QUESTION & SCOPE
Target Hypothesis and Review Protocol
Define target identity, disease ontology, mechanism, population/model, evidence windows, inclusion/exclusion criteria, databases, search dates and decision thresholds before retrieval.
Target Validation Based Literature and Database Mining
Build a traceable target evidence dossier from peer-reviewed literature, curated databases and project data—while preserving source context, contradictions and uncertainty.
Review Your TargetOVERVIEW
Validation begins with an answerable evidence question
This service evaluates whether published and database evidence supports a defined target–disease–mechanism hypothesis. We search broadly, normalize target and disease concepts, extract evidence statements, verify them in source context, grade study relevance and integrate independent evidence types into a decision-oriented dossier.
The work is distinct from target discovery: it begins with one or more nominated targets and tests prespecified questions such as disease association, direction of effect, tissue/cell context, perturbation phenotype, pathway position, pharmacological precedent and safety. Literature frequency is not treated as biological validity, and automated relation extraction is never accepted without source-level review.
Projects can stand alone or follow Target Discovery from Omics Data and precede modality-specific Target Druggability Assessment.
CORE SERVICES
Evidence work packages from search to adjudication
RETRIEVAL
Systematic Literature and Database Search
Combine controlled vocabulary, aliases, semantic/entity searches and citation chaining across PubMed/PMC, Europe PMC and fit-for-purpose genetics, expression, interaction, pathway, perturbation, drug and safety resources.
NORMALIZATION
Entity Resolution and Source Harmonization
Map gene/protein, variant, disease, drug and model-organism terms to stable identifiers; reconcile isoforms, obsolete names, duplicated records and database version differences.
CURATION
Evidence Extraction and Grading
Capture study design, sample/model, intervention, comparator, endpoint, effect direction, magnitude, uncertainty, assay context and direct supporting passage; grade independence, relevance and methodological strength.
SYNTHESIS
Concordance, Contradiction and Gap Analysis
Separate independent replication from repeated database ingestion, compare supporting and opposing evidence, identify species/context mismatches and document evidence that is missing—not merely negative.
DECISION
Target Evidence Dossier and Validation Roadmap
Translate curated evidence into claim-level conclusions, confidence statements, open questions and orthogonal experiments that can confirm direction, engagement, phenotype or safety.
TARGET EVIDENCE MATRIX
Each evidence type answers a different question
Human Genetics
Does inherited or somatic variation implicate the target, and is the causal gene assignment and direction credible?
Expression & Localization
Is the target present in relevant tissues, cell types and disease states, with appropriate controls and batch-aware analysis?
Perturbation Evidence
Do genetic or pharmacological interventions change a disease-relevant phenotype, with specificity and rescue controls?
Pathway & Interaction
Is the target positioned in a plausible mechanism, and is the relation directly measured or computationally inferred?
Drug & Chemical Evidence
Are there selective modulators, target-engagement data and interpretable activity relationships in relevant systems?
Safety & Essentiality
Do human tolerance, tissue expression, knockout phenotypes or class effects indicate an on-target liability?
INTEGRATED WORKFLOW
Six stages with a reproducible audit trail
| Stage | Key Activities | Decision Output |
|---|---|---|
| 1. Scope & Protocol | Resolve identifiers; define PICO/PECO-style question, evidence domains, date window, species and inclusion rules. | Search protocol and target validation claims to test. |
| 2. Search & Capture | Run versioned queries across literature and databases; record search date, query, result counts and source snapshots. | Deduplicated candidate evidence library. |
| 3. Screening & Normalization | Screen titles/abstracts/full text; normalize entities and units; link duplicated or derivative database records. | Included evidence set with exclusion reasons. |
| 4. Extraction & Quality Review | Extract design, sample/model, assay, effect direction/size, uncertainty and source passage; assess bias and relevance. | Claim-level evidence table and quality grade. |
| 5. Synthesis & Adjudication | Group independent evidence, compare contexts, map contradictions and avoid treating citation count as replication. | Evidence matrix, confidence rationale and gaps. |
| 6. Reporting & Update Plan | Prepare dossier, references, database versions, machine-readable tables and prioritized experimental validation. | Decision-ready report and refreshable evidence baseline. |
Statistics and AI: Where quantitative results are comparable, effect sizes and uncertainty are retained rather than reduced to significance labels; multiplicity and model covariates are recorded. Publication bias, selective reporting, duplicated cohorts and database circularity are assessed qualitatively or quantitatively when feasible. NLP/LLM-assisted screening and relation extraction are evaluated on a manually reviewed sample. Entity normalization, prompt/model versions and confidence thresholds are recorded; protected test sets, cross-corpus evaluation and external validation are preferred. Automated output outside the represented entity, relation, language or document domain is flagged for full manual review.
DELIVERABLES
Six connected outputs, not a bibliography dump
Search Protocol & Query Log
Databases, aliases, controlled terms, date ranges, filters, complete queries and search dates.
Screened Evidence Library
Deduplicated citations with inclusion/exclusion status, document links and source provenance.
Normalized Evidence Table
Stable entity IDs, study context, assay, comparator, effect direction, magnitude and supporting passage.
Target Evidence Matrix
Genetic, expression, perturbation, pathway, pharmacology and safety evidence with independence flags.
Contradiction & Gap Map
Opposing findings, context dependencies, weak links, missing experiments and unresolved nomenclature.
Validation Dossier & Roadmap
Claim-level conclusions, confidence rationale, references, database versions and ranked next experiments.
SCIENTIFIC EVIDENCE
Automated retrieval expands coverage; curation protects meaning
PubTator 3.0 illustrates a modern literature-mining pipeline that combines named-entity recognition, identifier mapping, relation extraction and indexed retrieval. Its published benchmark also shows that performance differs across entity types and relation tasks, so automated annotations require task-specific review.1
Open Targets literature evidence demonstrates how text-mined target–disease relations can complement genetics, expression, drug and model-organism evidence, but also highlights the need for entity disambiguation and source-level provenance.2 LitVar 2.0 further shows the value of variant normalization and full-text/supplement retrieval for evidence discovery.3
1 Wei, C.-H.; et al. PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge. Nucleic Acids Research 2024, 52, W540–W546. https://doi.org/10.1093/nar/gkae235. Open Access under CC BY 4.0.
2 Kafkas, Ş.; Dunham, I.; McEntyre, J. Literature evidence in open targets—a target validation platform. Journal of Biomedical Semantics 2017, 8, 20. https://doi.org/10.1186/s13326-017-0131-3. Open Access under CC BY 4.0.
3 Allot, A.; et al. Tracking genetic variants in the biomedical literature using LitVar 2.0. Nature Genetics 2023, 55, 901–903. https://doi.org/10.1038/s41588-023-01414-x. Open Access under CC BY 4.0.
PROJECT STRATEGY
Evidence architecture built for scientific decisions
Claim-first, source-linked
Every conclusion is decomposed into a specific claim and linked to the paper passage, database record, model, assay and date that support or challenge it.
Independence over volume
Ten databases ingesting one publication do not equal ten replications. We track cohort, experiment and source dependencies before scoring convergence.
Context before consensus
Species, tissue, cell state, disease stage, intervention direction and endpoint determine whether apparently conflicting findings are truly inconsistent.
Refreshable by design
Versioned queries, stable identifiers and machine-readable evidence tables allow the dossier to be updated as new literature and database releases appear.
This service validates the strength and consistency of available evidence; it does not convert association into causality or a nominated target into a proven therapy. High-value hypotheses should be tested with independent datasets, orthogonal assays and disease-relevant perturbation experiments. To discuss your targets and evidence needs, please Contact Us or use the Online Inquiry below.
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