SEQ ── Bulk RNA-Seq, single-cell/single-nucleus (scRNA-Seq, snRNA-Seq), and spatial transcriptomics (Visium, MERFISH, Xenium). Differential expression, pathway enrichment, gene ontology networks, cell-type annotation, trajectory analysis — all delivered with publication-quality figures and biological interpretation. ── NON-CODING RNA (ncRNA) ── miRNA target prediction, lncRNA functional annotation, small RNA-Seq analysis, siRNA/ASO off-target screening, and ncRNA expression profiling. Critical for RNA therapeutics — and a service very few bioinformatics freelancers offer. ── STRUCTURAL BIOINFORMATICS (PROTEIN & RNA) ── My core training. 3D structure prediction and modeling for both proteins and RNA (AlphaFold, RoseTTAFold, RNAfold, FARFAR2). Molecular docking (protein-ligand and RNA-ligand), virtual screening, binding site analysis, and mRNA structure optimization. Working on both protein AND RNA targets is a genuine rarity in this market. ── MOLECULAR DYNAMICS & QM/MM ── Full atomistic MD (GROMACS, AMBER, NAMD, OpenMM) for drug binding stability, binding free energies, conformational landscapes, and cryptic pocket discovery. QM/MM calculations for reaction mechanisms and electronic-level binding insights. All running on dedicated GPU-accelerated HPC workstations — enabling simulations most consultants cannot handle. ── ANTIBODY COMPUTATIONAL ANALYSIS ── Structure modeling, CDR optimization, antibody-antigen docking, humanization, developability prediction. De novo sequence design via inverse folding (ProteinMPNN) — given a desired backbone geometry, I generate optimized sequences for rational CDR redesign and novel antibody engineering. ── MULTI-OMICS & PIPELINES ── Integration of genomics, transcriptomics, proteomics, and epigenomics into unified biological narratives. Automated, containerized pipelines (Nextflow, Snakemake, Docker) for reproducible analysis at scale. ── WHY CLIENTS CHOOSE TO WORK WITH ME ── → End-to-end delivery. Interpreted results, publication-quality figures, clear narratives — ready for investors, grants, regulatory submissions, or journals. Not raw tables. → Serious infrastructure. Dedicated GPU-accelerated HPC for large-scale virtual screening, long MD/QM-MM trajectories, and genome-wide analyses. → Full spectrum, one contact. Genomics to molecular dynamics to pipeline automation — no need to coordinate multiple freelancers. → Fast start, iterative delivery. Most projects begin within days. You see progress throughout, with no final dump to deal with. → Confidentiality & IP protection. I routinely handle proprietary compounds, unpublished structures, and preclinical data. Fully comfortable with NDAs and data handling agreements. All deliverables and code are yours — clean IP transfer, no strings attached. I work with preclinical-stage biotechs, academic spin-offs, CROs needing white-label bioinformatics, and AI-driven drug discovery companies. Whether it's a one-off analysis, a multi-month engagement, or a standing retainer — let's talk.

Hi! I'm a bilingual English–Chinese content writer with a background in scientific research and IT recruitment. I help businesses and professionals create clear, engaging, and accurate content—from blog articles and website copy to LinkedIn profiles, resumes, market research, and English–Chinese translations. My experience in scientific research allows me to understand technical topics quickly, while my recruitment background helps me write content that speaks to employers, job seekers, and business audiences. I value clear communication, attention to detail, and delivering work on time. 🔬 Scientific & Technical Writing – Experienced in research, technical documentation, and simplifying complex topics into reader-friendly content. 🌏 Multilingual Communication - Native Mandarin Chinese (Traditional& Simplified), Fluent English, Conversational Cantonese & Malay ✍️ Passion for Content Writing – Create natural, engaging content for blogs, social media captions, business copy, and translations. 💼 IT Recruitment Expertise – Experienced in Client Engagement, Tech Recruitment, LinkedIn sourcing, resume screening, and business communication. - Career Coaching - LInkedIn Profile Optimisation - CV Writing - Candidate Sourcing - Job Description Writing - Interview Preparation

I help genomics companies, academic research labs, and PhD researchers turn complex sequencing and clinical datasets into publication-ready results, grant-winning insights, and actionable biological discoveries. With hands-on expertise in bulk RNA-Seq, single-cell RNA-Seq, multi-omics integration, and clinical biostatistics, I deliver more than analysis; I provide scientific interpretation, reproducible pipelines, and clear visualisations that accelerate decision-making. Whether you're: Identifying novel therapeutic targets Dissecting cellular heterogeneity Integrating transcriptomics with epigenomics Validating biomarkers for clinical studies Preparing figures for high-impact journals I ensure your data becomes a defensible scientific asset — not just processed output. Let's transform your raw data into meaningful biological insight. 🧬 Core Services 1️⃣ Bulk RNA-Seq & Transcriptome Analysis Tools: HISAT2, STAR, DESeq2, edgeR Deliverables: Differential expression analysis Pathway & enrichment analysis Publication-ready plots Reproducible pipelines Best For: Mechanistic studies, biomarker discovery, therapeutic target identification. 2️⃣ Single-Cell RNA-Seq (scRNA-Seq) Tools: Cell Ranger, Seurat, Monocle Deliverables: Clustering & annotation Trajectory analysis Rare cell population detection Integration of multiple datasets Best For: Tumour microenvironment, immune profiling, tissue heterogeneity. 3️⃣ Multi-Omics Data Integration Transcriptomics + Epigenomics RNA-Seq + Clinical Metadata Cross-platform data harmonisation Outcome: Systems-level biological insight and stronger mechanistic conclusions. 4️⃣ Clinical Biostatistics & Modelling Logistic regression Survival analysis Bayesian models ML classifiers (Random Forest, XGBoost, LASSO) Clinical dataset interpretation Outcome: Robust statistical validation for publications, diagnostics, and trials. 🧪 Technical Expertise Programming: Python, R, Bash, SQL Bioinformatics: GATK, STAR, DESeq2, Seurat, QIIME, MACS2 Statistics & ML: Regression, Bayesian modelling, AI/ML pipelines Visualization: ggplot2, Matplotlib Infrastructure: HPC environments, reproducible workflows, Git 🎯 Stronger Call to Action If you need reliable bioinformatic support for your lab, startup, or thesis, let's discuss your dataset and research goals. Prioritise clarity, reproducibility, and scientific rigour.

Bioengineer and former Founding R&D Engineer at BioCompute Inc., with ~4 years of experience across startups and international research labs in computational biology, bioinformatics, and genomic data analysis. Named inventor on a US patent filing. I build computational pipelines and analytical systems for biological data: NGS, WGS, long-read sequencing, and high-dimensional experimental outputs. My work combines bioinformatics, statistical modeling, signal processing, and machine learning to extract interpretable patterns from complex biological systems, often within larger pipelines that integrate APIs, containerized environments, and multiple software components. My experience spans genome assembly, variant analysis, methylation detection, structural bioinformatics, raw signal analysis from Oxford Nanopore platforms, and physiological signal analysis. Because I work across both experimental genomics and computational analysis, I can quickly diagnose issues at the interface between sequencing workflows and downstream data interpretation. I also take on biostatistics consulting work including experimental design, hypothesis testing, and grant-level analytical support. At BioCompute, I built a sequencing-based proof-of-concept system integrating native DNA workflows with a custom bioinformatics decoding pipeline developed from scratch, validated on real sequencing runs and contributing to a US patent filing where I am a named inventor. I also delivered genome assembly, methylation analysis, and structured analytical reporting to external customers. What I work with: - Next-generation sequencing (short- and long-read, WGS) - Oxford Nanopore (MinION Mk1B/Mk1D, PromethION; wet lab + analysis) - Genome assembly and annotation (bacterial and small genomes) - Variant analysis and mutation interpretation - Structural bioinformatics (AlphaFold, DSSP, molecular docking) - Modified-base and methylation analysis (pysam, modkit, SAMtools) - Time series and signal processing (FFT, ML-based approaches) - Physiological and biosignal data analysis - Biological image analysis (cell segmentation, microscopy, CV quantification) - Statistical modeling and biostatistics (regression, hypothesis testing, experimental data interpretation) - Lab automation (Opentrons-style JSON protocol systems) - Python bioinformatics pipelines (Linux, Bash) - Containerized and reproducible workflows (Docker, Snakemake) - API-driven data pipelines and backend integrations Tools and technologies: - Python scientific stack (NumPy, SciPy, Pandas, scikit-learn) - Deep learning (PyTorch, TensorFlow) - Image/video analysis (OpenCV, scikit-image) - Docker, Snakemake - Linux, Bash, REST APIs, JSON-based services Wet lab and experimental consulting: Experimental design for sequencing-based studies. Troubleshooting PCR, cloning, library prep, and sequencing workflows. Guidance on protein expression and molecular cloning workflows. Connecting wet lab outputs with downstream computational analysis. Genomics research and analytical consulting: Technical and market research on genomics and sequencing technologies. Variant impact assessment and interpretation workflows. Literature analysis and publication mapping. Translating computational outputs into structured reports and slide decks for both technical and non-technical stakeholders. What you get: - Reproducible, well-documented bioinformatics workflows - Systems-level thinking across wet lab and computational layers - Clear outputs with strong attention to data integrity - Fast turnaround If you're working with sequencing data, complex biological signals, clinical or experimental datasets, or need grant-level analytical support, let's talk.

🚀 Passionate Machine Learning and Data Science Expert with 4+ Years of Experience Welcome to my Upwork profile! I am a results-driven Machine Learning and Data Science professional dedicated to turning data into actionable insights. Over the past three years, I have honed my skills in various aspects of the field, achieving significant milestones that showcase my commitment to excellence. 🌟 Recognized as a top-rated performer within my companies 🤖 Built Successful Models 🔍 Specialized in Predictive Models in Healthcare Key Milestones: 🔍 Data Exploration and Analysis: Conducted in-depth data analysis to uncover patterns, trends, and valuable insights. Utilized advanced statistical techniques to interpret complex data sets. 🤖 Machine Learning Model Development: Designed and implemented machine learning models for predictive analytics, classification, and clustering. Achieved notable accuracy improvements through iterative model refinement. 📊 Data Visualization: Created compelling visualizations to communicate data-driven stories effectively. Used tools like Matplotlib, Seaborn, and Tableau to present complex information in a clear and concise manner. 🛠️ Tool Proficiency: Mastered a range of tools and technologies, including Python, TensorFlow, PyTorch, scikit-learn, and more. Applied these skills to develop robust solutions tailored to unique business challenges. 📈 Performance Optimization: Enhanced model performance and efficiency through optimization techniques. Implemented strategies to handle large-scale datasets and streamline computational processes. 🤝 Collaborative Projects: Successfully collaborated with cross-functional teams, including developers, analysts, and business stakeholders, to integrate machine learning solutions into real-world applications. Why Work With Me: 🌐 With a strong foundation in both theoretical concepts and practical applications, I bring a holistic approach to data-driven problem-solving. 🚀 My passion for staying updated with the latest advancements in the field ensures that I deliver cutting-edge solutions that align with industry best practices. 🤓 I thrive on challenges and am committed to providing high-quality, customized solutions that meet the unique needs of each project. Let's embark on a data-driven journey together! Whether you need assistance with predictive modeling, data analysis, or implementing machine learning solutions, I am here to help you achieve your goals.

I’m a bioinformatics specialist with deep expertise in multi-omics, advanced statistics, and data-driven computational biology. I build end-to-end analysis pipelines, machine-learning workflows, and full-stack analytical applications tailored for research and biotech. My core strengths include: • Multi-omics analysis: bulk & single-cell RNA-seq, ATAC-seq, proteomics, lipidomics, metabolomics, cytokine panels, and integrative multi-omics modeling (MOFA, DIABLO, WGCNA, chromVAR, network analysis). • Advanced statistics & computational methods: differential analysis, mixed effects models, longitudinal designs, batch correction, feature selection, Bayesian modeling, and high-dimensional inference. • Machine learning & AI: supervised and unsupervised learning, classification, clustering, feature engineering, model tuning, and interpretable ML for biological and clinical datasets. • Python and R engineering: reproducible pipelines, notebooks, renv/venv environments, optimized code, data wrangling, visualization, and automation (tidyverse, DESeq2, Seurat, limma, sklearn, PyTorch-light workflows). • Biostatistics & data science: rigorous data QC, exploratory analysis, normalization, imputation, effect estimation, and publication-ready figures. • Full-stack app design & dashboards: R Shiny, Plotly Dash, Streamlit, FastAPI back ends, and modern front-end development using React, Vite, Tailwind, component libraries, and API-driven architectures. I build clean, fast, and interactive tools for analytics, reporting, and client use. I deliver production-ready, reproducible, and well-documented workflows suitable for research labs, startups, and clinical projects. At the tail end of my skill set, I also support small-scale computational chemistry tasks including virtual screening, molecular dynamics, and light drug-design workflows, when relevant to biological findings. If you need a bioinformatics expert who merges strong statistical foundations, robust coding, full-stack development, and deep domain knowledge, I can take your project from raw data to polished, actionable insight.