Guideline Design of an Active Gene Annotation Corpus for the Purpose of Drug Repurposing

Wang, Yuxing; Yao, Xinzhi; Zhou, Kaiyin; Qin, Xuan; Kim, Jin-Dong; Cohen, K. Bretonnel; Xia, Jingbo

doi:10.1109/cisp-bmei.2018.8633253

Cited by 11 publications

(12 citation statements)

References 6 publications

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“…Until now, the corpus working on drug repurposing was rare. Recent progress came from Wang et al’s work [38], which designed an “active gene annotation corpus (AGAC)” to cultivate functional change of mutated genes. AGAC aimed to capture LOF- or GOF-mutated genes, and made it possible to find “agonist vs. LOF” and “antagonist vs. GOF” pairs for “drug vs. gene.” This was a nice addition to a mutation-centric corpus for the purpose of drug repurposing [38].…”

Section: In Silico Methods For Drug Knowledge Discoverymentioning

confidence: 99%

“…The above methods mainly fulfilled tensor axes with various drug-related domain data like gene expression or chemical features, and then a novel link discovery was mined out from the decomposed tensor. Meanwhile, a hybrid strategy of BioNLP and tensor decompostion came from Zhou et al [65], who used AGAC corpus [38] as a training set to perform OMIM-wide text mining, and predict novel higher order links among five entities, including genes, mutations, functions, diseases, and functional changes. In this work, new nonzero cells in the decomposed tensors were treated as novel links, among five entities, and infer the functional change of a mutated gene.…”

Section: In Silico Methods For Drug Knowledge Discoverymentioning

confidence: 99%

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

2019

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2019, Korea Genome Organization This is an open-access article distributed under the terms of the Creative Commons Attribution license (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different resources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or tensor decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.

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Section: In Silico Methods For Drug Knowledge Discoverymentioning

confidence: 99%

Section: In Silico Methods For Drug Knowledge Discoverymentioning

confidence: 99%

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

2019

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“…AGAC is a corpus annotated by human experts, with an aim at capturing function changes of mutated genes in a pathogenic context. The design of the corpus and the guidelines were published in 2017 (Wang et al, 2018), and a case study of using such an annotated corpus for drug repurposing was successfully performed in 2019, unveiling potential associations of variations with a wide spectrum of human diseases (Zhou et al, 2019). Since then, the whole annotation work took 20 months, with involvement of four annotators.…”

Section: Introductionmentioning

confidence: 99%

An Overview of the Active Gene Annotation Corpus and the BioNLP OST 2019 AGAC Track Tasks

Wang¹,

Zhou²,

Gachloo³

et al. 2019

Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

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The active gene annotation corpus (AGAC) was developed to support knowledge discovery for drug repurposing. The AGAC track of the BioNLP Open Shared Tasks 2019 was organized, to facilitate cross-disciplinary collaboration across BioNLP and Pharmacoinformatics communities, for drug repurposing. The AGAC track consists of three subtasks: 1) named entity recognition, 2) thematic relation extraction, and 3) loss of function (LOF) / gain of function (GOF) topic classification. The AGAC track was participated by five teams, of which the performance is compared and analyzed. The results revealed a substantial room for improvement in the design of the task, which we analyzed in terms of "imbalanced data", "selective annotation" and "latent topic annotation".

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“…Using the natural language processing methods to discover and mine drug-related knowledge from text has been a hot topic in recent years. For the goal of drug repurposing, an active gene annotation corpus (AGAC) was developed as a benchmark dataset (Wang et al, 2018b). The AGAC track is part of the BioNLP Open Shared Task 2019, aims to gather text mining approaches among the BioNLP community to propel drugoriented knowledge discovery.…”

Section: Introductionmentioning

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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Guideline Design of an Active Gene Annotation Corpus for the Purpose of Drug Repurposing

Cited by 11 publications

References 6 publications

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

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

An Overview of the Active Gene Annotation Corpus and the BioNLP OST 2019 AGAC Track Tasks

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

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