Event extraction for systems biology by text mining the literature

Ananiadou, Sophia; Pyysalo, Sampo; Tsujii, Jun’ichi; Kell, Douglas B.

doi:10.1016/j.tibtech.2010.04.005

Cited by 179 publications

(135 citation statements)

References 73 publications

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“…Therefore, in the future, we will explore a word embedding-based deep learning method (Collobert et al, 2011;Bengio et al, 2003;Mikolov et al, 2013), since the word embeddings are learned automatically from large amounts of unlabelled data without using any NLP tools. In addition, our work focuses on the binary PPIs extraction from biomedical literature and biomedical event extraction (Ananiadou et al, 2010) can link pathways to literature evidence and aid pathway construction and enrichment (Dai et al, 2010). Therefore, we will introduce our DNN method into the biomedical event extraction task.…”

Section: Discussionmentioning

confidence: 99%

A protein-protein interaction extraction approach based on deep neural network

Zhao

Yang

Lin

et al. 2016

IJDMB

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Protein-Protein Interactions (PPIs) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Machine learning methods have been the most popular ones in PPI extraction area. However, these methods are still feature engineering-based, which means that their performances are also heavily dependent on the appropriate feature selection which is still a skill-dependent task. This paper presents a deep neural network-based approach which can learn complex and abstract features automatically from unlabelled data by unsupervised representation learning methods. This approach first employs the training algorithm of auto-encoders to initialise the parameters of a deep multilayer neural network. Then the gradient descent method using back propagation is applied to train this deep multilayer neural network model. Experimental results on five public PPI corpora show that our method can achieve better performance than can a multilayer neural network: on two 'toughest handling' corpora AImed and BioInfer, the former outperforms the latter with the improvements of 3.10 and 2.89 percentage units in F-score, respectively. In addition, the performance comparison with APG also verifies the effectiveness of our method.Keywords: deep learning; biomedical text mining; interaction extraction; neural network.Reference to this paper should be made as follows: Zhao, Z., Yang, Z., Lin, H., Wang, J. and Gao, S. (2016) 'A protein-protein interaction extraction approach based on deep neural network', Int.

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Section: Discussionmentioning

confidence: 99%

A protein-protein interaction extraction approach based on deep neural network

Zhao

Yang

Lin

et al. 2016

IJDMB

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“…Additionally, in medical science, text pattern extraction is performed to detect certain terminologies of the medical text that may be related to other common words which share similar context. According to [20], pattern matching approaches in biologyis diverse ranging from basic processes (e.g., sentence extraction) to complicated processes (e.g., part-of-speech or regex).…”

Section: Related Workmentioning

confidence: 99%

Feature extraction using regular expression in detecting proper noun for Malay news articles based on KNN algorithm

Sulaiman

Wahid

Morsidi

2018

J. Fundam and Appl Sci.

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Pendidikan Sultan Idris, 35900Tanjong Malim, Perak, MalaysiaPublished online: ABSTRACTThe identification of proper nouns from text aims to classify named entities according to their respective groupings, an aspect included in Named Entity Recognition (NER). Proper noun disambiguation can adversely affect morphological analysis, a vital trai availability via classification and new word assimilation. The occurrences of proper nouns can be annotated from the text resources using separate entity mapping from their fragments. research was carried out to examine the impa sequence that queried and acquired proper nouns from a collection of unannotated Malay language news articles that envisions several techniques to improve text accuracy such as pre-processing and data clustering. The results showed that the F the output tested on the unannotated news dataset were between 30% and 60%.

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“…With increasing number of scientific publications manual pathway curation is becoming more and more impossible. Therefore, Automated Pathway Curation (APC) and semi-automated biological knowledge extraction has been an active research area (Ananiadou et al, 2010;Szostak et al, 2015) trying to overcome the limitations of manual curation using various techniques from hand-crafted NLP systems (Allen et al, 2015) to machine learning techniques (Björne et al, 2011). Machine-learning NLP systems, in particular, show good performance in BioNLP tasks, but they are still performing less good in automated pathway curation, partly because there have been few attempts to measure the performance of NLP systems for APC directly.…”

Section: Introductionmentioning

confidence: 99%

BioNLP 2017

Cohen¹,

Demner‐Fushman²,

Ananiadou³

et al. 2017

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According to the Association for Computational Linguistics guidelines on special interest groups (SIGs), The function of a SIG is to encourage interest and activity in specific areas within the ACL's field [1]. Is the SIGBioMed special interest group "within the ACL's field"? The titles of this year's papers suggest that it is, in that the current interest in deep learning in its many and varied manifestations is mirrored in those titles. Do those papers cover a specific area? They do, and in doing so, they demonstrate one of the great satisfactions of working in biomedical natural language processing.One of the joys of involvement in the biomedical natural language processing community is seeing the development of research with clinical applications. As examples of such work being presented at BioNLP 2017, we would like to point out the two papers that discuss the application of natural language processing to the diagnosis of neurological disorders. Bhatia et al. [2] describe an approach to using speech processing in the assessment of patients with amyotrophic lateral sclerosis (also known as Lou Gehrig's disease), one of the more horrific motor neuron diseases. Good assessment of amyotrophic lateral sclerosis patients is important for a number of reasons, including the fact that accurate tracking of the inevitable deterioration that is a hallmark of this disease gives patients and their families the possibility of purposeful planning for the attendant disability and death. However, current methodologies for evaluating the status of amyotrophic lateral sclerosis patients necessarily involve expensive equipment and highly trained personnel; when further developed, this methodology could make such evaluation much more, and more frequently, available to ALS patients. The fact that the work reported here involves a speech modality is especially exciting, as speech-related indicators of future ALS can be present long before diagnosis. The paper uses measurements of phonological features of speech and their divergence from a baseline, and demonstrates correlation with physiological measures.Adams et al. [3] describe work on detecting and categorizing word production errors associated with anomia, a particular kind of inability to find words. Screening for anomia is important because anomia is a symptom of stroke, but it is difficult and time-consuming to do, and therefore is not done as often as it should be. Automatic detection of anomia could be a nice enabler of improved care for stroke victims, but it is made difficult due to the subtlety of the phonological and semantic judgments that have to be made when assessing the phenomenon. The paper uses a combination of language modeling and phonologically-based edit distance calculation to approach the task, applying these techniques to data from the AphasiaBank collection of transcribed aphasic and healthy speech.Although we have summarized only these two examples that address neurological disorders, there are several other papers on the use of natural language proc...

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Event extraction for systems biology by text mining the literature

Cited by 179 publications

References 73 publications

A protein-protein interaction extraction approach based on deep neural network

A protein-protein interaction extraction approach based on deep neural network

Feature extraction using regular expression in detecting proper noun for Malay news articles based on KNN algorithm

BioNLP 2017

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