Ref A1
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Disease target mining answers the question that precedes every screening campaign: which gene or protein is worth designing against at all. We integrate public and client-owned omics layers — bulk and single-cell transcriptomics, genomic association data, proteomics and clinical annotation — and score candidate genes on disease association, directionality of effect, tissue specificity, network position and tractability. The output is a ranked, evidence-linked target list in which every candidate carries the data that put it there, so your team can argue the case internally rather than accept a score.
Key Features & Advantages
Evidence Per Candidate, Not Just a Rank
Each prioritised gene is delivered with the differential expression, association statistics, network metrics and literature support behind it, so the ranking can be audited and challenged.
Causality Weighted Above Correlation
Where suitable GWAS and eQTL resources exist, we add Mendelian randomisation and colocalisation evidence to separate genes that merely track disease from genes plausibly driving it.
Tractability Assessed Early
Candidates are annotated with modality fit — small molecule, antibody, degrader, peptide — using structural availability, subcellular localisation and known ligandability, so intractable hits are flagged before they consume budget.
Reproducible Pipelines
Dataset versions, filtering thresholds and scoring weights are recorded and shipped with the report; the ranking can be regenerated when new cohorts or internal data arrive.
Where This Service Is Used
1Building a first target list for a new disease programme where no in-house candidate exists
2Independently testing a target hypothesis the team already favours, including the evidence against it
3Finding resistance-associated or escape targets from post-treatment patient cohorts
4Identifying subtype-specific targets where a therapy works in only part of the patient population
5Repositioning: locating an indication in which the target of an existing asset is more strongly implicated
Methods and Techniques Applied
1Curation and harmonisation of public cohorts (TCGA, GEO, GTEx, DepMap, biobank summary statistics) alongside client data
2Differential expression and pathway enrichment across tumour/normal, responder/non-responder or treated/untreated contrasts
3GWAS and eQTL integration, colocalisation and Mendelian randomisation where instruments are available
4Protein interaction network construction with hub, bottleneck and module analysis to capture network-level importance
5Dependency and essentiality screening data to check whether loss of the gene actually affects disease-relevant cells
6Composite scoring across disease association, causality, specificity, safety liability and tractability, with weights agreed with you
What We Need From You
1The disease, subtype, patient population and the therapeutic modality you intend to use
2Any internal omics data, screening data or clinical cohorts you wish included (count matrices, VCFs, proteomics tables)
3Targets already committed or already excluded, with the reason, so effort is not duplicated
4Constraints that matter to the decision: tissue of interest, safety concerns, competitive landscape
What You Receive
1Ranked target table with per-candidate scores and the underlying evidence for each scoring dimension
2Two-page dossier for each top candidate: biology, disease evidence, expression profile, tractability, known liabilities
3Figures ready for internal review — volcano plots, expression atlases, network maps, enrichment summaries
4Processed data tables and the analysis scripts with dataset versions and parameters recorded
5Explicit statement of which candidates are weakly supported and which evidence gaps a follow-up experiment would close
What We Compute and Report
1Differential expression per gene with effect size, adjusted significance and the cohorts contributing
2Composite target score across disease association, causal evidence, tissue specificity and tractability
3Network position metrics: connectivity, betweenness and module membership within the disease network
4Causal effect estimates with sensitivity statistics where genetic instruments are available
5Expression profile across normal tissues, used to estimate on-target safety exposure
Questions We Are Asked Before Scoping
Can you work with only public data?
▸Yes. Many programmes start with public cohorts alone. Adding internal data usually sharpens the ranking, and we will say in the feasibility review how much difference it is likely to make.
Does a high score mean the target is validated?
▸No. The ranking prioritises hypotheses for experimental testing. Validation requires perturbation experiments in a disease-relevant system, which we help design but do not perform.
What if the evidence contradicts our favoured target?
▸We report it. Negative and inconclusive findings are delivered as plainly as supportive ones, with the specific datasets and statistics that produced them.
Interpretation and Limitations
Computational predictions prioritise hypotheses; they do not independently establish binding, biological activity, therapeutic efficacy, safety or clinical validity. Performance depends on input quality, target class, the structural evidence available and the applicability domain of the selected methods. Scores used for ranking are comparative devices and should not be read as measured quantities. All deliverables are for research use only and are not intended for diagnostic or therapeutic use.
Is This the Right Service for You?
Choose this when
No target is committed yet and the programme needs a defensible shortlist rather than a hunch
Internal omics or clinical data exists but has never been integrated with public evidence
A target must be justified to a board, a partner or an investor with the evidence attached
Consider another service when
The target is already chosen and the question is whether it can be drugged
Binding Site & Druggability Assessment The question is about a specific dataset rather than target selection
Bulk & Single-Cell RNA-Seq Analysis What Goes Wrong, and How We Handle It
These are the failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.
Expression differences that reflect tissue composition, not disease biology
Deconvolution and cell-type-aware analysis separate a genuine expression change from a shift in which cells are present.
Highly connected hub genes scoring well simply because they are well studied
Literature-derived edges are down-weighted and network metrics are computed on confidence-filtered interactions.
A candidate that is compelling biologically and impossible to drug
Tractability for your intended modality is scored alongside disease association, not added afterwards.
A Worked Example
Ranking targets for an immuno-oncology programme
The situation
A team had responder and non-responder biopsies from a checkpoint inhibitor trial, plus no committed target.
What the work involved
Public tumour cohorts were harmonised with the internal samples, differential expression and pathway signal were established across responder status, dependency data was layered in, and candidates were scored on causality, tumour specificity, normal-tissue exposure and antibody tractability.
What came back
Eighteen ranked candidates, each with a two-page dossier. Four were already in competitor pipelines and were flagged as such; three had no antibody-accessible epitope and were marked as unsuitable for the intended modality.
Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.
What Published Work Reports
The results below were published by independent groups and are cited to show what these computational approaches have achieved in the literature. They are not ICDMO project results, and outcomes vary substantially between targets, datasets and programmes. We do not promise comparable results for any particular project.
Around three quarters of predicted ligand poses fall within 2 Å
Current deep-learning structure prediction, evaluated on an independent protein–ligand benchmark, placed roughly 76% of ligands within 2 Å of the experimental pose, and reported approximately 80% by coordinate error — a substantial improvement over classical docking baselines on the same set, achieved without supplying a binding site. The corollary matters as much as the headline: roughly a quarter of cases still fall outside that threshold, which is why we report confidence per interface rather than delivering a single model.
Abramson et al., Nature, 2024 ↗Service Workflow
1
Scientific Consultation
We discuss the objective, the decision it supports and the data you hold. Nothing is quoted before we understand what the result has to answer.
2
Feasibility Review
We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.
3
Technical Proposal
Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.
4
Computation and QC
The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.
5
Report and Follow-Up
You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.
Service Process
1
Online Consultation
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2
Solution Matching
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Service Contract
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Project Execution
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Data Delivery
Specifications & Turnaround
Note: ICDMO delivers computational (in silico) services. Results prioritise hypotheses for testing; they do not by themselves establish binding, biological activity, efficacy or safety. All deliverables are for research use only and are not intended for diagnostic or therapeutic use. Downstream experimental work is scoped separately and executed through qualified partners.
Get a Custom Quote
Our scientific team responds within 24 hours with a detailed technical proposal and pricing tailored to your project.
Contact Us Online ConsultationStandard Deliverables
✓Sequence-verified final product
✓Full QC documentation
✓Certificate of Analysis (CoA)
✓Electronic data files
✓Project completion report
Why ICDMO?
Computational CDMO Focus
Reproducible, Versioned Pipelines
Confidence & Limitations Reported
Experiment-Aware Recommendations
Response Within One Business Day