ANDSystem: an Associative Network Discovery System for Automated Literature Mining in the Field of Biology

Overview

Journal BMC Syst Biol

Publisher Biomed Central

Specialty Biology

Date 2015 Apr 17

PMID 25881313

Citations 29

Authors

Vladimir A Ivanisenko

Olga V Saik

Nikita V Ivanisenko

Evgeny S Tiys

Timofey V Ivanisenko

Pavel S Demenkov

Nikolay A Kolchanov

Affiliations

Soon will be listed here.

Abstract

Background: Sufficient knowledge of molecular and genetic interactions, which comprise the entire basis of the functioning of living systems, is one of the necessary requirements for successfully answering almost any research question in the field of biology and medicine. To date, more than 24 million scientific papers can be found in PubMed, with many of them containing descriptions of a wide range of biological processes. The analysis of such tremendous amounts of data requires the use of automated text-mining approaches. Although a handful of tools have recently been developed to meet this need, none of them provide error-free extraction of highly detailed information.

Results: The ANDSystem package was developed for the reconstruction and analysis of molecular genetic networks based on an automated text-mining technique. It provides a detailed description of the various types of interactions between genes, proteins, microRNA's, metabolites, cellular components, pathways and diseases, taking into account the specificity of cell lines and organisms. Although the accuracy of ANDSystem is comparable to other well known text-mining tools, such as Pathway Studio and STRING, it outperforms them in having the ability to identify an increased number of interaction types.

Conclusion: The use of ANDSystem, in combination with Pathway Studio and STRING, can improve the quality of the automated reconstruction of molecular and genetic networks. ANDSystem should provide a useful tool for researchers working in a number of different fields, including biology, biotechnology, pharmacology and medicine.

Citing Articles

An Accurate and Efficient Approach to Knowledge Extraction from Scientific Publications Using Structured Ontology Models, Graph Neural Networks, and Large Language Models.

Ivanisenko T, Demenkov P, Ivanisenko V Int J Mol Sci. 2024; 25(21).

PMID: 39519363 PMC: 11546091. DOI: 10.3390/ijms252111811.

Correlation between proteome changes and synchrony of cardiac electrical excitation under 3-day «dry immersion» conditions.

Pastushkova L, Goncharova A, Rusanov V, Nosovsky A, Kashirina D, Popova O Front Physiol. 2023; 14:1285802.

PMID: 38107479 PMC: 10722197. DOI: 10.3389/fphys.2023.1285802.

Reconstruction of the regulatory hypermethylation network controlling hepatocellular carcinoma development during hepatitis C viral infection.

Antropova E, Khlebodarova T, Demenkov P, Volianskaia A, Venzel A, Ivanisenko N J Integr Bioinform. 2023; 20(3).

PMID: 37978846 PMC: 10757076. DOI: 10.1515/jib-2023-0013.

Associative gene networks reveal novel candidates important for ADHD and dyslexia comorbidity.

Hongyao H, Chun J, Xiaoyan G, Fangfang L, Jing Z, Lin Z BMC Med Genomics. 2023; 16(1):208.

PMID: 37667328 PMC: 10478365. DOI: 10.1186/s12920-023-01502-1.

Apoptosis Genes as a Key to Identification of Inverse Comorbidity of Huntington's Disease and Cancer.

Bragina E, Gomboeva D, Saik O, Ivanisenko V, Freidin M, Nazarenko M Int J Mol Sci. 2023; 24(11).

PMID: 37298337 PMC: 10253782. DOI: 10.3390/ijms24119385.

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