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Routine discovery of complex genetic models using genetic algorithms
2004
Applied Soft Computing
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Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. ...
We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other ...
Acknowledgments This work was supported by National Institutes of Health grants HL65234, HL65962, GM31304, AG19085, AG20135, and LM007450. ...
doi:10.1016/j.asoc.2003.08.003
pmid:20948983
pmcid:PMC2952957
fatcat:7kxwg3irlfew7afhgf5cufey24
Grammatical Evolution for the Discovery of Petri Net Models of Complex Genetic Systems
[chapter]
2003
Lecture Notes in Computer Science
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We propose here a grammatical evolution approach for the automatic discovery of Petri net models of biochemical systems that are consistent with population level genetic models of disease susceptibility ...
We demonstrate the grammatical evolution approach routinely identifies interesting and useful Petri net models in a human-competitive manner. ...
All decisions made in the building of the PN model are made by each subsequent bit or combination of bits in the genetic algorithm chromosome. ...
doi:10.1007/3-540-45110-2_139
fatcat:r65yndimgfcpzkcoiifcadypau
Evaluation of a discrete dynamic systems approach for modeling the hierarchical relationship between genes, biochemistry, and disease susceptibility
2003
Discrete and continuous dynamical systems. Series B
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A central goal of human genetics is the identification of combinations of DNA sequence variations that increase susceptibility to common, complex human diseases. ...
In the present study, we evaluate this automated model discovery approach using four different nonlinear gene-gene interaction models. ...
Acknowledgements This work was supported by National Institutes of Health grants HL65234, HL65962, GM31304, AG19085, and AG20135. ...
doi:10.3934/dcdsb.2004.4.275
fatcat:kwhavy2dnncj7bqjsatvn5esya
Personalized medicine and atrial fibrillation: will it ever happen?
2012
BMC Medicine
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Recent evidence demonstrates a heritable component underlying AF, and genetic discoveries have identified common variants associated with the arrhythmia. ...
Ultimately one hopes that the consideration of genetic variation in clinical practice may enhance care and improve health outcomes. ...
However, increased attention to pharmacogenetics and prediction modeling using genetic information offers hope for improved clinical care. ...
doi:10.1186/1741-7015-10-155
pmid:23210687
pmcid:PMC3568716
fatcat:5vm52hc2hvfoxm657n4qe3prhm
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning
[article]
2022
arXiv
pre-print
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So far, evolutionary computation (EC), in particular in the form of genetic programming (GP), represents a key enabler for the discovery of interpretable machine learning (IML) models. ...
Interpretability can be critical for the safe and responsible use of machine learning models in high-stakes applications. ...
In our view, GP research aimed at IML should strive to improve the discovery of accurate models, subject to these models having limited complexity. ...
arXiv:2204.02046v1
fatcat:k46prk5yk5dqxaz6g44y3idvua
Genetic algorithms for computational materials discovery accelerated by machine learning
2019
npj Computational Materials
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Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model ...
This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. ...
The authors acknowledge support of the European Commission under the FP7 Fuel
VIII. REFERENCES ...
doi:10.1038/s41524-019-0181-4
fatcat:e5zt7axjorfrpdfnltfj2jnt2a
Biosignature Discovery for Substance Use Disorders Using Statistical Learning
2018
Trends in Molecular Medicine
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Biosignatures in Substance Use Disorders Biomarkers for substance use disorders (SUD) are available for drug use based on detection of the substance or its metabolites, e.g., ethyl glucuronide for alcohol ...
High-throughput clinical, imaging and "omic" technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. ...
New computation and data management technologies no longer limit complex statistical modeling routines. • Machine learning algorithms are beginning to be applied to SUD data. ...
doi:10.1016/j.molmed.2017.12.008
pmid:29409736
pmcid:PMC5836808
fatcat:ubbbuswxtfbirgnar3vwidlpda
Extracting research-quality phenotypes from electronic health records to support precision medicine
2015
Genome Medicine
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In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. ...
to accelerate genomic discovery. ...
a byproduct of healthcare for genetic discovery. ...
doi:10.1186/s13073-015-0166-y
pmid:25937834
pmcid:PMC4416392
fatcat:g4hiwcrxybatnc5s2ryrxxgw6y
Development of predictive genetic tests for improving the safety of new medicines: the utilization of routinely collected electronic health records
2014
Drug Discovery Today
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., Development of predictive genetic tests for improving the safety of new medicines: the utilization of routinely collected electronic health records, Drug Discov Today (2013), http://dx. ...
Acknowledgments The research leading to these results was conducted as part of the PROTECT consortium (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium, ...
Using the example of serious drug-induced liver injury, we illustrate how a database of routinely collected electronic health records (EHRs) could be used to overcome these barriers by facilitating rapid ...
doi:10.1016/j.drudis.2013.11.003
pmid:24239729
fatcat:r6cqzshgavggto4l25gwrdbtwm
Data Mining by Decomposition: Adaptive Search for Hypothesis Generation
1999
INFORMS journal on computing
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A useful way to address this complexity is to partition the search problem and apply separate, but intertwined, algorithms for attribute search and pattern search. ...
A genetic algorithm is applied on the attribute search problem to identify subsets that lead to more interesting patterns. ...
Acknowledgments The author acknowledges the important contributions of LCDR David ...
doi:10.1287/ijoc.11.3.239
fatcat:ujkxhiuqerdgxlejr5dcpgwpwu
Automated Discovery of Symbolic Approximation Formulae using Genetic Programming
2020
International Journal of Computer Applications
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This paper describes the use of genetic programming to automate the discovery of symbolic approximation formulae. ...
Based on these results, we consider genetic programming to be a powerful and effective technique for the automated discovery of symbolic approximation formulae. ...
THE REGRESSION MODEL In the regression model the individuals in the genetic population are compositions of primitive functions and terminals. The set of primitive functions used is. ...
doi:10.5120/ijca2020920053
fatcat:locwzwckwnb7dkhb7xsm7odqii
On the Relevance of Genetic Programming to Evolutionary Economics
[chapter]
2001
Evolutionary Controversies in Economics
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To answer this question, let us go back to the origin of genetic algorithms. ...
These experiments show the effect of the length of the LISP S-expression (the algorithmic complexity of the program) on discovery. ...
doi:10.1007/978-4-431-67903-5_10
fatcat:olg2ayl2enfqpik3ccsun6tfuy
R/qtlbim: QTL with Bayesian Interval Mapping in experimental crosses
2007
Bioinformatics
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It includes several efficient Markov chain Monte Carlo (MCMC) algorithms for evaluating the posterior of genetic architectures, i.e. the number and locations of QTL, their main and epistatic effects, and ...
trait loci (QTL) models for continuous, binary and ordinal traits in experimental crosses. ...
This work is supported by National Institutes of Health (NIH) Grants R01 GM069430 (NY). ...
doi:10.1093/bioinformatics/btm011
pmid:17237038
pmcid:PMC4995770
fatcat:2ujc5veyune7vn7j6g7awvorqe
Systems Biology Modeling in Human Genetics Using Petri Nets and Grammatical Evolution
[chapter]
2004
Lecture Notes in Computer Science
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We previously demonstrated that this approach routinely identifies biological systems models that are consistent with a variety of complex genetic models in which disease susceptibility is determined by ...
Our systems biology strategy uses grammatical evolution for symbolic manipulation and optimization of Petri net models. ...
This work was supported by National Institutes of Health grants HL65234, HL65962, GM31304, AG19085, and AG20135. ...
doi:10.1007/978-3-540-24854-5_40
fatcat:qr5lnp77gbfydlo42wcp2rktey
Artificial Intelligence for Cardiac Imaging-Genetics Research
2020
Frontiers in Cardiovascular Medicine
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High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. ...
one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. ...
This has contributed to shift the focus to genetic discovery and the study of common, complex disease traits. ...
doi:10.3389/fcvm.2019.00195
pmid:32039240
pmcid:PMC6985036
fatcat:vxnwmwmdtbdulflvgcflqppx7e
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