AIMC Topic: Phenotype

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Integrating phenotype ontologies with PhenomeNET.

Journal of biomedical semantics
BACKGROUND: Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in c...

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

BMC bioinformatics
BACKGROUND: Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate om...

Identifying genotype-phenotype relationships in biomedical text.

Journal of biomedical semantics
BACKGROUND: One important type of information contained in biomedical research literature is the newly discovered relationships between phenotypes and genotypes. Because of the large quantity of literature, a reliable automatic system to identify thi...

Matching disease and phenotype ontologies in the ontology alignment evaluation initiative.

Journal of biomedical semantics
BACKGROUND: The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data ...

Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules.

BioMed research international
Named-Entity Recognition is commonly used to identify biological entities such as proteins, genes, and chemical compounds found in scientific articles. The Human Phenotype Ontology (HPO) is an ontology that provides a standardized vocabulary for phen...

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.

BMC bioinformatics
BACKGROUND: The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this c...

Digging deep into Golgi phenotypic diversity with unsupervised machine learning.

Molecular biology of the cell
The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportuni...

A new synonym-substitution method to enrich the human phenotype ontology.

BMC bioinformatics
BACKGROUND: Named entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are one of the crucial enabling technologies in bioinformatics, pro...

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits.

PloS one
In Ayurveda system of medicine individuals are classified into seven constitution types, "Prakriti", for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiologic...

Phenotype Prediction from Metagenomic Data Using Clustering and Assembly with Multiple Instance Learning (CAMIL).

IEEE/ACM transactions on computational biology and bioinformatics
The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper, we demonstrate a novel, efficient, ...