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Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy

Sangkyu Lee, Sarah Kerns, Harry Ostrer, Barry Rosenstein, Joseph O. Deasy, Jung Hun Oh
2018 International Journal of Radiation Oncology, Biology, Physics  
Conclusions-We applied machine learning methods and bioinformatics tools to genome-wide data to predict and explain GU toxicity.  ...  Gene ontology analysis highlighted key biological processes, such as neurogenesis and ion transport, from the genes known to be important for urinary tract functions.  ...  Machine learning-based multivariate modeling is an alternative approach that considers many important SNPs simultaneously and combines the small effects of the SNPs to achieve greater predictive power  ... 
doi:10.1016/j.ijrobp.2018.01.054 pmid:29502932 pmcid:PMC5886789 fatcat:gzqcllramnbkblkjvpfkxvblue

DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins

Ali Akbar Jamali, Reza Ferdousi, Saeed Razzaghi, Jiuyong Li, Reza Safdari, Esmaeil Ebrahimie
2016 Drug Discovery Today  
Ebrahimie DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins Drug Discovery Today, 2016; 21(5):718-724 After the embargo period  via non-commercial  ...  Elsevier has an agreement In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND licensethis is easy to do  if aggregated with other manuscripts, for  ...  Implemented machine-learning predictors Here, we evaluated different machine-learning approaches to determine which one predicted drug targets with the highest performance.  ... 
doi:10.1016/j.drudis.2016.01.007 pmid:26821132 fatcat:shukynoncfba5i3h7pzxl46aba

De novo profile generation based on sequence context specificity with the long short-term memory network [article]

Kazunori D Yamada, Kengo Kinoshita
2017 bioRxiv   pre-print
Our findings will be useful for the development of other prediction methods related to biological sequences by machine learning methods.  ...  beta-strands, where long-range interactions of amino acids are important and are known to be difficult for the existing window-based prediction methods.  ...  Acknowledgements We are grateful to Kentaro Tomii and Toshiyuki Oda for constructive discussion.  ... 
doi:10.1101/240515 fatcat:axndrte7gjactpjd6u4rc32wvm

Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study [article]

Sangkyu Lee, Xiaolin Liang, Meghan Woods, Anne Reiner, Duncan Thomas, Patrick Concannon, Leslie Bernstein, Charles Lynch, John Boice, Joseph Deasy, Jonine Bernstein, Jung Hun Oh
2019 bioRxiv   pre-print
In summary, machine learning/bioinformatics methods applied to genome-wide genotyping data have great potential to reveal plausible biological correlates associated with the risk of RCBC.  ...  The biological analysis identified the cyclic AMP-mediated signaling and Ephrin-A as significant biological correlates, which were previously shown to influence cell survival after radiation in an ATM-dependent  ...  The specific goal of this study was to apply an agnostic machine learning approach to GWAS data to identify a predictive risk model for RCBC.  ... 
doi:10.1101/547422 fatcat:pnu6zdvrsfha7epatawzzp6xxe

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

2018 Briefings in Bioinformatics  
Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine  ...  We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance  ...  Features calculated and extracted for machine learning based predictors To construct robust and accurate machine learning predictors for lysine PTM prediction, diverse features in terms of sequence, structure  ... 
doi:10.1093/bib/bby089 pmid:30285084 pmcid:PMC6954452 fatcat:ipqhpnlhufegnodxthnwl75t4y

Automatic generation of bioinformatics tools for predicting protein–ligand binding sites

Yusuke Komiyama, Masaki Banno, Kokoro Ueki, Gul Saad, Kentaro Shimizu
2015 Bioinformatics  
We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands.  ...  Results: We developed a system for automatically generating protein-ligand binding predictions.  ...  Acknowledgements We thank the National Institute of Informatics for provision of the database server and advice on the Semantic Web.  ... 
doi:10.1093/bioinformatics/btv593 pmid:26545824 pmcid:PMC4803387 fatcat:qu5gwsh4zjdwnjljjjwttq56hq

G2P: Using machine learning to understand and predict genes causing rare neurological disorders [article]

Juan A. Botia, Sebastian Guelfi, David Zhang, Karishma D'Sa, Regina Reinolds, Daniel Onah, Ellen M. McDonagh, Antonio Rueda-Martin, Arianna Tucci, Augusto Rendon, Henry Houlden, John Hardy (+1 others)
2018 bioRxiv   pre-print
Thus, we demonstrate both the explanatory and predictive power of machine-learning-based models in neurological disease.  ...  To facilitate precision medicine and neuroscience research, we developed a machine-learning technique that scores the likelihood that a gene, when mutated, will cause a neurological phenotype.  ...  ML-based classifiers for disease gene prediction can be optimized for accuracy at the expense of genome coverage The major aim of this study was to generate machine learning classifiers, based on genetic  ... 
doi:10.1101/288845 fatcat:fkrppxadivbhjgosp24by4aj6e

Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines

Akbar K. Waljee, Kay Sauder, Anand Patel, Sandeep Segar, Boang Liu, Yiwei Zhang, Ji Zhu, Ryan W. Stidham, Ulysses Balis, Peter D. R. Higgins
2017 Journal of Crohn's & Colitis  
Our aims were to: 1) develop machine learning algorithms [MLA] using laboratory values and age to identify patients in objective remission on thiopurines; and 2) determine whether achieving algorithm-predicted  ...  Conclusions: A machine learning algorithm was able to identify IBD patients on thiopurines with algorithm-predicted objective remission, a state associated with significant clinical benefits, including  ...  Supplementary Data Supplementary data are available at ECCO-JCC online.  ... 
doi:10.1093/ecco-jcc/jjx014 pmid:28333183 pmcid:PMC5881698 fatcat:q76b56lsbncw5nqm454yf6rvbq

BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models

Hong-Liang Li, Yi-He Pang, Bin Liu
2021 Nucleic Acids Research  
that BioSeq-BLM will provide new approaches for biological sequence analysis based on natural language processing technologies, and contribute to the development of this very important field.  ...  We also extend the BLMs into a system called BioSeq-BLM for automatically representing and analyzing the sequence data.  ...  ACKNOWLEDGEMENTS We are also very much indebted to the three anonymous reviewers, whose constructive comments are very helpful for strengthening the presentation of this paper.  ... 
doi:10.1093/nar/gkab829 pmid:34581805 pmcid:PMC8682797 fatcat:yjqv3y5vejcyriigr2tvln7rum

Prediction of compounds' biological function (metabolic pathways) based on functional group composition

Yu-Dong Cai, Ziliang Qian, Lin Lu, Kai-Yan Feng, Xin Meng, Bing Niu, Guo-Dong Zhao, Wen-Cong Lu
2008 Molecular diversity  
Here, we introduce a machine learning method (Nearest Neighbor Algorithm) based on functional group composition of compounds to the analysis of metabolic pathways.  ...  A set of 2,764 compounds from 11 major classes of metabolic pathways were selected for study.  ...  Therefore, the following machine learning method based on functional group composition is reasonable. Then, we developed a NNA predictor based on functional group composition.  ... 
doi:10.1007/s11030-008-9085-9 pmid:18704735 fatcat:4iyih4hgfffxlnmcjrlhc76vwy

New risk model is able to identify patients with a low risk of progression in systemic sclerosis

Nina Marijn van Leeuwen, Marc Maurits, Sophie Liem, Jacopo Ciaffi, Nina Ajmone Marsan, Maarten Ninaber, Cornelia Allaart, Henrike Gillet van Dongen, Robbert Goekoop, Tom Huizinga, Rachel Knevel, Jeska De Vries-Bouwstra
2021 RMD Open  
ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop  ...  pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify  ...  Predictors The included predictors in the Machine-Learning-Assisted model were selected based on the predictors identified by experts, 8 additional predictors were selected based on clinical expertise  ... 
doi:10.1136/rmdopen-2020-001524 pmid:34059523 fatcat:5wm6b3yujfa5hm4dn3fzyw6d6q

Indian Stock Market Predictor System [chapter]

C. H Vanipriya, K. Thammi Reddy
2014 Advances in Intelligent Systems and Computing  
The proposed system combines both these methods to develop a hybrid machine learning Stock Market Predictor based on Neural Networks, with intent of improving the accuracy.  ...  Our proposed system combines both the statistical numeric data and sentiments of the stock on the internet to predict future prices in the stock market.  ...  ., for their constant support.  ... 
doi:10.1007/978-3-319-03095-1_3 fatcat:5ycvc6ma45gsdfvj7yjmjplx4q

Web Query Behaviour Concerning HEV (Blue) Light in Ophthalmology

Ahmed Al-Imam, Hend Jaddoa Al-Doori, Luay Mahmoud Hassan
2021 Journal of the Faculty of Medicine Baghdad  
ANOVA, linear modeling, and machine learning unanimously agreed on predictors' significant effect (search topics and time) on the web search volume.  ...  For instance, the search volume for the "Biological effects of HEV light" increased significantly in 2018-2020. Predictive modeling was most accurate for "Biological effects of HEV light".  ...  We evaluated the level of evidence of the current study as per the categorization system rectified by the Oxford Centre for Evidence-Based Medicine [7] . for all the search topics except for "Biological  ... 
doi:10.32007/jfacmedbagdad.6331840 fatcat:djisnm4hlbf4bersf7gx7jgy3y

netDx: Interpretable patient classification using integrated patient similarity networks [article]

Shraddha Pai, Shirley Hui, Ruth Isserlin, Muhammad A Shah, Hussam Kaka, Gary D Bader
2016 bioRxiv   pre-print
In comparison to traditional machine learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space.  ...  A clinical predictor based on genomic data needs to be easily interpretable to drive hypothesis-driven research into new treatments.  ...  We also thank Han Liang for discussion on implementation details for the machine learning used in Yuan et al. (2014). This work was supported by a Canadian Institutes of Health Research award  ... 
doi:10.1101/084418 fatcat:qhfqydr53nd7zjqudn4ee6ccf4

Harnessing Big Data for Systems Pharmacology [article]

Lei Xie, Eli Draizen, Philip Bourne
2016 bioRxiv   pre-print
associated with genetic/epigenetic variants and environmental factors, is coupled with molecular conformational dynamics, is affected by possible off-targets, is modulated by the complex interplay of biological  ...  Here, we discuss several upcoming issues in systems pharmacology and potential solutions to them using big data technology.  ...  Machine learning models could be inferred using different features and base learners such as Deep Neural Network, Support Vector Machine, or Random Forest.  ... 
doi:10.1101/077115 fatcat:owkyissq65agrdovh2vdgux24m
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