Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable AI
Chakraborty, D.; Gutierrez-Chakraborty, E. P.; Rodriguez-Aguayo, C.; Basagaoglu, H.; Lopez-Berestein, G.; Amero, P.
Show abstract
Explainable Artificial Intelligence (XAI) enables a holistic understanding of the complex and nonlinear relationships between genes and prognostic outcomes of cancer patients. In this study, we focus on a distinct aspect of XAI - to generate accurate and biologically relevant hypotheses and provide a shorter and more creative path to advance medical research. We present an XAI-driven approach to discover otherwise unknown genetic biomarkers as potential therapeutic targets in high-grade serous ovarian cancer, evidenced by the discovery of IL27RA, which leads to reduced peritoneal metastases when knocked down in tumor-carrying mice given IL27-siRNA-DOPC nanoparticles. SummaryExplainable Artificial Intelligence is amenable to generating biologically relevant testable hypotheses despite their limitations due to explanations originating from post hoc realizations.
Matching journals
The top 12 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates 92%
- Automated cell type annotation and exploration of single-cell signalling dynamics using mass cytometry 92%
- Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients. 92%
Similar papers in this journal
- Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition 94%
- Overcoming Resistance to BRAFV600E Inhibition in Melanoma by Deciphering and Targeting Personalized Protein Network Alterations 94%
- Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma 93%
Similar papers in this journal
- Leveraging sequences missing from the human genome to diagnose cancer 92%
- LUNAR: A Deep Learning Model to Predict Glioma Recurrence Using Integrated Genomic and Clinical Data 91%
- Deep plasma proteomics identifies and validates an eight-protein biomarker panel that separate benign from malignant tumors in ovarian cancer 91%
Similar papers in this journal
- Pancreatic cancer risk prediction using deep sequential modeling of longitudinal diagnostic and medication records 93%
- Deep Learning identifies new morphological patterns of Homologous Recombination Deficiency in luminal breast cancers from whole slide images. 93%
- Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides 91%
Similar papers in this journal
- Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology 94%
- Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines 93%
- Genomic hypomethylation in cell-free DNA predicts responses to checkpoint blockade in lung and breast cancer 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.