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.
>40,000
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
>15,000
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.
Bioinformatics
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
Contract research organizations (CROs)
Biotechnology companies
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.