Gradient BioResearch is a multi-area research platform spanning computational biology, cardiology, neurology, pathology, oncology, and immunology: with guided workflows, reproducible pipelines and analysis in a single application.
Six domains, one shared platform
Single-cell, bulk RNA-seq, spatial transcriptomics, and biomarker discovery in guided study workspaces.
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ECG, RR, blood pressure, and PPG recordings with preprocessing, HRV, cohort comparison, and interpretation.
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Single-cell and multi-modal immune studies with annotation, cell-state scoring, repertoire analysis, signaling networks, and disease cohort comparison.
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Connectivity matrices, EEG, and fMRI with graph metrics, network visualization, NBS cohort analysis, and reporting.
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Multi-omic oncology studies with TME profiling, cell-cell communication, immuno-oncology, mutation landscape, survival analysis, and analysis.
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Whole-slide and region images with tiling, tissue detection, cell segmentation, and spatial quantification.
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Shared infrastructure for every research area
Users, organizations, datasets, jobs, and provenance live in one application while each research area keeps its own workflow.
Generate summaries from structured analysis outputs, with claims tied back to recorded metrics, genes, pathways, or run artifacts.
Every run captures parameters, pipeline versions, status, and artifacts so analyses can be reviewed, repeated, and audited.
Move from datasets to UMAPs, brain networks, slide viewers, differential expression, waveforms, HRV metrics, and cohort comparisons.
Computational biology, cardiology, neurology, pathology, oncology, and immunology each have dedicated dataset types, pipeline workflows, and analysis views: all under one shared organization and user model.
Organize datasets, metadata, subjects, design variables, and analysis runs under a shared organization/user model.
Launch compute jobs for QC, clustering, differential expression, graph metrics, EEG preprocessing, HRV, tissue segmentation, spatial quantification, or cohort summaries.
Review study dashboards, UMAPs, brain networks, slide viewers, waveforms, run artifacts, and statistical comparisons in the browser.
Use grounded AI summaries and publication-oriented outputs that cite recorded results instead of inventing analysis claims.