AIMC Topic: Phenotype
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HTP-NLP: A New NLP System for High Throughput Phenotyping.
Studies in health technology and informatics
Jan 1, 2017
Secondary use of clinical data for research requires a method to quickly process the data so that researchers can quickly extract cohorts. We present two advances in the High Throughput Phenotyping NLP system which support the aim of truly high throu...
Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional p...
Best Practices in Manual Annotation with the Gene Ontology.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
The Gene Ontology (GO) is a framework designed to represent biological knowledge about gene products' biological roles and the cellular location in which they act. Biocuration is a complex process: the body of scientific literature is large and selec...
Determining Multiple Sclerosis Phenotype from Electronic Medical Records.
Journal of managed care & specialty pharmacy
Dec 1, 2016
BACKGROUND: Multiple sclerosis (MS), a central nervous system disease in which nerve signals are disrupted by scarring and demyelination, is classified into phenotypes depending on the patterns of cognitive or physical impairment progression: relapsi...
Complementary feature selection from alternative splicing events and gene expression for phenotype prediction.
Bioinformatics (Oxford, England)
Sep 1, 2016
MOTIVATION: A central task of bioinformatics is to develop sensitive and specific means of providing medical prognoses from biomarker patterns. Common methods to predict phenotypes in RNA-Seq datasets utilize machine learning algorithms trained via g...
Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Aug 1, 2016
Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-...
PHOCOS: inferring multi-feature phenotypic crosstalk networks.
Bioinformatics (Oxford, England)
Jun 15, 2016
MOTIVATION: Quantification of cellular changes to perturbations can provide a powerful approach to infer crosstalk among molecular components in biological networks. Existing crosstalk inference methods conduct network-structure learning based on a s...
Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.
Bioinformatics (Oxford, England)
Jun 15, 2016
UNLABELLED: Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simu...
Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics.
The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
Mar 1, 2016
BACKGROUND: Unsupervised learning technique allows researchers to identify different phenotypes of diseases with complex manifestations.
Schwann Cell and Axon: An Interlaced Unit-From Action Potential to Phenotype Expression.
Advances in experimental medicine and biology
Jan 1, 2016
Here we propose a model of a peripheral axon with a great deal of autonomy from its cell body-the autonomous axon-but with a substantial dependence on its ensheathing Schwann cell (SC), the axon-SC unit. We review evidence in several fields and show ...
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