ArticleMethods in molecular biology (Clifton, N.J.)2021
Overcoming Interpretability in Deep Learning Cancer Classification.
Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Predicting the risk of type 2 diabetes mellitus (T2DM) emergence in 5 years using mammography images: a comparison study between radiomics and deep learning algorithm.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- An interpretable decision-support model for breast cancer diagnosis using histopathology images.Journal of pathology informatics · 2023Article
- Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images.Frontiers in chemistry · 2023Article
- Computational pathology in ovarian cancer.Frontiers in oncology · 2022Review
- Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.Disease markers · 2022Article
- Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment.Cancers · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since its inception, deep learning has revolutionized the field of machine learning and data-driven science. One such data-driven science to be transformed by deep learning is genomics. In the past decade, numerous genomics studies have adopted deep learning and its applications range from predicting regulatory elements to cancer classification. Despite its dominating efficacy in these applications, deep learning is not without drawbacks. A prominent shortcoming of deep learning is the lack of interpretability. Hence, the main objective of this study is to address this obstacle in the deep learning cancer classification. Here we adopt a feature importance scoring methodology (Gradient-based class activation mapping or Grad-CAM) on a quasi-recurrent neural network model that classify cancer based on FASTA sequencing data. In this study, we managed to formulate a nucleotide-to-genomic-region Grad-CAM scoring methodology, as well as, validate the use this methodology for the chosen model. Consequently, this allows for the utilization of the Grad-CAM scoring methodology for feature importance in deep learning cancer classification. The results from our study identify potential novel candidate genes, genomic elements, and mechanisms for future cancer research.
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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.