ArticleScientific reports2016
Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Decoding cellular vulnerabilities for interpretable drug response prediction.Briefings in bioinformatics · 2026Article
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- The Hallmarks of Predictive Oncology.Cancer discovery · 2025Article
- Computational frameworks transform antagonism to synergy in optimizing combination therapies.NPJ digital medicine · 2025Review
- Considerations in the search for epistasis.Genome biology · 2024Review
- Article
- Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.Briefings in bioinformatics · 2024Review
- Reliable anti-cancer drug sensitivity prediction and prioritization.Scientific reports · 2024Article
- Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.Journal of chemical information and modeling · 2024Article
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.Cell reports methods · 2024Article
- PANCDR: precise medicine prediction using an adversarial network for cancer drug response.Briefings in bioinformatics · 2024Article
- Precision oncology: a review to assess interpretability in several explainable methods.Briefings in bioinformatics · 2023Review
- Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.Frontiers in medicine · 2023Review
- A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer.Frontiers in molecular biosciences · 2023Article
- Deep reinforcement learning for personalized treatment recommendation.Statistics in medicine · 2022Article
- Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method.Scientific reports · 2022Article
- SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types.Cell genomics · 2022Article
- Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia.Cancer discovery · 2022Article
- Pan-cancer network disorders revealed by overall and local signaling entropy.Journal of molecular cell biology · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present 'Logic Optimization for Binary Input to Continuous Output' (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models.
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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.