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

2019 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Mandi M. Roe ◽  
Gillian Reynolds ◽  
Indika Kahanda
Keyword(s):  
Relation Extraction ◽  
Biomedical Literature ◽  
Supervised Machine Learning ◽  
Human Phenotype ◽  
Unstructured Text ◽  
Gold Standard Dataset ◽  
Sentence Level ◽  
Machine Learning Approach ◽  
Human Proteins ◽  
Biomedical Relation Extraction

ABSTRACTThe MEDLINE database provides an extensive source of scientific articles and heterogeneous biomedical information in the form of unstructured text. One of the most important knowledge present within articles are the relations between human proteins and their phenotypes, which can stay hidden due to the exponential growth of publications. This has presented a range of opportunities for the development of computational methods to extract these biomedical relations from the articles. However, currently, no such method exists for the automated extraction of relations involving human proteins and human phenotype ontology (HPO) terms. In our previous work, we developed a comprehensive database composed of all co-mentions of proteins and phenotypes. In this study, we present a supervised machine learning approach called PPPred (Protein-Phenotype Predictor) for classifying the validity of a given sentence-level co-mention. Using an in-house developed gold standard dataset, we demonstrate that PPPred significantly outperforms several baseline methods. This two-step approach of co-mention extraction and classification constitutes a complete biomedical relation extraction pipeline for extracting protein-phenotype relations.CCS CONCEPTS•Computing methodologies → Information extraction; Supervised learning by classification; •Applied computing →Bioinformatics;

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

2020 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Katrina Lyon ◽  
Julia Schearer ◽  
Indika Kahanda
Keyword(s):  
Network Models ◽  
Ensemble Classifier ◽  
Biomedical Literature ◽  
Supervised Machine Learning ◽  
Neural Network Models ◽  
Unstructured Text ◽  
Sentence Level ◽  
Machine Learning Approach ◽  
Human Proteins ◽  
Types Of Information

AbstractThe 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 relationships 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 relationships 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.

scholarly journals Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study

10.2196/17644 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e17644
Author(s):  
Xiaofeng Liu ◽  
Jianye Fan ◽  
Shoubin Dong
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Long Distance ◽  
Replacement Method ◽  
Replacement Algorithm ◽  
Additional Noise ◽  
The Relationship ◽  
Document Level ◽  
Biomedical Relation Extraction

Background The most current methods applied for intrasentence relation extraction in the biomedical literature are inadequate for document-level relation extraction, in which the relationship may cross sentence boundaries. Hence, some approaches have been proposed to extract relations by splitting the document-level datasets through heuristic rules and learning methods. However, these approaches may introduce additional noise and do not really solve the problem of intersentence relation extraction. It is challenging to avoid noise and extract cross-sentence relations. Objective This study aimed to avoid errors by dividing the document-level dataset, verify that a self-attention structure can extract biomedical relations in a document with long-distance dependencies and complex semantics, and discuss the relative benefits of different entity pretreatment methods for biomedical relation extraction. Methods This paper proposes a new data preprocessing method and attempts to apply a pretrained self-attention structure for document biomedical relation extraction with an entity replacement method to capture very long-distance dependencies and complex semantics. Results Compared with state-of-the-art approaches, our method greatly improved the precision. The results show that our approach increases the F1 value, compared with state-of-the-art methods. Through experiments of biomedical entity pretreatments, we found that a model using an entity replacement method can improve performance. Conclusions When considering all target entity pairs as a whole in the document-level dataset, a pretrained self-attention structure is suitable to capture very long-distance dependencies and learn the textual context and complicated semantics. A replacement method for biomedical entities is conducive to biomedical relation extraction, especially to document-level relation extraction.

scholarly journals CovidBERT-Biomedical Relation Extraction for Covid-19

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Shashank Hebbar ◽  
Ying Xie
Keyword(s):  
Relation Extraction ◽  
Biomedical Literature ◽  
Medical Treatments ◽  
Relationship Extraction ◽  
Short Period ◽  
Approved Drugs ◽  
New Treatments ◽  
Extraction Model ◽  
Fine Tune ◽  
Biomedical Relation Extraction

Given the ongoing pandemic of Covid-19 which has had a devastating impact on society and the economy, and the explosive growth of biomedical literature, there has been a growing need to find suitable medical treatments and therapeutics in a short period of time. Developing new treatments and therapeutics can be expensive and a time consuming process. It can be practical to re-purpose existing approved drugs and put them in clinical trial. Hence we propose CovidBERT, a biomedical relationship extraction model based on BERT that extracts new relationships between various biomedical entities, namely gene-disease and chemical-disease relationships. We use the transformer architecture to train on Covid-19 related literature and fine-tune it using standard annotated datasets to show improvement in performance from baseline models. This research uses the transformer BERT model as its foundation and extracts relations from newly published biomedical papers.

scholarly journals A hybrid approach toward biomedical relation extraction training corpora: combining distant supervision with crowdsourcing

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Diana Sousa ◽  
Andre Lamurias ◽  
Francisco M Couto
Keyword(s):  
Hybrid Approach ◽  
Relation Extraction ◽  
Knowledge Bases ◽  
Amazon Mechanical Turk ◽  
Domain Expert ◽  
Human Phenotype ◽  
Distant Supervision ◽  
Original Dataset ◽  
Partial Domain ◽  
Biomedical Relation Extraction

Abstract Biomedical relation extraction (RE) datasets are vital in the construction of knowledge bases and to potentiate the discovery of new interactions. There are several ways to create biomedical RE datasets, some more reliable than others, such as resorting to domain expert annotations. However, the emerging use of crowdsourcing platforms, such as Amazon Mechanical Turk (MTurk), can potentially reduce the cost of RE dataset construction, even if the same level of quality cannot be guaranteed. There is a lack of power of the researcher to control who, how and in what context workers engage in crowdsourcing platforms. Hence, allying distant supervision with crowdsourcing can be a more reliable alternative. The crowdsourcing workers would be asked only to rectify or discard already existing annotations, which would make the process less dependent on their ability to interpret complex biomedical sentences. In this work, we use a previously created distantly supervised human phenotype–gene relations (PGR) dataset to perform crowdsourcing validation. We divided the original dataset into two annotation tasks: Task 1, 70% of the dataset annotated by one worker, and Task 2, 30% of the dataset annotated by seven workers. Also, for Task 2, we added an extra rater on-site and a domain expert to further assess the crowdsourcing validation quality. Here, we describe a detailed pipeline for RE crowdsourcing validation, creating a new release of the PGR dataset with partial domain expert revision, and assess the quality of the MTurk platform. We applied the new dataset to two state-of-the-art deep learning systems (BiOnt and BioBERT) and compared its performance with the original PGR dataset, as well as combinations between the two, achieving a 0.3494 increase in average F-measure. The code supporting our work and the new release of the PGR dataset is available at https://github.com/lasigeBioTM/PGR-crowd.

scholarly journals Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study (Preprint)

2019 ◽  
Author(s):  
Xiaofeng Liu ◽  
Jianye Fan ◽  
Shoubin Dong
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Long Distance ◽  
Replacement Method ◽  
Replacement Algorithm ◽  
Additional Noise ◽  
The Relationship ◽  
Document Level ◽  
Biomedical Relation Extraction

BACKGROUND The most current methods applied for intrasentence relation extraction in the biomedical literature are inadequate for document-level relation extraction, in which the relationship may cross sentence boundaries. Hence, some approaches have been proposed to extract relations by splitting the document-level datasets through heuristic rules and learning methods. However, these approaches may introduce additional noise and do not really solve the problem of intersentence relation extraction. It is challenging to avoid noise and extract cross-sentence relations. OBJECTIVE This study aimed to avoid errors by dividing the document-level dataset, verify that a self-attention structure can extract biomedical relations in a document with long-distance dependencies and complex semantics, and discuss the relative benefits of different entity pretreatment methods for biomedical relation extraction. METHODS This paper proposes a new data preprocessing method and attempts to apply a pretrained self-attention structure for document biomedical relation extraction with an entity replacement method to capture very long-distance dependencies and complex semantics. RESULTS Compared with state-of-the-art approaches, our method greatly improved the precision. The results show that our approach increases the F1 value, compared with state-of-the-art methods. Through experiments of biomedical entity pretreatments, we found that a model using an entity replacement method can improve performance. CONCLUSIONS When considering all target entity pairs as a whole in the document-level dataset, a pretrained self-attention structure is suitable to capture very long-distance dependencies and learn the textual context and complicated semantics. A replacement method for biomedical entities is conducive to biomedical relation extraction, especially to document-level relation extraction.

scholarly journals Towards Unsupervised Learning of Temporal Relations between Events

2012 ◽  
Vol 45 ◽  
pp. 125-163 ◽  
Author(s):  
S.A. Mirroshandel ◽  
G. Ghassem-Sani
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Relation Extraction ◽  
Temporal Relation ◽  
Supervised Machine Learning ◽  
Temporal Relations ◽  
Machine Learning Approach ◽  
General Classifier ◽  
Weakly Supervised ◽  
Temporal Relation Extraction

Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as Question Answering, Information Extraction, and Summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, we have concentrated our efforts to reduce the need for annotated data as much as possible. This paper presents two different algorithms towards this goal. The first algorithm is a weakly supervised machine learning approach for classification of temporal relations between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, inspired by the hypothesis of "one type of temporal relation per discourse'', it extracts useful information from a cluster of topically related documents. We show that by combining the global information of such a cluster with local decisions of a general classifier, a bootstrapping cross-document classifier can be built to extract temporal relations between events. Our experiments show that without any additional annotated data, the accuracy of the proposed algorithm is higher than that of several previous successful systems. The second proposed method for temporal relation extraction is based on the expectation maximization (EM) algorithm. Within EM, we used different techniques such as a greedy best-first search and integer linear programming for temporal inconsistency removal. We think that the experimental results of our EM based algorithm, as a first step toward a fully unsupervised temporal relation extraction method, is encouraging.

scholarly journals Biomedical relation extraction via knowledge-enhanced reading comprehension

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Jing Chen ◽  
Baotian Hu ◽  
Weihua Peng ◽  
Qingcai Chen ◽  
Buzhou Tang
Keyword(s):  
Reading Comprehension ◽  
Knowledge Representation ◽  
Knowledge Integration ◽  
Question Answering ◽  
Interaction Mechanism ◽  
Relation Extraction ◽  
Knowledge Bases ◽  
Biomedical Literature ◽  
Main Research ◽  
Biomedical Relation Extraction

Abstract Background In biomedical research, chemical and disease relation extraction from unstructured biomedical literature is an essential task. Effective context understanding and knowledge integration are two main research problems in this task. Most work of relation extraction focuses on classification for entity mention pairs. Inspired by the effectiveness of machine reading comprehension (RC) in the respect of context understanding, solving biomedical relation extraction with the RC framework at both intra-sentential and inter-sentential levels is a new topic worthy to be explored. Except for the unstructured biomedical text, many structured knowledge bases (KBs) provide valuable guidance for biomedical relation extraction. Utilizing knowledge in the RC framework is also worthy to be investigated. We propose a knowledge-enhanced reading comprehension (KRC) framework to leverage reading comprehension and prior knowledge for biomedical relation extraction. First, we generate questions for each relation, which reformulates the relation extraction task to a question answering task. Second, based on the RC framework, we integrate knowledge representation through an efficient knowledge-enhanced attention interaction mechanism to guide the biomedical relation extraction. Results The proposed model was evaluated on the BioCreative V CDR dataset and CHR dataset. Experiments show that our model achieved a competitive document-level F1 of 71.18% and 93.3%, respectively, compared with other methods. Conclusion Result analysis reveals that open-domain reading comprehension data and knowledge representation can help improve biomedical relation extraction in our proposed KRC framework. Our work can encourage more research on bridging reading comprehension and biomedical relation extraction and promote the biomedical relation extraction.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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