A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition

Gachloo, Mina; Wang, Yuxing; Xia, Jingbo

doi:10.5808/gi.2019.17.2.e18

Cited by 12 publications

(12 citation statements)

References 60 publications

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“…The AGAC track organizers develop an active gene annotation corpus (AGAC) (Wang et al, 2018b;Gachloo et al, 2019), for the sake of knowledge discovery in drug repurposing. The track corpus consists of 1250 PubMed abstracts: 250 for public, 1000 for final evaluation.…”

Section: Methodsmentioning

confidence: 99%

Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning

Xiong

et al. 2019

Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

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The prediction of the relationship between the disease with genes and its mutations is a very important knowledge extraction task that can potentially help drug discovery. In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019. Task 1 can be regarded as the traditional name entity recognition (NER), which cultivates molecular phenomena related to gene mutation. Task 2 can be regarded as relation extraction which captures the thematic roles between entities. For two tasks, we exploit the pre-trained biomedical language representation model (i.e., BERT) in the pipe of information extraction for the collection of mutation-disease knowledge from PubMed. And also, we design a fine-tuning technique and extra features by using multi-task learning. The experiment results show that our proposed approaches achieve 0.60 (ranks 1) and 0.25 (ranks 2) on task 1 and task 2 respectively in terms of F 1 metric.

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Section: Methodsmentioning

confidence: 99%

Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning

Xiong

et al. 2019

Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

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“…Owing to the rapid growth of biomedical literature in recent years, it is feasible to automatically extract knowledge from the published papers, such as the drugs and diseases they mention and their relations in a large scale [20,21]. The knowledge found can be used in many biomedical related fields such as drug discovery, safety monitoring, and drug side-effect detection [22,23]. Manually identifying such entities can be very accurate, but is also time consuming and laborious.…”

Section: Side Effect and Phenotype Miningmentioning

confidence: 99%

Hybrid phenotype mining method for investigating off-target protein and underlying side effects of anti-tumor immunotherapy

Zheng

Meng

Zweigenbaum

et al. 2020

BMC Med Inform Decis Mak

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Background: It is of utmost importance to investigate novel therapies for cancer, as it is a major cause of death. In recent years, immunotherapies, especially those against immune checkpoints, have been developed and brought significant improvement in cancer management. However, on the other hand, immune checkpoints blockade (ICB) by monoclonal antiboties may cause common and severe adverse reactions (ADRs), the cause of which remains largely undetermined. We hypothesize that ICB-agents may induce adverse reactions through off-target protein interactions, similar to the ADR-causing off-target effects of small molecules. In this study, we propose a hybrid phenotype mining approach which integrates molecular level information and provides new mechanistic insights for ICB-associated ADRs. Methods: We trained a conditional random fields model on the TAC 2017 benchmark training data, then used it to extract all drug-centric phenotypes for the five anti-PD-1/PD-L1 drugs from the drug labels of the DailyMed database. Proteins with structure similar to the drugs were obtained by using BlastP, and the gene targets of drugs were obtained from the STRING database. The target-centric phenotypes were extracted from the human phenotype ontology database. Finally, a screening module was designed to investigate off-target proteins, by making use of gene ontology analysis and pathway analysis. Results: Eventually, through the cross-analysis of the drug and target gene phenotypes, the off-target effect caused by the mutation of gene BTK was found, and the candidate side-effect off-target site was analyzed. Conclusions: This research provided a hybrid method of biomedical natural language processing and bioinformatics to investigate the off-target-based mechanism of ICB treatment. The method can also be applied for the investigation of ADRs related to other large molecule drugs.

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“…Drug repositioning is a relatively inexpensive and fast alternative to the lengthy and financially onerous task of new drug development [ 28 ]. Semantic relationship mining between a drug and other molecules or entities can also be used for drug-related knowledge discovery [ 29 ] and cooccurring entities analysis [ 30 ]. However, because these datasets could be stored in different places and in different ways, with different data formats and inconsistent representations of the same entity, the power of data mining across multiple datasets is far from being realized.…”

Section: Introductionmentioning

confidence: 99%

A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets

Zhang

et al. 2020

BMC Med Inform Decis Mak

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Background Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue. Methods First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets. Results and conclusions We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson’s disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.

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A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition

Cited by 12 publications

References 60 publications

Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning

Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning

Hybrid phenotype mining method for investigating off-target protein and underlying side effects of anti-tumor immunotherapy

A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets

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