The Platform
How Genomarker works: unified multi-omics data management, exploration, enrichment, and prediction — machine learning guided by biological knowledge.
Genomarker organizes the full arc of a biomarker study — data management, exploration, enrichment, and prediction — into one interactive platform, instead of a fragile chain of disconnected scripts and tools.
Data management
Multi-omics studies produce heterogeneous data at every step: raw assays, processed matrices, sample metadata, clinical annotations. Genomarker provides interactive data management across genomics, transcriptomics, proteomics, and metabolomics, keeping these layers linked so that downstream analysis always works on a coherent, traceable dataset.
Exploration
Before any model is fit, the data has to be understood. Genomarker’s interactive exploration lets researchers move across omics layers — from cohort-level summaries down to individual features and samples — so outliers, batch effects, and promising signals surface early, when they are cheap to act on.
Enrichment
A list of significant features is not yet biology. Enrichment in Genomarker connects analytical results to structured biological knowledge, placing candidate markers in the context of genes, pathways, and known disease mechanisms. This is where statistical signal is separated from biological signal — and where irreproducible candidates tend to fall away.
Prediction
Genomarker builds predictive models over the managed, explored, and enriched data: classifiers and scores intended to survive validation, not just to fit the discovery cohort. Because every step upstream is part of the same platform, a prediction carries its provenance with it — which data, which processing, which evidence.
Machine learning, guided by biology
The platform’s premise is that machine learning on omics data works best when it is constrained by what biology already knows. Genomarker combines ML with structured biological knowledge throughout the pipeline, which is how it addresses the biomarker reproducibility problem while analyzing all omics types — genomics, transcriptomics, proteomics, and metabolomics — in one place.
Lineage: the Real Time Learning Machine
Genomarker descends from the Real Time Learning Machine (RTLM), the real-time machine-learning approach invented by Dr. Saed Sayad and described in his publications on real-time data mining. The same conviction — that learning systems should be interactive, incremental, and close to the data — runs through the platform’s design.
Request access to Genomarker
Genomarker access is currently provided on request. Tell us about your multi-omics study and we will follow up.