ArticleScientific reports2021
Verifying explainability of a deep learning tissue classifier trained on RNA-seq data.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Consistency of feature attribution in deep learning architectures for multi-omics.Scientific reports · 2026Article
- Artificial intelligence-based prognostic models in acute myeloid leukemia: systematic review and meta-analysis.Blood neoplasia · 2026Article
- Explainable deep learning and radiomics integration for differentiating insulinomas from NF-PNETs in EUS imaging.BMC medical informatics and decision making · 2026Article
- Perioperative machine learning models with SHAP interpretation for predicting adverse outcomes in breast cancer surgery.Frontiers in surgery · 2026Article
- Integrative multi-cohort analysis of DNA methylation profiles for pancreatic ductal adenocarcinoma biomarker discovery and prognosis.Frontiers in bioinformatics · 2026Article
- Explainable machine learning identifies immune-inflammatory biomarkers and therapeutic candidates in drug-resistant epilepsy.Scientific reports · 2025Article
- Spatial Modularity of Innate Immune Networks Across Bactrian Camel Tissues.Animals : an open access journal from MDPI · 2025Article
- The Breast Cancer Classifier refines molecular breast cancer classification to delineate the HER2-low subtype.NPJ breast cancer · 2025Article
- An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.Scientific reports · 2025Article
- Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression.International journal of molecular sciences · 2024Article
- Disease progression associated cytokines in COVID-19 patients with deteriorating and recovering health conditions.Scientific reports · 2024Article
- Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington's disease using single-cell RNA sequencing data.BMC genomics · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Deep learning for predicting 16S rRNA gene copy number.Scientific reports · 2024Article
- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
- Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures.Nature communications · 2023Article
- Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology.Scientific reports · 2023Article
- Big Data in Gastroenterology Research.International journal of molecular sciences · 2023Review
- Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures andBioinformatics advances · 2023Article
- Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest.Frontiers in genetics · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions. Explainability models allow transparency of ML algorithms, however their reliability within high-dimensional data is unclear. To test the reliability of the explainability model SHapley Additive exPlanations (SHAP), we developed a convolutional neural network to predict tissue classification from Genotype-Tissue Expression (GTEx) RNA-seq data representing 16,651 samples from 47 tissues. Our classifier achieved an average F1 score of 96.1% on held-out GTEx samples. Using SHAP values, we identified the 2423 most discriminatory genes, of which 98.6% were also identified by differential expression analysis across all tissues. The SHAP genes reflected expected biological processes involved in tissue differentiation and function. Moreover, SHAP genes clustered tissue types with superior performance when compared to all genes, genes detected by differential expression analysis, or random genes. We demonstrate the utility and reliability of SHAP to explain a deep learning model and highlight the strengths of applying ML to transcriptome data.
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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.