Association of genes to genetically inherited diseases using data mining

Perez‐Iratxeta, Carolina; Bork, Peer; Andrade, Miguel Ángel Gutiérrez

doi:10.1038/ng895

Cited by 316 publications

(192 citation statements)

References 11 publications

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“…Although there has been a recent growth in text-mining research geared toward capturing gene-phenotype relationships from the literature (1,(17)(18)(19)(20)(21), it has failed to provide deep semantic and nested levels of associations from which ternary or higher order relations (e.g., a celltype-dependent specific gene function) across concepts can be derived. Alternatively, some natural language processing (NLP) techniques can provide a deeper level of semantic relationship and a nested level of associations across concepts, allowing for more sophisticated computational studies.…”

Section: Representation Of Phenotypes For High-throughput Analysesmentioning

confidence: 99%

Computational Approaches to Phenotyping: High-Throughput Phenomics

Lussier

Yang

2007

Proceedings of the American Thoracic Society

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The recent completion of the Human Genome Project has made possible a high-throughput "systems approach" for accelerating the elucidation of molecular underpinnings of human diseases, and subsequent derivation of molecular-based strategies to more effectively prevent, diagnose, and treat these diseases. Although altered phenotypes are among the most reliable manifestations of altered gene functions, research using systematic analysis of phenotype relationships to study human biology is still in its infancy. This article focuses on the emerging field of high-throughput phenotyping (HTP) phenomics research, which aims to capitalize on novel highthroughput computation and informatics technology developments to derive genomewide molecular networks of genotype-phenotype associations, or "phenomic associations." The HTP phenomics research field faces the challenge of technological research and development to generate novel tools in computation and informatics that will allow researchers to amass, access, integrate, organize, and manage phenotypic databases across species and enable genomewide analysis to associate phenotypic information with genomic data at different scales of biology. Key state-of-the-art technological advancements critical for HTP phenomics research are covered in this review. In particular, we highlight the power of computational approaches to conduct large-scale phenomics studies.

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Section: Representation Of Phenotypes For High-throughput Analysesmentioning

confidence: 99%

Computational Approaches to Phenotyping: High-Throughput Phenomics

Lussier

Yang

2007

Proceedings of the American Thoracic Society

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“…Many other researchers also explored the relationship between the GO annotations and disease-causing genes and predicted new candidates by using their functional similarity to the known causal genes, such as G2D [54], POCUS [55], FP [56] and GFFST [57].…”

Section: Spgoranker: Integration Of Go Annotations Into Sprankermentioning

confidence: 99%

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks

Ganegoda

et al. 2014

Sci. China Life Sci.

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Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies. However, it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments. With the advances of the high-throughput techniques, a large number of protein-protein interactions have been produced. Therefore, to address this issue, several methods based on protein interaction network have been proposed. In this paper, we propose a shortest path-based algorithm, named SPranker, to prioritize disease-causing genes in protein interaction networks. Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes, we further propose an improved algorithm SPGOranker by integrating the semantic similarity of gene ontology (GO) annotations. SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account. The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches, ICN, VS and RWR. The experimental results show that SPranker and SPGOranker outperform ICN, VS, and RWR for the prioritization of orphan disease-causing genes. Importantly, for the case study of severe combined immunodeficiency, SPranker and SPGOranker predict several novel causal genes. disease-causing genes, prioritization, gene ontology, protein interaction network, shortest path Citation:Li M, Li Q, Ganegoda GU, Wang JX, Wu FX, Pan Y. Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks.

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“…Sehgal [23] uses Mesh headings to create concept profiles to compute the similar genes and drugs. Perez-Iratxeta [24] uncovers the relation of inherited disease and gene by Mesh terms of MEDLINE and RefSeq. While, Freudenberg et al [25] got disease clusters according to the fuzzy similarity between phenotype information of disease, which is extracted from OMIM (Online Mendelian Inheritance in Man) then predict the possible disease relevant genes based on the disease clusters.…”

Section: Related Work On Biomedical Literature Miningmentioning

confidence: 99%

Text Mining for Finding Functional Community of Related Genes Using TCM Knowledge

Zhou

Liu³

et al. 2004

Lecture Notes in Computer Science

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Abstract. We present a novel text mining approach to uncover the functional gene relationships, maybe, temporal and spatial functional modular interaction networks, from MEDLINE in large scale. Other than the regular approaches, which only consider the reductionistic molecular biological knowledge in MEDLINE, we use TCM knowledge(e.g. Symptom Complex) and the 50,000 TCM bibliographic records to automatically congregate the related genes. A simple but efficient bootstrapping technique is used to extract the clinical disease names from TCM literature, and term co-occurrence is used to identify the disease-gene relationships in MEDLINE abstracts and titles. The underlying hypothesis is that the relevant genes of the same Symptom Complex will have some biological interactions. It is also a probing research to study the connection of TCM with modern biomedical and post-genomics studies by text mining. The preliminary results show that Symptom Complex gives a novel top-down view of functional genomics research, and it is a promising research field while connecting TCM with modern life science using text mining.

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Association of genes to genetically inherited diseases using data mining

Cited by 316 publications

References 11 publications

Computational Approaches to Phenotyping: High-Throughput Phenomics

Computational Approaches to Phenotyping: High-Throughput Phenomics

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks

Text Mining for Finding Functional Community of Related Genes Using TCM Knowledge

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