Disease candidate gene identification and prioritization using protein interaction networks

Chen, Jing; Aronow, Bruce J.; Jegga, Anil G.

doi:10.1186/1471-2105-10-73

Cited by 315 publications

(290 citation statements)

References 55 publications

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“…Our network-based knowledge model method is very different from previous approaches that use networks such as those presented in [6]. There are at least three major differences:…”

Section: Other Network-based Methodsmentioning

confidence: 88%

“…We demonstrate the utility of our method by prioritizing genes related to five diseases. We show that our method outperforms the existing link analysis methods that were used by Chen et al to prioritize candidate genes [6].…”

Section: Introductionmentioning

confidence: 83%

“…Our model is less restrictive and assumes that the genes can be dependent. Another issue with the method used in [6] is that the training of the model must be repeated for a each new set of seed genes. Equation 3 defines the PageRank with Priors method used in [6].…”

Section: Other Network-based Methodsmentioning

confidence: 99%

“…We now elaborate on the last two differences. Equation 2 defines the importance or relevance of a gene t with respect to a set of n seed genes R = (r 1 ...r n ) as used in [6].…”

Section: Other Network-based Methodsmentioning

confidence: 99%

“…In [8], Chen et al defined a heuristic relevance scoring function on a protein-protein interaction network and applied it to prioritize candidate genes for Alzheimer's disease. More recently Chen et al [6] explored link analysis methods to study similar interaction networks to identify and prioritize candidate genes given a list of seed genes known to be associated with the disease of interest. The link analysis methods used in this work were Hyperlink-Induced Topic Search (HITS) [11] and PageRank [12] which have been extensively applied for analyzing social, Web and citation networks.…”

Section: Introductionmentioning

confidence: 99%

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Gene prioritization using a probabilistic knowledge model

Wang

Hauskrecht

Visweswaran

2009

2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop

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Abstract-We are interested in exploiting domain knowledge for the task of candidate gene prioritization. In this paper, we present a new gene prioritization method that learns a probabilistic knowledge model and exploits it to prioritize candidate genes. The knowledge model is represented by a network of associations among domain concepts (e.g., genes) and is extracted from a domain database (e.g., protein-protein interaction database). This knowledge model is then used to perform probabilistic inferences and applied to the task of gene prioritization. We evaluate our new method on five diseases and show that it outperforms a recently described network-based method for candidate gene prioritization.

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“…Our network-based knowledge model method is very different from previous approaches that use networks such as those presented in [6]. There are at least three major differences:…”

Section: Other Network-based Methodsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 83%

Section: Other Network-based Methodsmentioning

confidence: 99%

“…We now elaborate on the last two differences. Equation 2 defines the importance or relevance of a gene t with respect to a set of n seed genes R = (r 1 ...r n ) as used in [6].…”

Section: Other Network-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gene prioritization using a probabilistic knowledge model

Wang

Hauskrecht

Visweswaran

2009

2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop

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Protein Network Analysis for Disease Gene Identification and Prioritization

Chen

Jegga

2011

Applied Statistics for Network Biology

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Network‐ and attribute‐based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability

Kou

Betancur²,

Xu³

et al. 2012

American J of Med Genetics Pt C

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Autism spectrum disorders (ASD) are a group of related neurodevelopmental disorders with significant combined prevalence (~1%) and high heritability. Dozens of individually rare genes and loci associated with high-risk for ASD have been identified, which overlap extensively with genes for intellectual disability (ID). However, studies indicate that there may be hundreds of genes that remain to be identified. The advent of inexpensive massively parallel nucleotide sequencing can reveal the genetic underpinnings of heritable complex diseases, including ASD and ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) provides an embarrassment of riches, where many candidate variants emerge. It has been argued that genetic variation for ASD and ID will cluster in genes involved in distinct pathways and protein complexes. For this reason, computational methods that prioritize candidate genes based on additional functional information such as protein-protein interactions or association with specific canonical or empirical pathways, or other attributes, can be useful. In this study we applied several supervised learning approaches to prioritize ASD or ID disease gene candidates based on curated lists of known ASD and ID disease genes. We implemented two network-based classifiers and one attribute-based classifier to show that we can rank and classify known, and predict new, genes for these neurodevelopmental disorders. We also show that ID and ASD share common pathways that perturb an overlapping synaptic regulatory subnetwork. We also show that features relating to neuronal phenotypes in mouse knockouts can help in classifying neurodevelopmental genes. Our methods can be applied broadly to other diseases helping in prioritizing newly identified genetic variation that emerge from disease gene discovery based on WES and WGS.

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Disease candidate gene identification and prioritization using protein interaction networks

Cited by 315 publications

References 55 publications

Gene prioritization using a probabilistic knowledge model

Gene prioritization using a probabilistic knowledge model

Protein Network Analysis for Disease Gene Identification and Prioritization

Network‐ and attribute‐based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability

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