ArticleNature communications2021
Permutation-based identification of important biomarkers for complex diseases via machine learning models.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.
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Who cites it
71 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Support Vector Machine Models for Cancer Detection and Related Clinical Applications Using Omics and Omics-Adjacent Data: A Systematic Review.BioMed research international · 2026Pooled it
- Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis.Pediatric research · 2026Article
- Established machine learning matches tabular foundation models in clinical predictions.BMC medical informatics and decision making · 2026Article
- Multikingdom microbiome-based machine learning enables multiple sclerosis diagnosis.NPJ biofilms and microbiomes · 2026Article
- Plasma acylcarnitine dysregulation associated with CPT1-mediated metabolism contributes to oral carcinogenesis.Scientific reports · 2026Article
- Single-Particle Analysis of Cargo-Dependent Deformation in Lipid Nanoparticles Using Resistive Pulse Sensing: Implications for Formulation Optimization and Quality Control of mRNA Nanomedicine.ACS applied nano materials · 2026Article
- A deep joint-learning proteomics model for diagnosis of six conditions associated with dementia.Nature medicine · 2026Article
- Radiation Doses to Cardiac Substructures Predict Elevation in High-sensitivity Cardiac Troponin T Levels in Radiation Therapy for Lung Cancer.International journal of radiation oncology, biology, physics · 2026Article
- A transcriptomic dimension of neuronal and immune gene programs within the subgenual anterior cingulate cortex in schizophrenia.Translational psychiatry · 2026Article
- Review
- Predictive Biomarkers for Asymptomatic Adults: Opportunities, Risks, and Guidance for General Practice.Diagnostics (Basel, Switzerland) · 2026Review
- Defective efferocytosis links autonomic dysfunction and atrial fibrillation: multi-omics integration andFrontiers in immunology · 2026Article
- Integrating eye-tracking into psychometric research: computational approaches to explore response burden in questionnaire completion.Frontiers in psychology · 2026Article
- An interpretable deep learning framework for predictive modeling of postoperative infections in ICU patients.PloS one · 2026Article
- A postoperative recurrence prediction model for intrahepatic cholangiocarcinoma based on multi-omics analysis of adjacent-to-tumor tissues.Frontiers in oncology · 2026Article
- Systematic identification of pan-cancer single-gene expression biomarkers in drug high-throughput screens.PloS one · 2026Article
- Predicting Migratory Survival in a Songbird Hybrid Zone Using Machine Learning.Research square · 2025Article
- An Interpretable Deep Learning Framework for Biomarker Discovery in Complex Disease Survival Outcomes.bioRxiv : the preprint server for biology · 2025Article
- Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology.Biomimetics (Basel, Switzerland) · 2025Review
- Radiomics early assessment of post chemotherapy cardiotoxicity in cancer patients using 2D echocardiography imaging an interpretable machine learning study.Scientific reports · 2025Article
11 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine learning-based methods have been developed and widely used to alleviate some analytic challenges in complex human disease studies. While enjoying the modeling flexibility and robustness, these model frameworks suffer from non-transparency and difficulty in interpreting each individual feature due to their sophisticated algorithms. However, identifying important biomarkers is a critical pursuit towards assisting researchers to establish novel hypotheses regarding prevention, diagnosis and treatment of complex human diseases. Herein, we propose a Permutation-based Feature Importance Test (PermFIT) for estimating and testing the feature importance, and for assisting interpretation of individual feature in complex frameworks, including deep neural networks, random forests, and support vector machines. PermFIT (available at https://github.com/SkadiEye/deepTL ) is implemented in a computationally efficient manner, without model refitting. We conduct extensive numerical studies under various scenarios, and show that PermFIT not only yields valid statistical inference, but also improves the prediction accuracy of machine learning models. With the application to the Cancer Genome Atlas kidney tumor data and the HITChip atlas data, PermFIT demonstrates its practical usage in identifying important biomarkers and boosting model prediction performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.