proteomics made accessible

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Advanced Proteomic Search Engine

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SoCal Bioinformatics Inc.

Advanced data analysis, mining and biomarker discovery solutions for research and clinical development.



The Proteomics API

Turning LCMS data into biological insight is essentially a well practiced operation, however inconsistent. Several tools exist to extract molecular information and re-construct protein sequences, but the processes vary from tool to tool. We provide a concise method for translating label-free proteomics into biological meaning which can be easily analyzed, searched and explored.



Consult with SoCAL Bioinformatics

Experiment Design

From patient stratification to laboratory work flow, SCBI has helped clients develop processes to ensure quality data is generated from the beginning.

Data Analysis

With existing data SCBI can apply proprietary tools to reduce variability, expand biological coverage and filter outlier data prior to statistical analysis.

Marker Discovery

Using advanced machine learning techniques and custom discovery pipelines, SCBI can find the signal hidden among the biological noise.



MACHINE LEARNING

Molecular signatures have the power to assess one’s current biological state, unlike genomics which can only provide a static snapshot of what could be - butterfly or caterpillar. We apply machine learning solutions to large biological datasets to discover relevant markers for the current biological state.




Covering proteomics A-to-Z with a whole suite of solutions, not just a single piece in the puzzle.

SoCal Bioinformatics Inc.

Recent Articles


From Concept to Biomarkers: An MRM Approach to Clinical Cancer Marker Discovery
2018-05-31

Efforts in discovering biomarkers from liquid biopsies with sufficient performance to be clinically useful remain a significant challenge in diagnostics. However, as detailed in this example, researchers can leverage highly multiplexed approaches to simultaneously measure a multitude of proteins.

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Predictive Modeling Pitfalls: Information Leakage Can Generate Models that Fail to Validate with New Data
2018-05-30

Building predictive models that successfully generalize to new data is a challenging process full of potential pitfalls. During the modeling building process, cross-validation is routinely used to optimize parameters, and importantly, provide an independent assessment of trained model performance using the hold-out test set partitions.

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