Take Your Data to Greater Heights

SoCal CompBio integrates big data analytics, technical expertise, and extensive experience—built upon a robust biological understanding—to assist you in transforming data into valuable insights. While anyone can process your data through a pipeline, what you truly require is a collaborative partner who comprehends your objectives and guides you toward achieving them.

Services

SoCal CompBio provides consulting to the life sciences industry with bioinformatics, computational biology and machine learning. John Whitaker of SoCal CompBio has previous experience with:

Biomarker discovery from clinical trials

Identifying a biomarker, DGM4, that predicts response to an antidepressant from retrospective analysis of samples that remained from two clinical trials. John Whitaker of SoCal CompBio is first author on the discoveries patent. DGM4 was validated in a independent clinical trial.

Diagnostic test and risk score development

Developing a noninvasive skin cancer risk assessment that combined measurements of cancer related mutations from healthy appearing skin with other risk factors, to calculator skin cancer risk. John Whitaker is first listed author on the discoveries patent and last listed author on the peer-reviewed publication.

Epigenetics and epigenomics

Worked on the Roadmap Epigenome Project to produce the first reference map of the human epigenome. Working at UCSD and Johnson & Johnson to use epigenomics to identify rheumatoid arthritis biomarkers and potential drug targets.

Select publications and patents

John Whitaker and SoCal CompBio have co-authored numerous research articles and patents:

Precision medicine for psychiatry

A biomarker, DGM4, that predicts response to the developmental antidepressant, Liafensine. DGM4 is a single nucleotide polymorphisms (SNP) that was discovered in a respective analysis of blood samples that remind from a previous clinical trial. The effectiveness of DGM4 was confirmed in an independent cohort during a new phase II study.

Skin cancer risk

A noninvasive skin cancer risk assessment test that combined measurements of cancer related mutations from healthy-appearing sun exposed skin with other risk factors, to calculate skin cancer risk.

Mapping epigenomes

Worked as an integrative analysis lead during the Roadmap Epigenome project. Developed machine learning methods; for example, to predict epigenomic modification from DNA sequence. While at Johnson & Johnson applied similar approaches to drug target identification for rheumatoid arthritis.

For a complete list of publications, patents and career history please see Google Scholar and Linkedin.