LAITOR - Literature Assistant for Identification of Terms co-Occurrences and Relationships

Barbosa-Silva, Adriano; Soldatos, Theodoros; Magalhães, Ivan L. F.; Pavlopoulos, Georgios A.; Fontaine, Jean-Fred; Andrade‐Navarro, Miguel A.; Schneider, Reinhard; Ortega, Josè Miguel

doi:10.1186/1471-2105-11-70

Cited by 29 publications

(39 citation statements)

References 36 publications

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“…Of note, the data mining method implemented in PESCADOR is based on the LAITOR (Literature Assistant for Identification of Terms co-Occurrences and Relationships) tool [14] that ensures the users the analysis of the meaning of the text, not just the presence of key words. Actually, LAITOR identifies biointeraction terms in the text of the abstracts according to a dictionary of biointeraction terms [13], [14].…”

Section: Methodsmentioning

confidence: 99%

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Systems Biology Elucidates Common Pathogenic Mechanisms between Nonalcoholic and Alcoholic-Fatty Liver Disease

Sookoian

Pirola

2013

PLoS ONE

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The abnormal accumulation of fat in the liver is often related either to metabolic risk factors associated with metabolic syndrome in the absence of alcohol consumption (nonalcoholic fatty liver disease, NAFLD) or to chronic alcohol consumption (alcoholic fatty liver disease, AFLD). Clinical and histological studies suggest that NAFLD and AFLD share pathogenic mechanisms. Nevertheless, current data are still inconclusive as to whether the underlying biological process and disease pathways of NAFLD and AFLD are alike. Our primary aim was to integrate omics and physiological data to answer the question of whether NAFLD and AFLD share molecular processes that lead to disease development. We also explored the extent to which insulin resistance (IR) is a distinctive feature of NAFLD. To answer these questions, we used systems biology approaches, such as gene enrichment analysis, protein–protein interaction networks, and gene prioritization, based on multi-level data extracted by computational data mining. We observed that the leading disease pathways associated with NAFLD did not significantly differ from those of AFLD. However, systems biology revealed the importance of each molecular process behind each of the two diseases, and dissected distinctive molecular NAFLD and AFLD-signatures. Comparative co-analysis of NAFLD and AFLD clarified the participation of NAFLD, but not AFLD, in cardiovascular disease, and showed that insulin signaling is impaired in fatty liver regardless of the noxa, but the putative regulatory mechanisms associated with NAFLD seem to encompass a complex network of genes and proteins, plausible of epigenetic modifications. Gene prioritization showed a cancer-related functional map that suggests that the fatty transformation of the liver tissue is regardless of the cause, an emerging mechanism of ubiquitous oncogenic activation. In conclusion, similar underlying disease mechanisms lead to NAFLD and AFLD, but specific ones depict a particular disease signature that has a different impact on the systemic context.

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Section: Methodsmentioning

confidence: 99%

“…Actually, LAITOR identifies biointeraction terms in the text of the abstracts according to a dictionary of biointeraction terms [13], [14]. In addition, other available platforms are not flexible enough as PESCADOR to filter interactions extracted from a PubMed query.…”

Section: Methodsmentioning

confidence: 99%

Systems Biology Elucidates Common Pathogenic Mechanisms between Nonalcoholic and Alcoholic-Fatty Liver Disease

Sookoian

Pirola

2013

PLoS ONE

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“…Since two conceptsfor example, a gene and a chemical-can co-occur without there being a meaningful relationship between them, most co-occurrence-based approaches make use of algorithms that take the occurrence frequency of the concepts into account in some way [27][28][29][30]. There are currently two different approaches to this problem, one based on co-occurrence of terms and the other on natural language processing (NLP) techniques.…”

Section: Information Extractionmentioning

confidence: 99%

Bioinformatics Methods for Interpreting Toxicogenomics Data

Hettne

Kleinjans

Stierum

et al. 2014

Toxicogenomics-Based Cellular Models

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“…While the on-the-fly approach has high run times, it keeps the information available to the user as updated as possible. To circumvent this high runtime, other tools use only pre-processing of the documents [9], [6], [15] at the cost of providing information that is somewhat out of date. Pre-processing of texts/documents consists of analyzing the documents offline, extracting the relevant information and creating an adequate table to contain that information inside the database.…”

Section: Introductionmentioning

confidence: 98%

MetReS: A Metabolic Reconstruction Database for Cloud Computing

Vilaplana

Solsona

Teixidó

et al. 2014

2014 International Conference on Intelligent Networking and Collaborative Systems

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When designing a cloud infrastructure, it is critical to ensure beforehand that the system will be able to offer the desired level of QoS (Quality of Service). Our attention is focused here on efficient QoS accessing to a biological database in cloud computing systems.Our group developed two software applications that address important biological problems, Biblio-MetReS and Homol-MetReS. Biblio-MetReS is a data-mining tool that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. Reconstruction of molecular networks is essential to understand how organisms work at the molecular level and has strong implication, for example, in finding targets to treat different types of disease. In addition, the identification and functional annotation of the individual components of the network is crucial to understand what those targets might do in the context of the organism.These two software applications access the same database of organisms with annotated genes.The efficiency of the two applications is directly related to the design of the shared database. This database is continuously growing, as hundreds to thousands of new genomes are sequenced and annotated each year. The main goal of the current work was to improve the current database performance and to test if this improvement would scale to larger data-sets and more complex types of analysis that are not yet done by either of the applications. To achieve this goal, different database architectures were designed and analyzed. We started the study with a public relational database, MySQL, which was the current database server used by these applications. Then, due to the large size of the database, Apache Hadoop, a framework used for large-scale data processing, was considered and studied as an alternative.Although Big Data systems are not always a replacement of traditional relational databases, we proved by extensive tests the applicability of Apache Hadoop to a standard biological database containing some of the most frequently used types of information in molecular and systems biology. With time, as this database will continuously grow, the proposed solution will further improve its efficiency. Furthermore, this solution allows to extract additional valuable information from the data-sets that was not being currently considered.

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LAITOR - Literature Assistant for Identification of Terms co-Occurrences and Relationships

Cited by 29 publications

References 36 publications

Systems Biology Elucidates Common Pathogenic Mechanisms between Nonalcoholic and Alcoholic-Fatty Liver Disease

Systems Biology Elucidates Common Pathogenic Mechanisms between Nonalcoholic and Alcoholic-Fatty Liver Disease

Bioinformatics Methods for Interpreting Toxicogenomics Data

MetReS: A Metabolic Reconstruction Database for Cloud Computing

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