Identifying gene-disease associations using centrality on a literature mined gene-interaction network

Özgür, Arzucan; Vu, Thuy; Erkan, Güneş; Radev, Dragomir

doi:10.1093/bioinformatics/btn182

Cited by 311 publications

(184 citation statements)

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“…Proteins which interact with each other are frequently involved in a common biological process (Luo et al, 2007) or are involved in a disease process Ozgür et al, 2008;Goh and Choi, 2012): to this end, networks focusing on specific disease processes have been built and mined for new candidate drug target (Köhler et al, 2008;Chen et al, 2009).…”

Section: Network Based Methodsmentioning

confidence: 99%

Big Data: Challenge and Opportunity for Translational and Industrial Research in Healthcare

Rossi

Grifantini

2018

Front. Digit. Humanit.

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Research and innovation are constant imperatives for the healthcare sector: medicine, biology and biotechnology support it, and more recently computational and data-driven disciplines gained relevance to handle the massive amount of data this sector is and will be generating. To be effective in translational and healthcare industrial research, big data in the life science domain need to be organized, well annotated, catalogued, correlated and integrated: the biggest the data silos at hand, the stronger the need for organization and tidiness. The degree of such organization marks the transition from data to knowledge for strategic decision making. Medicine is supported by observations and data and for certain aspects medicine is becoming a data science supported by clinicians. While medicine defines itself as personalized, quantified (precision med) or in high-definition, clinicians should be prepared to deal with a world in which Internet of People paraphrases the Internet of Things paradigm. Integrated use of electronic health records (EHRs) and quantitative data (both clinical and molecular) is a key process to develop precision medicine. Health records collection was originally designed for patient care and billing and/or insurance purposes. The digitization of health records facilitates and opens up new possibilities for science and research and they should be now collected and managed with this aim in mind. More data and the ability to efficiently handle them is a significant advantage not only for clinicians and life science researchers, but for drugs producers too. In an industrial sector spending increasing efforts on drug repurposing, attention to efficient methods to unwind the intricacies of the hugely complex reality of human physiology, such as network based methods and physical chemistry computational methods, became of paramount importance. Finally, the main pillars of industrial R&D processes for vaccines, include initial discovery, early-late pre clinics, pre-industrialization, clinical phases and finally registration-commercialization. The passage from one step to another is regulated by stringent pass/fail criteria. Bottlenecks of the R&D process are often represented by animal and human studies, which could be rationalized by surrogate in vitro assays as well as by predictive molecular and cellular signatures and models.

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Section: Network Based Methodsmentioning

confidence: 99%

Big Data: Challenge and Opportunity for Translational and Industrial Research in Healthcare

Rossi

Grifantini

2018

Front. Digit. Humanit.

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“…In [1], the relationship between genes and diseases is predicted by mining genes interaction networks. An assumption was proposed before extracting gene-gene interaction: the genes relationship must contain at least two genes and one interactive word.…”

Section: B Filter Candidate Sentencesmentioning

confidence: 99%

“…How to extract the needed knowledge and to obtain new knowledge quickly and efficiently from massive articles has become a very important research area. The interaction between diseases and related proteins is one of the main research directions which mean a lot to disease prevention, diagnosis, therapy and designing biomedical experiment and so on [1].…”

Section: Introductionmentioning

confidence: 99%

Liver Cancer Related Gene Analysis Based on Literature Mining

Li¹,

Zou²,

Wang³

2013

IJBBB

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Abstract-To identify liver cancer related genes and understand their interactions, we introduce a method based on literature mining. The genes are extracted with the disease-gene association classifier based on Bayesian network, and then the interaction network between the corresponding proteins is built. This paper has extracted 464 genes which are related to the liver cancer, and found genes such as p53, VEGF, TNF, AKT are hub proteins, which play important roles in the network. The KEGG pathway analysis shows that 19 enriched pathways may participate in liver carcinogenesis. Network analysis and pathway analysis implies the complexity of the occurrence, progression and prognosis of liver cancer.Index Terms-Bayesian classifier, genes and pathways analysis, liver cancer, literature mining. I. INTRODUCTIONIn the past ten years, with the development of the biomedical science technology, the number of the biomedical literature has been growing exponentially. How to extract the needed knowledge and to obtain new knowledge quickly and efficiently from massive articles has become a very important research area. The interaction between diseases and related proteins is one of the main research directions which mean a lot to disease prevention, diagnosis, therapy and designing biomedical experiment and so on [1].Liver cancer is one of the most common malignant tumors in China. It is reported that the number of people who die of liver cancer is about 110,000 every year and is 55% of the global patients. What's worse, the early symptoms of liver cancer are not apparent and the condition of patients deteriorates quickly. On average, patients can only live about 6 months after the cancer was diagnosed. Therefore, with all these mentioned above, research on liver cancer is necessary.So far, the existing study has been focusing on the interaction between individual protein and liver cancer. The present problem is how to integrate the experiment information gained and researched to help analyze molecular interactions, pathways and their influence on the occurrence, progression as well as prognosis of liver cancer.A method of identifying disease-gene associations based on literature mining which was applied in prostate cancer has genes related to diseases as prior knowledge, and the extracted genes must appear with at least one of seed genes in the same sentence, which leads to the limit of that method. Manuscript received January 24, 2013; revised March 24, 2013. Engineering, Soochow University, P. R. China (e-mail: liuxuan1220@ yahoo.cn, zouwei_198107@163.com, jjwang@suda.edu.cn).In order to solve this problem, we try to extract genes related to liver cancer without priors from the massive literatures and automatically build the interaction network between the corresponding proteins. II. METHODS A. Search for Literatures on Liver CancerBy searching the key words and free words of liver cancer in MeSH database of NCBI and collecting aliases of liver cancer from the reference [2] to conclude 14 aliases of liver cancer, such ...

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“…In order to identify the important proteins/molecules that form "Hub" molecules in the network, we used the connectivity and centrality measurement parameters of the network such as Degree, Closeness, Betweenness, and Eigenvector centrality [39,[86][87][88]. We calculated all these network parameter values for each protein of the network and extracted the significant proteins that had the parameter values higher than the corresponding average values.…”

Section: Computational Study Of Reconstructed Notch Signaling Pathwaymentioning

confidence: 99%

Drug Targets and Biomarker Identification from Computational Study of Human Notch Signaling Pathway

Chowdhury¹,

Sarkar²

2013

Clin Exp Pharmacol

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Notch signaling pathway is widely implicated in controlling various cellular functions, cell fate determination, and stem cell renewal in human but aberrant activity in cancer stem cells may cause different types of cancers. Understanding the complexity of this pathway to identify important targets for cancer therapy and to suppress the pathway activity without affecting the normal functions is of utmost importance to clinical and experimental pharmacologists. For developing therapeutic strategy, non availability of detailed molecular interactions, complex regulations and cross talks with other pathways pose a serious challenge to get a coherent understanding of this pathway. This motivated us to reconstruct the largest human cell specific Notch pathway with more number of molecules and interactions available from literatures and databases. To identify probable drug targets and biomarkers for cancer prognosis, we also performed computational study of the pathway using structural and logical analysis and identified important hub proteins, cross talks and feedback mechanisms. The model simulation is validated using reported mRNA expression profile in Glioblastoma cell line and the predictions not only show significant accuracy but also able to identify the undetermined expressions. From our simulation, to identify novel combinations of drug targetable proteins and better substitute for GAMMA SECRETASE inhibition, we proposed two alternative scenarios: partial suppression of Notch target proteins by NICD1 & HIF1A; and complete suppression by NICD1 & MAML, in Glioblastoma cell line. This reconstructed Notch signaling pathway and the computational analysis for identifying new biomarkers and combinatory drug targets will be useful for future in-vitro and in-vivo analysis to control different cancers.

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Identifying gene-disease associations using centrality on a literature mined gene-interaction network

Cited by 311 publications

References 42 publications

Big Data: Challenge and Opportunity for Translational and Industrial Research in Healthcare

Big Data: Challenge and Opportunity for Translational and Industrial Research in Healthcare

Liver Cancer Related Gene Analysis Based on Literature Mining

Drug Targets and Biomarker Identification from Computational Study of Human Notch Signaling Pathway

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