Applying Monte Carlo Simulation to Biomedical Literature to Approximate Genetic Network

Al-Dalky, Rami; Taha, Kamal; Homouz, Dirar; Qasaimeh, Murad

doi:10.1109/tcbb.2015.2481399

Cited by 14 publications

(11 citation statements)

References 47 publications

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“…6The prediction is made over several thresholds. As the threshold increases, fewer pairs are assigned to the positive class Comparison with recent approaches: We evaluated our approach with CGDA [14], EDC-EDC [42] and MCforGN [43]. To compare to these approaches, we used the same ground truth data they follow (i.e., PGDB [44]).…”

Section: Prostate Cancer Case Study and Comparisonmentioning

confidence: 99%

“…It proposes novel linguistic computational techniques to extract genes interactions. It employs a hybrid constituency–dependency parser for developing a biological NLP information extraction.MCforGN [43]: MCforGN determines related genes based on their co-occurrence in MEDLINE abstracts. It employs both the standard centrality measures and Monte Carlo simulation to identify genetic networks and disease-gene associations.We evaluated the performance of our system using the common centrality measures across all approaches (i.e., Closeness, Betweenness, Degree).…”

Section: Prostate Cancer Case Study and Comparisonmentioning

confidence: 99%

“…Comparison with recent approaches: We evaluated our approach with CGDA [14], EDC-EDC [42] and MCforGN [43]. To compare to these approaches, we used the same ground truth data they follow (i.e., PGDB [44]).…”

Section: Prostate Cancer Case Study and Comparisonmentioning

confidence: 99%

“…MCforGN [43]: MCforGN determines related genes based on their co-occurrence in MEDLINE abstracts. It employs both the standard centrality measures and Monte Carlo simulation to identify genetic networks and disease-gene associations.…”

Section: Prostate Cancer Case Study and Comparisonmentioning

confidence: 99%

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Analyzing a co-occurrence gene-interaction network to identify disease-gene association

et al. 2019

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BackgroundUnderstanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes.ResultsWe evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes.ConclusionsThe results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods.Electronic supplementary materialThe online version of this article (10.1186/s12859-019-2634-7) contains supplementary material, which is available to authorized users.

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Section: Prostate Cancer Case Study and Comparisonmentioning

confidence: 99%

Section: Prostate Cancer Case Study and Comparisonmentioning

confidence: 99%

Section: Prostate Cancer Case Study and Comparisonmentioning

confidence: 99%

Section: Prostate Cancer Case Study and Comparisonmentioning

confidence: 99%

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Analyzing a co-occurrence gene-interaction network to identify disease-gene association

et al. 2019

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“…The main techniques for the biological entities extracted from the literature are summarized into co-occurrence techniques 13 , 14 , rule-based systems and machine-learning-based systems 15 – 17 . Co-occurrence approaches determine the genes that are related by their co-occurrence frequency found in the literature.…”

Section: Introductionmentioning

confidence: 99%

Constructing Genetic Networks using Biomedical Literature and Rare Event Classification

Al-Aamri

Taha

Al-Hammadi

et al. 2017

Sci Rep

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Text mining has become an important tool in bioinformatics research with the massive growth in the biomedical literature over the past decade. Mining the biomedical literature has resulted in an incredible number of computational algorithms that assist many bioinformatics researchers. In this paper, we present a text mining system called Gene Interaction Rare Event Miner (GIREM) that constructs gene-gene-interaction networks for human genome using information extracted from biomedical literature. GIREM identifies functionally related genes based on their co-occurrences in the abstracts of biomedical literature. For a given gene g, GIREM first extracts the set of genes found within the abstracts of biomedical literature associated with g. GIREM aims at enhancing biological text mining approaches by identifying the semantic relationship between each co-occurrence of a pair of genes in abstracts using the syntactic structures of sentences and linguistics theories. It uses a supervised learning algorithm, weighted logistic regression to label pairs of genes to related or un-related classes, and to reflect the population proportion using smaller samples. We evaluated GIREM by comparing it experimentally with other well-known approaches and a protein-protein interactions database. Results showed marked improvement.

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Detecting Overlapping Communities of Nodes with Multiple Attributes from Heterogeneous Networks

Taha¹,

Yoo²

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Applying Monte Carlo Simulation to Biomedical Literature to Approximate Genetic Network

Cited by 14 publications

References 47 publications

Analyzing a co-occurrence gene-interaction network to identify disease-gene association

Analyzing a co-occurrence gene-interaction network to identify disease-gene association

Constructing Genetic Networks using Biomedical Literature and Rare Event Classification

Detecting Overlapping Communities of Nodes with Multiple Attributes from Heterogeneous Networks

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