An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations

Abstract

Motivation: In order to create controlled vocabularies for shared use in different biomedical domains, a large number of biomedical ontologies such as Disease Ontology (DO) and Human Phenotype Ontology (HPO), etc., are created in the bioinformatics community. Quantitative measures of the associations among diseases could help researchers gain a deep insight of human diseases, since similar diseases are usually caused by similar molecular origins or have similar phenotypes, which is beneficial to reveal the common attributes of diseases and improve the corresponding diagnoses and treatment plans. Some previous are proposed to measure the disease similarity using a particular biomedical ontology during the past few years, but for a newly discovered disease or a disease with few related genetic information in Disease Ontology (i.e., a disease with less disease-gene associations), these previous approaches usually ignores the joint computation of disease similarity by integrating gene and phenotype associations. Results: In this paper we propose a novel method called GPSim to effectively deduce the semantic similarity of diseases. In particular, GPSim calculates the similarity by jointly utilizing gene, disease and phenotype associations extracted from multiple biomedical ontologies and databases. We also explore the phenotypic factors such as the depth of HPO terms and the number of phenotypic associations that affect the evaluation performance. A final experimental evaluation is carried out to evaluate the performance of GPSim and shows its advantages over previous approaches.

Evaluation of standard and semantically-augmented distance metrics for neurology patients

“…We group signs and symptoms under the more general term findings [1]. Distance metrics play an important role in advancing precision medicine, machine learning, and patient phenotyping [2][3][4][5][6][7][8][9][10][11][12]. Patient distances can be calculated based on findings that have been converted to machine codes based on concepts from a hierarchical ontology.…”

Evaluation of standard and semantically-augmented distance metrics for neurology patients

“…Distance metrics play an important role in advancing precision medicine, machine learning, and patient phenotyping [2][3][4][5][6][7][8][9][10][11]. In this paper, we focus on metrics that measure the distance between neurological patients based on their signs and symptoms [12].…”

“…A variety of similarity and distance metrics are available; they have been used to calculate distances between patients [13][14][15][16], documents [17][18][19], and phenotypes [4,5,9,10]. If similarity and distances metrics are normalized to a scale of 0 to 1.0, the distance between A and B is the complement of the similarity.…”

“…Bipartite similarity metrics resembling equation 2 have been implemented for the genegene and phenotype-phenotype distances in the Human Phenotype Ontology (HPO) [9][10]. Melton et al [16] adopted a similar metric to measure inter-patient distances.…”

iEsGene-ZCPseKNC: Identify Essential Genes Based on Z Curve Pseudo $k$ -Tuple Nucleotide Composition

“…These features were combined with some state-of-the-art classifiers to construct the predictors, such as Support Vector Machines (SVMs) [6], decision tree [4], Naïve Bayes [5] and Non-negative Matrix Factorization (NMF), etc. However, all these features rely on experimental data, which are frequently unavailable [11]- [14]. In order to solve this problem, some predictors were constructed only based on the features extracted from the DNA sequences, for examples, Ning et al [15] used single nucleotide frequencies, dinucleotide frequencies, amino acid frequencies, and Codon Adaptation Index (CAI) to predict the essential genes of 16 bacteria.…”