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

Abstract

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.

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

“…In this section, we discuss methods that do not fit in either of the above categories but provide interesting approaches. In Zhou and Fu ( 2018 ), an extended variant of the frequency approach is studied, which combines co-occurrence frequency and Inverse Document Frequency (IDF) for relations extraction. The study sets the first precedence to entity co-occurrence in MeSH terms and second to those in the article title, and third to the ones in the article abstract by assigning weights to each precedence level.…”

Expanding a Database-derived Biomedical Knowledge Graph via Multi-relation Extraction from Biomedical Abstracts

“…For example, a possible extractor would say gene X is associated with disease Y, because gene X and disease Y appear together more often than individually [20]. This approach has been used to establish the following relationship types: disease-gene relationships [20,21,22,23,24,25], protein-protein interactions [24,26,27], drug-disease treatments [28], and tissue-gene relations [29]. Extractors using the co-occurrence strategy provide exceptional recall results; however, these methods may fail to detect underreported relationships, because they depend on entity-pair frequency for detection.…”

CLDC: Efficient Classification of Medical Data Using Class Level Disease Convergence Divergence Measure

“…The method predicts the chronic disease based on frequency measures on the community data. The association of gene on occurrence of any disease using text mining is presented in [16]. The method computes term frequency and term weight for each genes and their associated.…”