Artif Intell Med 2018 04 22;85:43-49. Epub 2017 Sep 22.
Biomedical Research Institute of Girona, Avda. de França, s/n, 17007 Girona, Spain; CIBERobn Pathophysiology of Obesity and Nutrition, Instituto de Salud Carlos III, Madrid, Spain. Electronic address:
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BMC Genomics 2015 21;16 Suppl 2:S5. Epub 2015 Jan 21.
Background: Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases. Read More
Proteins 2008 Jun;71(4):1930-9
Department of Bioinformatics and Computational Biology, George Mason University, Manassas, Virginia 20110, USA.
There is substantial interest in methods designed to predict the effect of nonsynonymous single nucleotide polymorphisms (nsSNPs) on protein function, given their potential relationship to heritable diseases. Current state-of-the-art supervised machine learning algorithms, such as random forest (RF), train models that classify single amino acid mutations in proteins as either neutral or deleterious to function. However, it is frequently the case that the functional effect of a polymorphism on a protein resides between these two extremes. Read More
Technol Health Care 2016 ;24(1):31-42
Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Background: Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. Read More
BMC Nephrol 2013 Jul 23;14:162. Epub 2013 Jul 23.
Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Hong Kong, SAR, China.
Background: Multi-causality and heterogeneity of phenotypes and genotypes characterize complex diseases. In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD).
Methods: In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with DKD and 554 without DKD at enrolment and after a median follow-up period of 7. Read More