A text-mining analysis of the human phenome

Abstract

A number of large-scale efforts are underway to define the relationships between genes and proteins in various species. But, few attempts have been made to systematically classify all such relationships at the phenotype level. Also, it is unknown whether such a phenotype map would carry biologically meaningful information. We have used text mining to classify over 5000 human phenotypes contained in the Online Mendelian Inheritance in Man database. We find that similarity between phenotypes reflects biological modules of interacting functionally related genes. These similarities are positively correlated with a number of measures of gene function, including relatedness at the level of protein sequence, protein motifs, functional annotation, and direct protein-protein interaction. Phenotype grouping reflects the modular nature of human disease genetics. Thus, phenotype mapping may be used to predict candidate genes for diseases as well as functional relations between genes and proteins. Such predictions will further improve if a unified system of phenotype descriptors is developed. The phenotype similarity data are accessible through a web interface at http://www.cmbi.ru.nl/MimMiner/.

Prioritizing protein complexes implicated in human diseases by network optimization

“…The phenotypic similarities were downloaded from the literature [37], including pairwise similarities for 5,080 disease. The similarity is ranged from 0 to 1, where a larger value means higher phenotypic similar between a disease pair and vice versa.…”

“…To filter the small similarities that mean low confidences among disease pairs, we introduce a parameter to remove the edges that similarities are less than $\alpha =0.1$, the mean of all disease similarities. Existing studies have shown that relationships between diseases have noises [37], and thus a noise filtering process is helpful in improving the performance of detecting disease genes [33]. Finally, we obtain a heterogeneous network including 15,078 nodes and to 5,782,818 edges.…”

Integrative methods for analyzing big data in precision medicine

“…The first group of methods uses some notion of similarity between drugs (e.g., chemical similarity [149], similarity between gene expressions induced by drug actions [74], or drug-side effect similarity [150]) to group drugs and infer a novel drug candidate for repurposing from the group that can perform the same action as other drugs in the group. The second group of methods uses similarities between diseases (e.g., phenotype similarity [151], or similarity between disease symptoms [152]) to group diseases and to infer a novel drug for repurposing by expanding known associations between the drug and some members of the group to the rest of the group. Other approaches use target-based similarities [153], i.e., protein sequence similarity [154], or 3D structural similarity [155], to infer novel drugs.…”

A collaborative knowledge base for cognitive phenomics

“…While genetics and genomics knowledge bases have developed rapidly, [1][2][3] higher-level phenomics knowledge bases (that is, comprehensive repositories for phenotype data that can be used on a genome-wide scale) are only now emerging 4 (for example, Mouse Phenome Database (www.jax.org/phenome) and Australian Phenomics Centre (www.apf.edu.au)). These informatics resources can advance molecular psychiatry research by helping researchers better define phenotype constructs, select specific phenotypic measures and ultimately develop multilevel models that specify both phene-phene and gene-phene associations.…”