دورية أكاديمية
CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.
| العنوان: | CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations. |
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| المؤلفون: | Schrod S; Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Niedersachsen, Germany., Zacharias HU; Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, 30625 Hannover, Germany., Beißbarth T; Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Niedersachsen, Germany., Hauschild AC; Department of Medical Informatics, University Medical Center Göttingen, 37075 Niedersachsen, Germany., Altenbuchinger M; Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Niedersachsen, Germany. |
| المصدر: | Bioinformatics (Oxford, England) [Bioinformatics] 2024 Jun 28; Vol. 40 (Suppl 1), pp. i91-i99. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Oxford : Oxford University Press, c1998- |
| مواضيع طبية MeSH: | Deep Learning* , Computer Simulation*, Humans ; Cell Line, Tumor ; High-Throughput Screening Assays/methods ; Neoplasms/metabolism ; Computational Biology/methods ; Software ; Antineoplastic Agents/pharmacology |
| مستخلص: | Motivation: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance. Results: We propose CODEX (COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations) as a general framework for the causal modeling of HTS data, linking perturbations to their downstream consequences. CODEX relies on a stringent causal modeling strategy based on counterfactual reasoning. As such, CODEX predicts drug-specific cellular responses, comprising cell survival and molecular alterations, and facilitates the in silico exploration of drug combinations. This is achieved for both bulk and single-cell HTS. We further show that CODEX provides a rationale to explore complex genetic modifications from CRISPR-interference in silico in single cells. Availability and Implementation: Our implementation of CODEX is publicly available at https://github.com/sschrod/CODEX. All data used in this article are publicly available. (© The Author(s) 2024. Published by Oxford University Press.) |
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| معلومات مُعتمدة: | 01ZX1912A German Federal Ministry of Education and Research; 01KD2209D BMBF; Deutsche Forschungsgemeinschaft; AL 2355/1-1 German Research Foundation; Digital Tissue Deconvolution-Aus Einzelzelldaten lernen; BMBF; 01KD2208A FAIrPaCT; 01KD2101C BMBF; 01KU1910A MATCH; 408885537 DFG TRR274 |
| المشرفين على المادة: | 0 (Antineoplastic Agents) |
| تواريخ الأحداث: | Date Created: 20240628 Date Completed: 20240628 Latest Revision: 20240912 |
| رمز التحديث: | 20240913 |
| مُعرف محوري في PubMed: | PMC11211812 |
| DOI: | 10.1093/bioinformatics/btae261 |
| PMID: | 38940173 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1367-4811 |
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| DOI: | 10.1093/bioinformatics/btae261 |