239 hasil untuk machine learning

Machine Learning for Detecting Gene-Gene Interactions

McKinney, Brett A.; Reif, David M.; Ritchie, Marylyn D. dkk.

Appl Bioinformatics · 2006 · Inggris · PubMed Central

…occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect…

Application of machine learning in SNP discovery

Matukumalli, Lakshmi K; Grefenstette, John J; Hyten, David L dkk.

BMC Bioinformatics · 2006 · Inggris · PubMed Central

…SNP predictions even with stringent parameter values. We developed a machine learning (ML) method to augment PolyBayes to improve its prediction accuracy. ML methods have also been successfully applied to other bioinformatics problems in predicting genes, promoters, transcription factor binding…

Clinical Data Processing Tools: A Machine Learning Resource

Ohno-Machado, Lucila; Vinterbo, Staal; Øhrn, Aleksander dkk.

Proc AMIA Symp · 1999 · Inggris · PubMed Central

A Protein Classification Benchmark collection for machine learning

Sonego, Paolo; Pacurar, Mircea; Dhir, Somdutta dkk.

Nucleic Acids Res · 2006 · Inggris · PubMed Central

Protein classification by machine learning algorithms is now widely used in structural and functional annotation of proteins. The Protein Classification Benchmark collection () was created in order to provide standard datasets on which the performance of machine learning methods can be compared. It…

Advancing Post-Genome Data and System Integration Through Machine Learning

Azuaje, Francisco

Comp Funct Genomics · 2002 · Inggris · PubMed Central

Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic…

Filtering for medical news items using a machine learning approach.

Zheng, Wanhong; Milios, Evangelos; Watters, Carolyn

Proc AMIA Symp · 2002 · Inggris · PubMed Central

…domain. This paper addresses the medical news-filtering problem using a machine learning approach. We describe the application of two supervised machine learning techniques, Decision Trees and Naïve Bayes, to automatically construct classifiers on the basis of a training set, in which news articles…

Building an asynchronous web-based tool for machine learning classification.

Weber, Griffin; Vinterbo, Staal; Ohno-Machado, Lucila

Proc AMIA Symp · 2002 · Inggris · PubMed Central

Various unsupervised and supervised learning methods including support vector machines, classification trees, linear discriminant analysis and nearest neighbor classifiers have been used to classify high-throughput gene expression data. Simpler and more widely accepted statistical tools have not…

Kernel-based machine learning protocol for predicting DNA-binding proteins

Bhardwaj, Nitin; Langlois, Robert E.; Zhao, Guijun dkk.

Nucleic Acids Res · 2005 · Inggris · PubMed Central

…and structural information with moderate accuracy. Here we develop a machine learning protocol for the prediction of DNA-BPs where the classifier is Support Vector Machines (SVMs). Information used for classification is derived from characteristics that include surface and overall composition…

Human pol II promoter prediction: time series descriptors and machine learning

Gangal, Rajeev; Sharma, Pankaj

Nucleic Acids Res · 2005 · Inggris · PubMed Central

…from non-promoters and the generalization or predictive ability of the machine-learning algorithms. In this paper we attempt to define a novel approach by using unique descriptors and machine-learning methods for the recognition of eukaryotic polymerase II promoters. In this study, non-linear time…

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Clustering WHO-ART Terms Using Semantic Distance and Machine Learning Algorithms

Iavindrasana, Jimison; Bousquet, Cedric; Degoulet, Patrice dkk.

AMIA Annu Symp Proc · 2006 · Inggris · PubMed Central

…as a terminology service on a J2EE server. Two different unsupervised machine learning algorithms (KMeans, Pvclust) clustered WHO-ART terms according to a semantic distance operator previously described. Pvclust grouped 51% of WHO-ART terms. K-Means grouped 100% of WHO-ART terms but 25% clusters…

Machine learning for an expert system to predict preterm birth risk.

Woolery, L K; Grzymala-Busse, J

J Am Med Inform Assoc · 1994 · Inggris · PubMed Central

…health care system. DESIGN: A knowledge-base development methodology used machine learning, statistical analysis, and validation techniques to analyze three large datasets (18,890 subjects and 214 variables). The dependent (i.e., decision) variable studied was weeks of gestation at delivery, with…

Tandem machine learning for the identification of genes regulated by transcription factors

Dinakarpandian, Deendayal; Raheja, Venetia; Mehta, Saumil dkk.

BMC Bioinformatics · 2005 · Inggris · PubMed Central

…sites recognized by the factor. In this study, we have developed a tandem machine learning approach for the identification of regulatory target genes based on these parameters and on the corresponding binding site information contents that measure the affinities of the factor for these cognate…

Machine learning models for lung cancer classification using array comparative genomic hybridization.

Aliferis, C. F.; Hardin, D.; Massion, P. P.

Proc AMIA Symp · 2002 · Inggris · PubMed Central

…in a single experiment. The primary goal of this study was to develop machine learning models that classify non-small Lung Cancers according to histopathology types and to compare several machine learning methods in this learning task. DNA from tumors of 37 patients (21 squamous carcinomas, and 16…

Improving machine learning performance by removing redundant cases in medical data sets.

Ohno-Machado, L.; Fraser, H. S.; Ohrn, A.

Proc AMIA Symp · 1998 · Inggris · PubMed Central

Neural network models and other machine learning methods have successfully been applied to several medical classification problems. These models can be periodically refined and retrained as new cases become available. Since training neural networks by backpropagation is time consuming, it is…

A Genetics-Based Machine Learning Approach to Knowledge Discovery in Clinical Data

Holmes, John H.

Proc AMIA Annu Fall Symp · 1996 · Inggris · PubMed Central

Genomic Computing. Explanatory Analysis of Plant Expression Profiling Data Using Machine Learning

Kell, D. B.; Darby, R. M.; Draper, J.

Plant Physiol · 2001 · Inggris · PubMed Central

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao dkk.

Invest Ophthalmol Vis Sci · 2005 · Inggris · PubMed Central

PURPOSE: Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma…

Machine learning for regulatory analysis and transcription factor target prediction in yeast

Holloway, Dustin T.; Kon, Mark; DeLisi, Charles

Syst Synth Biol · 2006 · Inggris · PubMed Central

…remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a…