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Routine discovery of complex genetic models using genetic algorithms

Jason H Moore, Lance W Hahn, Marylyn D Ritchie, Tricia A Thornton, Bill C White
2004 Applied Soft Computing  
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]

Jason H. Moore, Lance W. Hahn
2003 Lecture Notes in Computer Science  
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

Lance Hahn, Jason Moore
2003 Discrete and continuous dynamical systems. Series B  
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?

Steven A Lubitz, Patrick T Ellinor
2012 BMC Medicine  
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]

Marco Virgolin, Eric Medvet, Tanja Alderliesten, Peter A.N. Bosman
2022 arXiv   pre-print
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

Paul C. Jennings, Steen Lysgaard, Jens Strabo Hummelshøj, Tejs Vegge, Thomas Bligaard
2019 npj Computational Materials  
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

James W. Baurley, Christopher S. McMahan, Carolyn M. Ervin, Bens Pardamean, Andrew W. Bergen
2018 Trends in Molecular Medicine  
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

Wei-Qi Wei, Joshua C Denny
2015 Genome Medicine  
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

Kevin Wing, Ian Douglas, Krishnan Bhaskaran, Olaf H. Klungel, Robert F. Reynolds, Munir Pirmohamed, Liam Smeeth, Tjeerd P. van Staa
2014 Drug Discovery Today  
., 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

Hemant K. Bhargava
1999 INFORMS journal on computing  
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

Mohamed M.
2020 International Journal of Computer Applications  
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]

Shu-Heng Chen
2001 Evolutionary Controversies in Economics  
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

B. S. Yandell, T. Mehta, S. Banerjee, D. Shriner, R. Venkataraman, J. Y. Moon, W. W. Neely, H. Wu, R. von Smith, N. Yi
2007 Bioinformatics  
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]

Jason H. Moore, Lance W. Hahn
2004 Lecture Notes in Computer Science  
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

Antonio de Marvao, Timothy J. W. Dawes, Declan P. O'Regan
2020 Frontiers in Cardiovascular Medicine  
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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