A guide for building biological pathways along with two case studies: hair and breast development

Trindade, Daniel; Orsine, Lissur Azevedo; Barbosa-Silva, Adriano; Donnard, Elisa; Ortega, Josè Miguel

doi:10.1016/j.ymeth.2014.10.006

Cited by 5 publications

(5 citation statements)

References 141 publications

(222 reference statements)

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“…Here, we propose a parallel, fast and unlimited text mining approach by adding customized programming functions suitable for HPC (high-performance computing). Text mining tools have been a valuable approach to support systems biology, not only for updating databases, but also for providing ab initio annotation of new pathways, by using automated text processing [ 12 , 13 ]. To our knowledge, only STRING [ 14 – 17 ] has a programmatic version, but with a different approach than the one proposed here.…”

Section: Introductionmentioning

confidence: 99%

LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks

et al. 2020

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Background: The amount of published full-text articles has increased dramatically. Text mining tools configure an essential approach to building biological networks, updating databases and providing annotation for new pathways. PESCADOR is an online web server based on LAITOR and NLProt text mining tools, which retrieves protein-protein co-occurrences in a tabular-based format, adding a network schema. Here we present an HPC-oriented version of PESCADOR's native text mining tool, renamed to LAITOR4HPC, aiming to access an unlimited abstract amount in a short time to enrich available networks, build new ones and possibly highlight whether fields of research have been exhaustively studied. Results: By taking advantage of parallel computing HPC infrastructure, the full collection of MEDLINE abstracts available until June 2017 was analyzed in a shorter period (6 days) when compared to the original online implementation (with an estimated 2 years to run the same data). Additionally, three case studies were presented to illustrate LAITOR4HPC usage possibilities. The first case study targeted soybean and was used to retrieve an overview of published co-occurrences in a single organism, retrieving 15,788 proteins in 7894 cooccurrences. In the second case study, a target gene family was searched in many organisms, by analyzing 15 species under biotic stress. Most co-occurrences regarded Arabidopsis thaliana and Zea mays. The third case study concerned the construction and enrichment of an available pathway. Choosing A. thaliana for further analysis, the defensin pathway was enriched, showing additional signaling and regulation molecules, and how they respond to each other in the modulation of this complex plant defense response. Conclusions: LAITOR4HPC can be used for an efficient text mining based construction of biological networks derived from big data sources, such as MEDLINE abstracts. Time consumption and data input limitations will depend on the available resources at the HPC facility. LAITOR4HPC enables enough flexibility for different approaches and data amounts targeted to an organism, a subject, or a specific pathway. Additionally, it can deliver comprehensive results where interactions are classified into four types, according to their reliability.

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Section: Introductionmentioning

confidence: 99%

LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks

et al. 2020

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“…Bioinformatics efforts, one the other hand, could speed up our understanding of the molecular mechanism of COPD. Some of these approaches have focused on mining relevant knowledge from medical literature [19–21], and building the biological pathways through visualization [22]. Novel methods such as Ontology Fingerprints [23] have been successfully used to infer active signaling pathways in cancer cells [24], to develop biological networks [25], and to help with personalized cancer therapy [26].…”

Section: Introductionmentioning

confidence: 99%

Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD

Chen

Jia

Zhu

et al. 2019

BMC Med Inform Decis Mak

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BackgroundDisease comorbidity is very common and has significant impact on disease treatment. Revealing the associations among diseases may help to understand the mechanisms of diseases, improve the prevention and treatment of diseases, and support the discovery of new drugs or new uses of existing drugs.MethodsIn this paper, we introduced a mathematical model to represent gene related diseases with a series of associated genes based on the overrepresentation of genes and diseases in PubMed literature. We also illustrated an efficient way to reveal the implicit connections between COPD and other diseases based on this model.ResultsWe applied this approach to analyze the relationships between Chronic Obstructive Pulmonary Disease (COPD) and other diseases under the Lung diseases branch in the Medical subject heading index system and detected 4 novel diseases relevant to COPD. As judged by domain experts, the F score of our approach is up to 77.6%.ConclusionsThe results demonstrate the effectiveness of the gene fingerprint model for diseases on the basis of medical literature.

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“…Text mining method had been applied in studying various biological problems like functional genomics [ 9 ], biological pathways [ 10 ], protein-protein interactions [ 11 ], protein representation [ 12 ], drug-gene association [ 13 ], comparative toxicogenomics [ 14 , 15 ], neuropsychiatric disorder [ 16 ], and other areas in the biomedical domain [ 17 ] including large-scale bioinformatics analyses [ 8 , 18 – 32 ]. DISEASES predicted the association through the co-occurrence method [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

The research on gene-disease association based on text-mining of PubMed

Zhou

2018

BMC Bioinformatics

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BackgroundThe associations between genes and diseases are of critical significance in aspects of prevention, diagnosis and treatment. Although gene-disease relationships have been investigated extensively, much of the underpinnings of these associations are yet to be elucidated.MethodsA novel method integrates MeSH database, term weight (TW), and co-occurrence methods to predict gene-disease associations based on the cosine similarity between gene vectors and disease vectors. Vectors are transformed from the texts of documents in the PubMed database according to the appearance and location of the gene or disease terms. The disease related text data has been optimized during the process of constructing vectors.ResultsThe overall distribution of cosine similarity value was investigated. By using the gene-disease association data in OMIM database as golden standard, the performance of cosine similarity in predicting gene-disease linkage was evaluated. The effects of applying weight matrix, penalty weights for keywords (PWK), and normalization were also investigated. Finally, we demonstrated that our method outperforms heterogeneous network edge prediction (HNEP) in aspects of precision rate and recall rate.ConclusionsOur method proposed in this paper is easy to be conducted and the results can be integrated with other models to improve the overall performance of gene-disease association predictions.

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A guide for building biological pathways along with two case studies: hair and breast development

Cited by 5 publications

References 141 publications

LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks

LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks

Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD

The research on gene-disease association based on text-mining of PubMed

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