Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy
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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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KL NSCLC lines show selective sensitivity to loss of COPI components and to drugs that block lysosomal acidification, such as bafilomycin A, because they need lysosomal macromolecule breakdown to supply TCA cycle substrates.
“KL NSCLC cell lines are characterized by unique sensitivity to depletion of multiple components of the coatomer 1 (COPI) complex and pharmacological inhibition of lysosomal acidification (for example by exposure to bafilomycin A) as a result of critical dependence on lysosomal macromolecule degradation for supply of TCA cycle substrates155.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [155]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J. A., Thompson, I. R., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1111 · Cited by 4,051 · see citing works
Self-citationRichardson, M., & Domingos, P. (2006) · Machine Learning · doi:10.1007/s10994-006-5833-1 · Cited by 2,676 · see citing works
Systematic identification of genomic markers of drug sensitivity in cancer cells
Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456 · see citing works
Self-citationPI3K pathway alterations in cancer: variations on a theme
Yuan, T. L., & Cantley, L. C. (2008) · Oncogene · doi:10.1038/onc.2008.245 · Cited by 1,845 · see citing works
De novo assembly of bacterial transcriptomes from RNA-seq data
Tjaden, B. C. (2015) · Genome biology · doi:10.1186/s13059-014-0572-2 · Cited by 1,695 · see citing works
Markov Random Fields and Their Applications
Kindermann, R. P., & Snell, J. L. (1980) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/001 · Cited by 1,292 · see citing works
Carney, P. A., Miglioretti, D. L., Yankaskas, B. C., Kerlikowske, K. M., Rosenberg, R., Rutter, C. M., Geller, B. M., Abraham, L., Taplin, S. H., Dignan, M., et al. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-138-3-200302040-00008 · Cited by 1,144 · see citing works
Cellular mechanisms of tumour suppression by the retinoblastoma gene
Burkhart, D. L., & Sage, J. (2008) · Nature Reviews Cancer · doi:10.1038/nrc2399 · Cited by 974 · see citing works
Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset
Seashore-Ludlow, B., Rees, M. G., Cheah, J. H., Cokol, M., Price, E. V., Coletti, M. E., Jones, V., Bodycombe, N. E., Soule, C. K., Gould, J., et al. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-0235 · Cited by 813 · see citing works
Mutual exclusivity analysis identifies oncogenic network modulesPreprint
Ciriello, G., Cerami, E. G., Sander, C., & Schultz, N. D. (2011) · Genome Research · doi:10.1101/gr.125567.111 · Cited by 676 · see citing works
The influence of variables on Boolean functions
Kahn, J., Kalai, G., & Linial, N. (1988) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1988.21923 · Cited by 624 · see citing works
Hubbard, R. A., Kerlikowske, K. M., Flowers, C. I., Yankaskas, B. C., Zhu, W., & Miglioretti, D. L. (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00004 · Cited by 413 · see citing works
An implementation of logical analysis of data
Boros, E., Hammer, P. L., Ibaraki, T., Kogan, A., Mayoraz, E., & Muchnik, I. B. (2000) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.842268 · Cited by 406 · see citing works
Detecting epistasis in human complex traits
Wei, W., Hemani, G., & Haley, C. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3747 · Cited by 403 · see citing works
Sáez-Rodríguez, J., Alexopoulos, L. G., Epperlein, J. P., Samaga, R., Lauffenburger, D. A., Klamt, S., & Sorger, P. K. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.87 · Cited by 347 · see citing works
Activities and Sensitivities in Boolean Network Models
Shmulevich, I., & Kauffman, S. A. (2004) · Physical Review Letters · doi:10.1103/physrevlett.93.048701 · Cited by 285 · see citing works
Self-citationRuczinski, I., Kooperberg, C. L., & LeBlanc, M. L. (2003) · Journal of Computational and Graphical Statistics · doi:10.1198/1061860032238 · Cited by 276 · see citing works
Fewer permutations, more accurate P -values
Knijnenburg, T., Wessels, L. F. A., Reinders, M. J. T., & Ilya Shmulevich, C. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp211 · Cited by 221 · see citing works
Self-citationPearl, J. (1998) · Cited by 213 · see citing works
Identifying interacting SNPs using Monte Carlo logic regression
Kooperberg, C. L., & Ruczinski, I. (2004) · Genetic Epidemiology · doi:10.1002/gepi.20042 · Cited by 210 · see citing works
CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer
Leiserson, M. D. M., Wu, H.-T., Vandin, F., & Raphael, B. J. (2015) · Genome biology · doi:10.1186/s13059-015-0700-7 · Cited by 202 · see citing works
Genetic Interactions Between Transcription Factors Cause Natural Variation in Yeast
Gerke, J., Lorenz, K., & Cohen, B. A. (2009) · Science · doi:10.1126/science.1166426 · Cited by 198 · see citing works
A continuous approach to inductive inference
Kamath, A. P., Karmarkar, N. K., Ramakrishnan, K. G., et al. (1992) · Mathematical Programming · doi:10.1007/bf01581082 · Cited by 103 · see citing works
Detecting high-order interactions of single nucleotide polymorphisms using genetic programming
Nunkesser, R., Bernholt, T., Schwender, H., et al. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm522 · Cited by 66 · see citing works
Collections of Simultaneously Altered Genes as Biomarkers of Cancer Cell Drug Response
Masica, D. L., & Karchin, R. (2013) · Cancer Research · doi:10.1158/0008-5472.can-12-3122 · Cited by 48 · see citing works
A Method of Producing a Boolean Function Having an Arbitrarily Prescribed Prime Implicant Table
Gimpel, J. F. (1965) · IEEE Transactions on Electronic Computers · doi:10.1109/pgec.1965.264175 · Cited by 42 · see citing works
Kindermann, R. P., & Snell, J. L. (1980) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/001/01 · Cited by 31 · see citing works
Sparse combinatorial inference with an application in cancer biology
Mukherjee, S., Pelech, S., Neve, R. M., et al. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn611 · Cited by 19 · see citing works
Markov Logic Networks in the Analysis of Genetic Data
Sakhanenko, N. A., & Galas, D. J. (2010) · Journal of Computational Biology · doi:10.1089/cmb.2010.0044 · Cited by 10 · see citing works
EPEPT: A web service for enhanced P-value estimation in permutation tests
Knijnenburg, T., Lin, J., Rovira, H., et al. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-411 · Cited by 10 · see citing works
Self-citationInferring combinatorial association logic networks in multimodal genome-wide screens
de Ridder, J., Gerrits, A., Bot, J. J., et al. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq211 · Cited by 5 · see citing works
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Research works printed but not linked: 1
- Kahn J., Kalai G. & Linial N. In Foundations of Computer Science, 29th Annual Symposium on. 68– 80 (IEEE, 1988).
Cited by
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Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M., Aben, N., Gonçalves, E., Barthorpe, S., Lightfoot, H., et al. (2016) · Cell · doi:10.1016/j.cell.2016.06.017 · Cited by 2,090 · see citing works
Self-citationCo-occurring genomic alterations in non-small-cell lung cancer biology and therapy
Skoulidis, F., & Heymach, J. V. (2019) · Nature Reviews Cancer · doi:10.1038/s41568-019-0179-8 · Cited by 1,019 · see citing works
Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia
Malani, D., Kumar, A., Brück, O., Kontro, M., Yadav, B., Hellesøy, M., Kuusanmäki, H., Dufva, O., Kankainen, M., Eldfors, S., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0410 · Cited by 193 · see citing works
Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies
Mina, M., Raynaud, F., Tavernari, D., Battistello, E., Sungalee, S., Saghafinia, S., Laessle, T., Sanchez-Vega, F., Schultz, N., Oricchio, E., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.06.010 · Cited by 129 · see citing works
Naulaerts, S., Dang, C. C., & Ballester, P. J. (2017) · Oncotarget · doi:10.18632/oncotarget.20923 · Cited by 41 · see citing works
- Authors
- Knijnenburg, T. A., Klau, G. W., Iorio, F., Garnett, M. J., McDermott, U., Shmulevich, I., & Wessels, L. F. A.
- Affiliations
- Institute for Systems Biology (US)
- Centrum Wiskunde & Informatica (NL)
- European Bioinformatics Institute (GB)
- Wellcome Sanger Institute (GB)
- The Netherlands Cancer Institute (NL)
- Corresponding author
- Wessels, Lodewyk F. A.
- ORCID
- Venue
- Scientific Reports
- Published
- 2016-11-23
- Volume
- 6
- Issue
- 1
- Pages
- 36812-36812
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5120272
- OpenAlex
- W2952285211
- Cited by
- 74
- References
- 33
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 11 · 2023: 11 · 2022: 7 · 2021: 5 · 2020: 7 · 2019: 9 · 2018: 3 · 2017: 5 · 2016: 1
- FWCI
- 3.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by)
- Funders
- National Cancer Institute (U24CA143835)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- logic model, model interpretability, cancer cell line panel, drug response prediction, precision oncology, sensitivity and specificity, hypothesis generation
- MeSH terms
- Algorithms, Antineoplastic Agents, Humans, Logic, Models, Theoretical, Neoplasms, Sensitivity and Specificity, Cell Line, Tumor, Data Mining, Precision Medicine
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