What makes a human?

One human is more than a data point. The blueprint and molecular building blocks of a human constitute billions of data points.


Protein coding and non-coding RNA genes

3.2 billion

Base pairs in the human genome

>6 million

Estimated number of proteoforms in a single human cell


Small molecules that have been detected in human blood

38 trillion

Approximate number of bacterial cells in a single human adult

The deluge of molecular data generated for understanding human health is immense.

BioinfoScientist employs advanced machine learning, AI, and data science techniques to help organizations use molecular data to treat disease and improve human health.


We offer cutting-edge solutions for transforming your molecular data into insights. We use cloud-based pipelines for processing, analyzing, and delivering insights from:

  • Genomics and genetics data including GWAS and whole-genome sequencing analyses
  • Epigenetics including DNA methylation, DNA-protein interaction, and chromatin accessibility analyses
  • Bulk and single-cell RNA-seq, differential splicing, miRNA and long non-coding RNA analyses
  • Proteomics analyses including mass-spectrometry (shotgun and targeted) and aptamer-based
  • Metabolomics and lipidomics analyses (targeted and non-targeted analyses)

Machine learning & AI

All humans are ~99.9% genetically similar. We leverage machine learning (ML) and artificial intelligence (AI) to determine how the remaining 0.1% of genetic variance contributes to health and disease. Moreover, we use ML and AI to go beyond the genome, using these techniques to understand the transcriptome, proteome, and metabolome. We employ ML and AI for:

  • Biomarker discovery
  • Identification of molecular signatures
  • Predicting response to new drugs
  • Enabling differential diagnoses and prognostic stratification from molecular and clinical data

Biostatistics & Epidemiology

From pre-clinical experiments to clinical studies and population-based research, employment of rigorous statistical analyses and modeling techniques is key to success and regulatory compliance. We have expertise in:

  • Experimental and study design (including sample size determination and power analysis)
  • Statistical programming
  • Survival analysis
  • Bayesian inference and Bayesian modeling
  • High dimensional data analysis
  • Causal inference

Data Science and Automation

Many organizations conducting clinical and pre-clinical research are beholden to manual processes. We help organizations leverage data science for converting manual processes into automated workflows. Our expertise includes:

  • Developing and validating software for routine laboratory analyses
  • Implementing automated quality control measures and practices
  • Programming liquid handling lab robotics
  • Developing analytics dashboards for monitoring processes

Who we serve

Pharmaceutical companies

Pharmaceutical companies

Contract research organizations

Contract research organizations (CROs)

Biotechnology companies

Biotechnology companies

Academic labs

Academic Labs

Partner with us to leverage your data for transforming human health

To consult with our data scientists about your project needs, please reach out with the contact information below, or send us a message using the form.