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Genetic variants and their interactions in disease risk prediction – machine learning and network perspectives

Sebastian Okser, Tapio Pahikkala, Tero Aittokallio
2013 BioData Mining  
Network-based analysis of genetic variants and their interaction partners is another emerging trend by which to explore how sub-network level features contribute to complex disease processes and related  ...  In this review, we describe the basic concepts and algorithms behind machine learning-based genetic feature selection approaches, their potential benefits and limitations in genome-wide setting, and how  ...  Greg Gibson and Prof. Jason Moore, for their constructive comments, and CSC, the Finnish IT center for science, for providing us with extensive computational resources.  ... 
doi:10.1186/1756-0381-6-5 pmid:23448398 pmcid:PMC3606427 fatcat:iy4o24b3tfbwrg2eyv6dwu5wri

Regularized Machine Learning in the Genetic Prediction of Complex Traits

Sebastian Okser, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Samuli Ripatti, Tero Aittokallio, Nicholas J. Schork
2014 PLoS Genetics  
variants and their interactions that are most predictive of complex phenotypic traits.  ...  While genetic risk prediction for human diseases is used as a motivating use case, we argue that these models are also widely applicable in nonhuman applications, such as animal and plant breeding, where  ...  We argue here that many medical applications of machine learning models in genetic disease risk prediction rely essentially on two factors: effective model regularization and rigorous model validation.  ... 
doi:10.1371/journal.pgen.1004754 pmid:25393026 pmcid:PMC4230844 fatcat:zaj3iys3bvdetiarfv5d2g7zd4

Designing Data-Driven Learning Algorithms: A Necessity to Ensure Effective Post-Genomic Medicine and Biomedical Research [chapter]

Gaston K. Mazandu, Irene Kyomugisha, Ephifania Geza, Milaine Seuneu, Bubacarr Bah, Emile R. Chimusa
2019 Artificial Intelligence - Applications in Medicine and Biology [Working Title]  
This has provided insights into genetic medicine, in which case, genetic factors influence variability in disease and treatment outcomes.  ...  In this chapter, we survey and discuss existing machine learning algorithms and postgenomic analysis models supporting the process of identifying valuable markers.  ...  However, some machine learning algorithms have been designed to annotate coding and non-coding genetic variants in order to identify disease-causing mutations.  ... 
doi:10.5772/intechopen.84148 fatcat:3ncabocxmjbn3ouomzsg3zbqca

The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease

Rohan Mishra, Bin Li
2020 Aging and Disease  
expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base.  ...  Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects.  ...  Genetic risk scores can be used to describe the synthetic effects of multiple variants on the pathogenesis of AD by calculating the number of disease-related alleles and their power to predict the risk  ... 
doi:10.14336/ad.2020.0312 pmid:33269107 pmcid:PMC7673858 fatcat:72rkx7bjvbaf7earfiu5d44rqm

Patient Similarity Networks for Precision Medicine

Shraddha Pai, Gary D. Bader
2018 Journal of Molecular Biology  
Genomic data captures genetic and environmental state, providing information about heterogeneity in disease and treatment outcome, but genomic-based clinical risk scores are limited.  ...  Traditional machine-learning approaches excel at performance, but often have limited interpretability.  ...  It is the idea that an individual patient's clinical outcome -disease risk, prognosis, and treatment response -is determined by their genetic, genomic, physiological and clinical profile, that corresponds  ... 
doi:10.1016/j.jmb.2018.05.037 pmid:29860027 pmcid:PMC6097926 fatcat:v6eucwccdjabthfefnzfddtvai

Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Biljana Stankovic, Nikola Kotur, Gordana Nikcevic, Vladimir Gasic, Branka Zukic, Sonja Pavlovic
2021 Genes  
Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes.  ...  This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data.  ...  The genetic architecture of IBD is polygenic, with both rare and common variants contributing to disease risk.  ... 
doi:10.3390/genes12091438 pmid:34573420 pmcid:PMC8466305 fatcat:myxfzoorhvckbcbiuy7h4pxc3m

Machine learning-based prediction of impulse control disorders in Parkinson's disease from clinical and genetic data

Johann Faouzi, Samir Bekadar, Fanny Artaud, Alexis Elbaz, Graziella Mangone, Olivier Colliot, Jean-Christophe Corvol
2022 IEEE Open Journal of Engineering in Medicine and Biology  
We trained three logistic regressions and a recurrent neural network to predict ICDs at the next visit using clinical risk factors and genetic variants previously associated with ICDs.  ...  Conclusions: Our results indicate that machine learning methods are potentially useful for predicting ICDs, but further works are required to reach clinical relevance.  ...  PI for genetic analysis).  ... 
doi:10.1109/ojemb.2022.3178295 pmid:35813487 pmcid:PMC9252337 fatcat:na2mdmeysvgujpyktfov7rj2ha

Next-generation drug repurposing using human genetics and network biology

Serguei Nabirotchkin, Alex E Peluffo, Philippe Rinaudo, Jinchao Yu, Rodolphe Hajj, Daniel Cohen
2020 Current opinion in pharmacology (Print)  
This Pharmacological Perspective reviews progress and perspectives in combining human genetics, especially genome-wide association studies, with network biology to drive drug repurposing for rare and common  ...  Drug repurposing, involving the identification of single or combinations of existing drugs based on human genetics data and network biology approaches represents a next-generation approach that has the  ...  ., Ltd and at Tasly Pharmaceutical Group Co., Ltd for fruitful discussions.  ... 
doi:10.1016/j.coph.2019.12.004 pmid:31982325 fatcat:diheunol6zd5lnkocycrfhxjui

Artificial intelligence for precision medicine in neurodevelopmental disorders

Mohammed Uddin, Yujiang Wang, Marc Woodbury-Smith
2019 npj Digital Medicine  
Taking advantage of high performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease from  ...  Herein, therefore, lies a precision medicine conundrum: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders?  ...  , to then predict risk of disease, treatment response, prognosis and other outcomes in individual patients based on their own characteristics.  ... 
doi:10.1038/s41746-019-0191-0 pmid:31799421 pmcid:PMC6872596 fatcat:zsuwfx65obcdtbsz66xwjeilu4

Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms

Sierra S. Nishizaki, Alan P. Boyle
2017 Trends in Genetics  
One of the formative goals of genetics research is to understand how genetic variation leads to phenotypic differences and human disease.  ...  We discuss several leading methods for annotating noncoding variants and how they can be integrated into research pipelines in hopes that they will be broadly applied in future GWAS analyses.  ...  We predict that the incorporation of these tools into the GWAS pipeline will result in a shorter turnaround time between GWAS, genetic discovery, and translational research.  ... 
doi:10.1016/j.tig.2016.10.008 pmid:27939749 pmcid:PMC5553318 fatcat:btgssc32xvhc3hz53xlcilurxa

Deep learning in pharmacogenomics: from gene regulation to patient stratification

Alexandr A Kalinin, Gerald A Higgins, Narathip Reamaroon, Sayedmohammadreza Soroushmehr, Ari Allyn-Feuer, Ivo D Dinov, Kayvan Najarian, Brian D Athey
2018 Pharmacogenomics (London)  
This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: (1) identification of novel regulatory variants located in noncoding domains and their  ...  function as applied to pharmacoepigenomics; (2) patient stratification from medical records; and (3) prediction of drugs, targets, and their interactions.  ...  performance for the disease prediction [97] and risk prediction given treatment [98] .  ... 
doi:10.2217/pgs-2018-0008 pmid:29697304 fatcat:tkhmrqkevjfqxdty6ttbw33jam

Big data in IBD: big progress for clinical practice

Nasim Sadat Seyed Tabib, Matthew Madgwick, Padhmanand Sudhakar, Bram Verstockt, Tamas Korcsmaros, Séverine Vermeire
2020 Gut  
Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and  ...  We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.  ...  [19] [20] [21] [22] This could result in predictions of genetic markers and variants with greater accuracy.  ... 
doi:10.1136/gutjnl-2019-320065 pmid:32111636 pmcid:PMC7398484 fatcat:64i6abikczhvbjwabgvp6cxyke

Identification of drug candidates and repurposing opportunities through compound–target interaction networks

Anna Cichonska, Juho Rousu, Tero Aittokallio
2015 Expert Opinion on Drug Discovery  
Janica Wakkinen and Dr. Simon Anders for many useful discussions about different types of experimental assays and computational models.  ...  Network modeling and machine learning approaches, such as those described in this review, can help in each of these challenges.  ...  Similarity-based machine learning methods for predicting drug--target interactions: a brief review. Brief Bioinform 2014;15(5):734-47 .  ... 
doi:10.1517/17460441.2015.1096926 pmid:26429153 fatcat:vtz37pji6jcnlmcuw6k3v7t3kq

Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

Michael K. K. Leung, Andrew Delong, Babak Alipanahi, Brendan J. Frey
2016 Proceedings of the IEEE  
, may be associated with disease risks.  ...  | In this paper, we provide an introduction to machine learning tasks that address important problems in genomic medicine.  ...  Xiong, for helpful discussions and comments. The authors would also like to thank the reviewers for their contributions to improve the paper.  ... 
doi:10.1109/jproc.2015.2494198 fatcat:esu2dpq52jgmjmxhy2vr7yslm4

Integrating Genetics and Omics to Understand Chronic Obstructive Pulmonary Disease

Edwin K. Silverman, Brian D. Hobbs
2020 Barcelona Respiratory Network  
However, despite substantial progress in delineating the genetic determinants of COPD, the biological networks influencing COPD remain largely undefined.  ...  Omics data can assist in identifying key genes that are driving genetic associations to COPD.  ...  However, multiple Omics data have not yet been reported for machine learning disease prediction in COPD.  ... 
doi:10.23866/brnrev:2019-0008 fatcat:43ppet4n65gwhau4e7a6ijjpua
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