PPPred: Classifying Protein-phenotype Co-mentions Extracted from Biomedical Literature

Shahri, Morteza Pourreza; Reynolds,; Kahanda, Indika

doi:10.1101/654475

Cited by 2 publications

(6 citation statements)

References 36 publications

(37 reference statements)

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“…Despite a large number of studies conducted on extracting entity relationships from the biomedical literature (including a handful of methods for extracting relationships between genes/proteins and phenotypes), the only method designed explicitly for human protein-HPO term relationship extraction is PPPred, which is a traditional machine learning classifier that was previously developed by our lab [31].…”

Section: Related Workmentioning

confidence: 99%

“…Aligning with our previous work [31], we formulate the task of co-mention classification as a supervised learning problem as described below.…”

Section: Approachmentioning

confidence: 99%

“…In our previous work [31], we used ProPheno 1.0 [30], which is a dataset of proteins-phenotype co-mentions extracted from the biomedical literature. This dataset maps the proteins and phenotypes to the corresponding UniProt 5 IDs and HPO IDs.…”

Section: Datamentioning

confidence: 99%

“…As a solution to the above, in a recent study [31], we proposed a novel two-step approach for extracting human protein-HPO term relationships. The first step was to extract protein-HPO co-mentions, which are co-occurrences of protein names and phenotype names in a particular span of text, i.e., a sentence, a paragraph, etc [28].…”

mentioning

confidence: 99%

“…The combination of a co-mention extractor and a co-mention classifier/ filter constitutes a complete relationship extraction pipeline. In the same study, [31], we developed PPPred (Protein-Phenotype Predictor), a co-mention classifier for classifying protein-phenotype co-mentions. We first randomly selected a subset of co-mentions from the ProPheno database and curated it with the assistance of biologists.…”

mentioning

confidence: 99%

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DeepPPPred: An Ensemble of BERT, CNN, and RNN for Classifying Co-mentions of Proteins and Phenotypes

Shahri

Lyon

Schearer

et al. 2020

Preprint

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The biomedical literature provides an extensive source of information in the form of unstructured text. One of the most important types of information hidden in biomedical literature is the relations between human proteins and their phenotypes, which, due to the exponential growth of publications, can remain hidden. This provides a range of opportunities for the development of computational methods to extract the biomedical relations from the unstructured text. In our previous work, we developed a supervised machine learning approach, called PPPred, for classifying the validity of a given sentence-level human protein-phenotype co-mention. In this work, we propose DeepPPPred, an ensemble classifier composed of PPPred and three deep neural network models: RNN, CNN, and BERT. Using an expanded gold-standard co-mention dataset, we demonstrate that the proposed ensemble method significantly outperforms its constituent components and provides a new state-of-the-art performance on classifying the co-mentions of human proteins and phenotype terms.

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Section: Related Workmentioning

confidence: 99%

“…Aligning with our previous work [31], we formulate the task of co-mention classification as a supervised learning problem as described below.…”

Section: Approachmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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DeepPPPred: An Ensemble of BERT, CNN, and RNN for Classifying Co-mentions of Proteins and Phenotypes

Shahri

Lyon

Schearer

et al. 2020

Preprint

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A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

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A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume and scope. Since no recent survey exists to overview how SSL has been used in text classification, we aim to fill this gap and present an up-to-date review of SSL for text classification. We retrieve 1794 works from the last 5 years from IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Then, 157 articles were selected to be included in this review. We present the application domain, datasets, and languages employed in the works. The text representations and machine learning algorithms. We also summarize and organize the works following a recent taxonomy of SSL. We analyze the percentage of labeled data used, the evaluation metrics, and obtained results. Lastly, we present some limitations and future trends in the area. We aim to provide researchers and practitioners with an outline of the area as well as useful information for their current research.

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PPPred: Classifying Protein-phenotype Co-mentions Extracted from Biomedical Literature

Cited by 2 publications

References 36 publications

DeepPPPred: An Ensemble of BERT, CNN, and RNN for Classifying Co-mentions of Proteins and Phenotypes

DeepPPPred: An Ensemble of BERT, CNN, and RNN for Classifying Co-mentions of Proteins and Phenotypes

A review of semi-supervised learning for text classification

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