Explainable AI for biomarker discovery in 2026: overview
Explainable AI for biomarker discovery uses interpretable models or explanation methods to identify which measurements influence a prediction and how they relate to a disease or outcome. It can help researchers assess candidate biomarkers across clinical, molecular and imaging data, while making model behavior easier to examine. But an explanation is not proof that a feature is biologically causal or clinically useful; independent validation and clear links to underlying mechanisms remain important.
Recent work is widening the evidence base beyond single data types. In September 2026, Nanjing Medical University reported an interpretable model and diagnostic nomogram for hypo-productive thrombocytopenia; in August, Inonu University researchers applied explainable machine learning to routine blood biomarkers for ovarian tumor discrimination. July 2026 studies explored fecal extracellular-vesicle metabolomics for colorectal cancer and routine laboratory biomarkers for metabolic liver disease. Across these examples, researchers are combining multi-omics and clinical measurements with feature explanations, while causal gene prioritization and pathway interpretation offer routes from predictive signals toward biological hypotheses.
- 1Attribution remains central: Post-hoc feature attribution is the largest approach by document count, but explaining a model’s prediction does not establish that a biomarker causes disease.
- 2Mechanisms gain attention: Causal and counterfactual methods grew 200% over the previous 12 months, reflecting interest in moving from predictive associations toward testable biological hypotheses.
- 3Clinical data stays practical: Recent studies use routine blood and laboratory biomarkers alongside omics, offering a path to interpretable models that may be easier to evaluate in clinical settings.
Top organizations working on explainable AI for biomarker discovery
Ranked by Linknovate activity score · last 24 monthsThe activity score adds up the relevance of every patent, publication, grant and news item linking an organization to explainable AI for biomarker discovery. Showing 7 of 1,865 organizations.
| # | Organization | Approach | Signals (24 mo) | Momentum | Why it matters |
|---|---|---|---|---|---|
| 1 | 
Capital Medical University
Research · China |
Network and pathway interpretation | PUB 74 · NEWS 2 | +200% | Recently, researchers used interpretable machine learning and epidemiological modeling to examine relationships between the triglyceride–glucose index and hyperhomocysteinemia within a cardiovascular–kidney–metabolic biomarker network. |
| 2 | 
Inonu University
Research · Turkey |
Post-hoc feature attribution | PUB 26 | +125% | Recently, researchers applied explainable machine learning to biomarker-based disease classification, including metabolomic and blood-marker models for ovarian and pancreatic cancer. |
| 3 | 
Vellore Institute of Technology
Research · India |
Post-hoc feature attribution | PUB 38 | +222% | Recently, researchers combined interpretable AI with multi-omics to investigate determinants of cancer drug response. Their work also applies explainable methods to transcriptomic biomarker identification. |
| 4 | 
King Khalid University
Research · Saudi Arabia |
Post-hoc feature attribution | PUB 28 | +150% | Recently, researchers applied consensus-based explainable AI to classify pancreatic cancer molecular subtypes. Their work also uses interpretable machine learning with gene-expression and metabolomic data to identify cancer biomarkers. |
| 5 | 
Southern Medical University
Research · China |
Post-hoc feature attribution | PUB 48 | +236% | Recently, researchers used explainable machine learning to identify lipid-derived biomarkers associated with abdominal obesity, alongside interpretable models for metabolic liver disease. |
| 6 | 
Princess Nora Bint Abdulrahman University
Research · Saudi Arabia |
Post-hoc feature attribution | PUB 25 | +425% | Researchers apply feature selection and SHAP-based interpretation to metabolomic and extracellular-vesicle profiles for cancer diagnosis, including breast and colorectal cancer. |
| 7 | 
Fudan University
Research · China |
Post-hoc feature attribution | PUB 54 | +215% | Recently, researchers integrated exposure data and multi-omics with SHAP interpretation to identify immune alterations and candidate biomarkers in COPD–lung cancer comorbidity. |
| 8 | ··Hidden organizationCompany | Approach | PAT 00 · PUB 00 GRANTS 0 · NEWS 00 | +00% | Available to registered users. |
| 9 | ··Hidden organizationCompany | Approach | PAT 00 · PUB 00 GRANTS 0 · NEWS 00 | +00% | Available to registered users. |
| 10 | ··Hidden organizationCompany | Approach | PAT 00 · PUB 00 GRANTS 0 · NEWS 00 | +00% | Available to registered users. |
Startups to watch and leading research centers
Startups to watch 13 total
InfervisionChinaRecently, researchers used an interpretable machine-learning model combining delta-radiomics to predict postoperative recurrence in early-stage lung adenocarcinoma.- ··Hidden startupCountryAvailable to registered users, with funding and founding year.
- ··Hidden startupCountryAvailable to registered users, with funding and founding year.
Leading research institutions 1,708 total
- Inonu UniversityTurkeyRecently, researchers applied explainable machine learning to biomarker-based disease classification, including metabolomic and blood-marker models for ovarian and pancreatic cancer.
- Capital Medical UniversityChinaRecently, researchers used interpretable machine learning and epidemiological modeling to examine relationships between the triglyceride–glucose index and hyperhomocysteinemia within a cardiovascular–kidney–metabolic biomarker network.
- Vellore Institute of TechnologyIndiaRecently, researchers combined interpretable AI with multi-omics to investigate determinants of cancer drug response. Their work also applies explainable methods to transcriptomic biomarker identification.
- ··Hidden research institutionCountryAvailable to registered users, with their key researchers.
- ··Hidden research institutionCountryAvailable to registered users, with their key researchers.
Where explainable AI for biomarker discovery innovation happens
Share of documents with at least one organization from each country.
Open the interactive map with all 1,865 organizationsMain technical approaches
Last 24 months| Approach | What it is | Documents | Leading organizations |
|---|---|---|---|
| Post-hoc feature attribution | Post-hoc methods explain a trained model’s predictions through feature contributions, but explanations can be unstable and do not establish biological causality. | 917 |
Inonu University, Princess Nora Bint Abdulrahman University, Malatya Turgut ozal University |
| Intrinsically interpretable models | These models make their decision logic readable by design, trading some modeling flexibility for explanations that are more directly tied to predictions. | 252 |
Manipal Academy of Higher Education, Chongqing Medical University, The First Affiliated Hospital Of Xian Medical University |
| Causal and counterfactual methods | Causal and counterfactual methods probe relationships or alternative outcomes, but their conclusions depend on assumptions and suitable data. | 116 |
ChinaMedical University, Capital Medical University, Taiyuan University of Technology |
| Explainable deep representations | These methods expose patterns learned from complex data such as images or omics, while explanations can remain difficult to validate biologically. | 137 |
Stanford University, Vellore Institute of Technology, Urgench State University |
| Network and pathway interpretation | Network and pathway methods place candidate biomarkers in biological context, but results depend on the completeness and quality of reference knowledge. | 279 |
King Khalid University, Carnegie Mellon University, Mudanya University |
Emerging trends
Last 12 months vs previous 36 monthsTerms whose frequency in the last 12 months grew the most compared with the previous 36 months.
Funding and investment
Recent funding rounds
Funding data lists 14 rounds totaling $1.54 billion and 17 grants totaling $10.1 million since 2020. These are broader landscape signals and are not necessarily dedicated solely to explainable AI for biomarker discovery.
Patent landscape
Top patent holders · last 24 monthsRecent developments
Latest news, duplicates removed- Sep 2026Magnetic resonance imaging (MRI) has become an essential tool for investigating the complex pathophysiological mechanisms underlying neuropsychiatric and neurological disorders. In contrast to conventional approaches…Frontiers in Neuroscience | New and Recent Articles
- Aug 2026Artificial intelligence (AI) is being increasingly integrated into cognitive and computational neuroscience to support the preprocessing, analysis, and interpretation of multimodal data, including electroencephalography…Frontiers in Neuroscience | New and Recent Articles
- Aug 2026Abstract Purpose: Machine learning to enable precise, non-invasive detection of cerebral amyloid-beta (Aβ) pathology by integrating cognitive assessments, plasma biomarkers, and structural neuroimaging. Methods: We…Frontiers in Neuroscience | New and Recent Articles
- Aug 2026Artificial intelligence is reshaping modern healthcare, yet one of its biggest challenges remains trust. While deep learning models continue to achieve remarkable predictive performance, many operate as “black boxes,”…TechBullion
- Month 20XXHidden developmentAvailable to registered users with alerts enabled.
Frequently asked questions about explainable AI for biomarker discovery
Which universities and research centers lead explainable AI for biomarker discovery?
Capital Medical University leads the listed research organizations with 76 documents in the 24-month window. Other active groups include Fudan University, with 54, and Southern Medical University, with 48. Their work spans biomarker networks, multi-omics, cancer, metabolic disease and clinical prediction; document activity alone does not measure research quality or clinical adoption.
What are the latest developments in explainable AI for biomarker discovery?
In September 2026, Nanjing Medical University reported an interpretable model for biomarker identification and diagnostic nomogram development in hypo-productive thrombocytopenia. In August 2026, Inonu University researchers applied explainable machine learning to routine blood biomarkers for distinguishing benign from malignant ovarian tumors. July 2026 work also examined fecal extracellular-vesicle metabolomics in colorectal cancer and laboratory biomarkers for metabolic liver disease.
What new methods are emerging in explainable AI for biomarker discovery?
Five approaches are prominent: post-hoc feature attribution, intrinsically interpretable models, causal and counterfactual methods, explainable deep representations, and network and pathway interpretation. Causal and counterfactual methods grew 200% over the previous 12 months, while network and pathway interpretation grew 157.7%. Recent studies also combine multi-omics or routine clinical measurements with model explanations.
Who holds the most explainable AI for biomarker discovery patents?
University of Heidelberg and Deutsches Krebsforschungszentrum Stiftung Des Oeffentlichen Rechts each hold 2 patents in the listed holder data. University of Mons, Randox Laboratories Ltd, Warsaw Orthopedic Inc., BenevolentAI, Prenosis Inc. and Toi Labs each hold 1. The counts identify recorded holders, not the commercial value or legal strength of their patents.
Is explainable AI for biomarker discovery patent activity growing?
No: the 24-month window records 9 patents, and patent activity declined 20% in the last 12 months compared with the previous 12 months. That contrasts with publication growth: the field recorded 1,411 publications last year, up 261.8%. The difference points to research activity outpacing patenting in the available data.
Which countries lead explainable AI for biomarker discovery?
China leads with 2,051 documents, followed by the United States with 1,210 and India with 675. The United Kingdom has 392, while Germany has 296. These figures indicate where recorded activity is concentrated; they do not show the quality, clinical translation or commercial value of work in each country.
What are the applications of explainable AI for biomarker discovery?
Neurological and psychiatric disorders lead the listed applications with 756 documents, followed by treatment response and patient stratification with 513, and cancer and oncology with 336. Other areas include cardiometabolic disease, with 313 documents, and infectious and immune diseases, with 152. Examples include cancer subtyping, disease risk prediction and treatment-response analysis.
How much funding has explainable AI for biomarker discovery received?
The funding data lists 14 rounds totaling $1,541,861,607 and 17 grants totaling $10,059,859 since 2020. These figures are associated with the broader landscape and should not be read as funding exclusively for explainable AI biomarker discovery. The listed rounds include grants and other financing types, so they are not all venture investment.
How mature is explainable AI for biomarker discovery?
The field remains research-heavy: it recorded 1,411 publications last year, while the 24-month window contains 9 patents and the dataset lists 32 clinical trials overall. This signals strong research activity alongside comparatively limited patenting. The figures do not establish that specific biomarkers or models have achieved clinical validation or routine use.