GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease

Zhou, Kaiyin; YuxingWang,; Zhang, Sheng; Gachloo, Mina; Kim, Jin-Dong; Luo, Qi; Cohen, K. Bretonnel; Xia, Jingbo

doi:10.3934/mbe.2019067

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…The baseline methods for task 1 or 2 was performed in Zhou et al’s work [65], while the “agonist vs. LOF” and “antagonist vs. GOF” hypothesis for the support of drug repurposing was proposed in Wang and Zhang’s work [18]. The development of the AGAC corpus [38] laid the basis for the data availability, while PubAnnotation [72] served as the evaluation platform.…”

Section: Resultsmentioning

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: Resultsmentioning

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 illuminative to be applied in drug-related knowledge discovery. For example, AGAC was successfully applied in LOF/GOF classification by using tensor decomposition algorithm [7]. As well, it has been adopted as the training data in a competition in the BioNLP open shared task 2019 [8], and applied to extract relevant literature for Alzhei-mer's disease for the support of gene disease association prediction [9].…”

Section: Agac As a Corpus For Key Annotations Labelingmentioning

confidence: 99%

LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19

et al. 2021

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Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.

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“…In addition, the literature review as well suggests that BioNLP and computational method shed light to drug-related knowledge discovery . In our early attempt of AGAC application (Zhou et al, 2019), a PubMed-wide GOF and LOF recognition is successfully achieved by using AGAC as training data. Specifically, AGAC corpus offers abundant semantic information in the function change recognition, and helps to evaluate the GOF/LOF topic of a Pubmed abstract.…”

Section: The Potential Of Latent Topic Annotationmentioning

confidence: 99%

“…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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GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease

Cited by 10 publications

References 11 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

LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19

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

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