Medical Research

Computational biology, clinical research support, and biomedical data analysis

Service Overview

Accelerate medical discoveries through advanced computational methods and data analysis. Our medical research services support academic institutions, pharmaceutical companies, and healthcare organizations with bioinformatics, clinical trial analysis, medical imaging analysis, and translational research.

We combine expertise in computational biology, statistics, and machine learning to analyze genomic data, identify disease biomarkers, support drug discovery, and extract insights from complex biomedical datasets—all while ensuring HIPAA compliance and research integrity.

Core Capabilities

  • Bioinformatics: Genomic sequence analysis, RNA-seq, variant calling, pathway analysis, and multi-omics integration
  • Clinical Trial Support: Statistical analysis plans, survival analysis, longitudinal data analysis, and regulatory reporting
  • Medical Imaging AI: Deep learning for radiology, pathology image analysis, tumor segmentation, and diagnostic support
  • Drug Discovery: Target identification, virtual screening, QSAR modeling, and molecular docking studies
  • Biomarker Discovery: Feature selection, classification models, and validation for diagnostic and prognostic biomarkers
  • Electronic Health Records: EHR data mining, cohort identification, real-world evidence generation, and clinical decision support

Best Practices & Methodologies

Research Standards

  • Regulatory Compliance: HIPAA, GCP, FDA 21 CFR Part 11, and IRB requirements
  • Reproducible Research: Version control, documented workflows, and publicly shared code/data where appropriate
  • Statistical Rigor: Pre-registration, multiple testing correction, and transparent reporting (CONSORT, STROBE guidelines)
  • Data Security: De-identification, secure data storage, encrypted transfer, and access controls
  • Validation: Independent validation cohorts, cross-validation, and external dataset testing
  • Clinical Collaboration: Work closely with clinicians to ensure biological validity and clinical relevance

Tools & Technologies

R / Bioconductor Python (BioPython, scikit-bio) GATK / SAMtools PyTorch / TensorFlow (Med AI) ImageJ / QuPath Cytoscape / STRING SAS / SPSS (Clinical) OMOP / FHIR

Research Areas

Oncology

Cancer genomics, tumor profiling, treatment response prediction, immunotherapy analysis

Cardiovascular

Risk prediction, ECG analysis, cardiac imaging, genetic risk factors

Neuroscience

Brain imaging analysis, neurodegenerative disease, EEG/MEG analysis, connectomics

Rare Diseases

Whole genome sequencing, variant interpretation, patient registries

Advance Medical Science

Accelerate discoveries with computational approaches to biomedical research.

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