scholarly journals FLUTE: Fast and reliable knowledge retrieval from biomedical literature

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Emilee Holtzapple ◽  
Cheryl A Telmer ◽  
Natasa Miskov-Zivanov
Keyword(s):  
Computational Modeling ◽  
Protein Interaction ◽  
State Of The Art ◽  
Biomedical Literature ◽  
Biomolecular Interactions ◽  
Accurate Model ◽  
Reading Methods ◽  
Reliable Knowledge ◽  
Machine Reading ◽  
Automated Methods

Abstract State-of-the-art machine reading methods extract, in hours, hundreds of thousands of events from the biomedical literature. However, many of the extracted biomolecular interactions are incorrect or not relevant for computational modeling of a system of interest. Therefore, rapid, automated methods are required to filter and select accurate and useful information. The FiLter for Understanding True Events (FLUTE) tool uses public protein interaction databases to filter interactions that have been extracted by machines from databases such as PubMed and score them for accuracy. Confidence in the interactions allows for rapid and accurate model assembly. As our results show, FLUTE can reliably determine the confidence in the biomolecular interactions extracted by fast machine readers and at the same time provide a speedup in interaction filtering by three orders of magnitude. Database URL: https://bitbucket.org/biodesignlab/flute.

Automated verification, assembly, and extension of GBM stem cell network model with knowledge from literature and data

2021 ◽  
Author(s):  
Emilee Holtzapple ◽  
Brent Cochran ◽  
Natasa Miskov-Zivanov
Keyword(s):  
Disease Progression ◽  
Large Scale ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Network Models ◽  
Signaling Networks ◽  
Cell Network ◽  
Network Verification ◽  
Machine Reading ◽  
Automated Methods

Signaling network models are usually assembled from information in literature and expert knowledge or inferred from data. The goal of modeling is to gain mechanistic understanding of key signaling pathways and provide predictions on how perturbations affect large-scale processes such as disease progression. For glioblastoma multiforme (GBM), this task is critical, given the lack of effective treatments and pace of disease progression. Both manual and automated assembly of signaling networks from data or literature have drawbacks. Existing GBM networks, as well as networks assembled using state-of-the-art machine reading, fall short when judged by the quality and quantity of information, as well as certain attributes of the overall network structure. The contributions of this work are two-fold. First, we propose an automated methodology for verification of signaling networks. Next, we discuss automation of network assembly and extension that relies on methods and resources used for network verification, thus, implicitly including verification in these processes. In addition to these methods, we also present, and verify a comprehensive GBM network assembled with a hybrid of manual and automated methods. Finally, we demonstrate that, while an automated network assembly is fast, such networks still lack precision and realistic network topology.

scholarly journals A Graph Convolutional Network–Based Method for Chemical-Protein Interaction Extraction: Algorithm Development

10.2196/17643 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e17643
Author(s):  
Erniu Wang ◽  
Fan Wang ◽  
Zhihao Yang ◽  
Lei Wang ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Language Processing ◽  
Protein Interaction ◽  
State Of The Art ◽  
Significance Test ◽  
Biomedical Literature ◽  
Convolutional Network ◽  
Data Set ◽  
Dependency Structure ◽  
Syntactic Information ◽  
Biomedical Texts

Background Extracting the interactions between chemicals and proteins from the biomedical literature is important for many biomedical tasks such as drug discovery, medicine precision, and knowledge graph construction. Several computational methods have been proposed for automatic chemical-protein interaction (CPI) extraction. However, the majority of these proposed models cannot effectively learn semantic and syntactic information from complex sentences in biomedical texts. Objective To relieve this problem, we propose a method to effectively encode syntactic information from long text for CPI extraction. Methods Since syntactic information can be captured from dependency graphs, graph convolutional networks (GCNs) have recently drawn increasing attention in natural language processing. To investigate the performance of a GCN on CPI extraction, this paper proposes a novel GCN-based model. The model can effectively capture sequential information and long-range syntactic relations between words by using the dependency structure of input sentences. Results We evaluated our model on the ChemProt corpus released by BioCreative VI; it achieved an F-score of 65.17%, which is 1.07% higher than that of the state-of-the-art system proposed by Peng et al. As indicated by the significance test (P<.001), the improvement is significant. It indicates that our model is effective in extracting CPIs. The GCN-based model can better capture the semantic and syntactic information of the sentence compared to other models, therefore alleviating the problems associated with the complexity of biomedical literature. Conclusions Our model can obtain more information from the dependency graph than previously proposed models. Experimental results suggest that it is competitive to state-of-the-art methods and significantly outperforms other methods on the ChemProt corpus, which is the benchmark data set for CPI extraction.

scholarly journals A Graph Convolutional Network–Based Method for Chemical-Protein Interaction Extraction: Algorithm Development (Preprint)

2019 ◽  
Author(s):  
Erniu Wang ◽  
Fan Wang ◽  
Zhihao Yang ◽  
Lei Wang ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Language Processing ◽  
Protein Interaction ◽  
State Of The Art ◽  
Significance Test ◽  
Biomedical Literature ◽  
Convolutional Network ◽  
Data Set ◽  
Dependency Structure ◽  
Syntactic Information ◽  
Biomedical Texts

BACKGROUND Extracting the interactions between chemicals and proteins from the biomedical literature is important for many biomedical tasks such as drug discovery, medicine precision, and knowledge graph construction. Several computational methods have been proposed for automatic chemical-protein interaction (CPI) extraction. However, the majority of these proposed models cannot effectively learn semantic and syntactic information from complex sentences in biomedical texts. OBJECTIVE To relieve this problem, we propose a method to effectively encode syntactic information from long text for CPI extraction. METHODS Since syntactic information can be captured from dependency graphs, graph convolutional networks (GCNs) have recently drawn increasing attention in natural language processing. To investigate the performance of a GCN on CPI extraction, this paper proposes a novel GCN-based model. The model can effectively capture sequential information and long-range syntactic relations between words by using the dependency structure of input sentences. RESULTS We evaluated our model on the ChemProt corpus released by BioCreative VI; it achieved an F-score of 65.17%, which is 1.07% higher than that of the state-of-the-art system proposed by Peng et al. As indicated by the significance test (<i>P</i>&lt;.001), the improvement is significant. It indicates that our model is effective in extracting CPIs. The GCN-based model can better capture the semantic and syntactic information of the sentence compared to other models, therefore alleviating the problems associated with the complexity of biomedical literature. CONCLUSIONS Our model can obtain more information from the dependency graph than previously proposed models. Experimental results suggest that it is competitive to state-of-the-art methods and significantly outperforms other methods on the ChemProt corpus, which is the benchmark data set for CPI extraction.

scholarly journals Integrate Candidate Answer Extraction with Re-Ranking for Chinese Machine Reading Comprehension

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 322
Author(s):  
Junjie Zeng ◽  
Xiaoya Sun ◽  
Qi Zhang ◽  
Xinmeng Li
Keyword(s):  
Reading Comprehension ◽  
Language Processing ◽  
State Of The Art ◽  
Language Model ◽  
Pipeline System ◽  
Challenging Problem ◽  
Reading Methods ◽  
Answer Extraction ◽  
Word Level ◽  
Machine Reading

Machine Reading Comprehension (MRC) research concerns how to endow machines with the ability to understand given passages and answer questions, which is a challenging problem in the field of natural language processing. To solve the Chinese MRC task efficiently, this paper proposes an Improved Extraction-based Reading Comprehension method with Answer Re-ranking (IERC-AR), consisting of a candidate answer extraction module and a re-ranking module. The candidate answer extraction module uses an improved pre-training language model, RoBERTa-WWM, to generate precise word representations, which can solve the problem of polysemy and is good for capturing Chinese word-level features. The re-ranking module re-evaluates candidate answers based on a self-attention mechanism, which can improve the accuracy of predicting answers. Traditional machine-reading methods generally integrate different modules into a pipeline system, which leads to re-encoding problems and inconsistent data distribution between the training and testing phases; therefore, this paper proposes an end-to-end model architecture for IERC-AR to reasonably integrate the candidate answer extraction and re-ranking modules. The experimental results on the Les MMRC dataset show that IERC-AR outperforms state-of-the-art MRC approaches.

scholarly journals An enhancement of the current design concepts for the improved consideration of residual stresses in fatigue-loaded welds

2021 ◽  
Vol 65 (4) ◽  
pp. 643-651
Author(s):  
Th. Nitschke-Pagel ◽  
J. Hensel
Keyword(s):  
Residual Stresses ◽  
State Of The Art ◽  
High Strength Steels ◽  
High Strength ◽  
Design Tools ◽  
Design Concepts ◽  
Treatment Processes ◽  
Real Distribution ◽  
Current Design ◽  
Reliable Knowledge

AbstractThe consideration of residual stresses in fatigue-loaded welds is currently done only qualitatively without reliable knowledge about their real distribution, amount and prefix. Therefore, the tools which enable a more or less unsafe consideration in design concepts are mainly based on unsafe experiences and doubtful assumptions. Since the use of explicitly determined residual stresses outside the welding community is state of the art, the target of the presented paper is to show a practicable way for an enhanced consideration of residual stresses in the current design tools. This is not only limited on residual stresses induced by welding, but also on post-weld treatment processes like HFMI or shot peening. Results of extended experiments with longitudinal fillet welds and butt welds of low and high strength steels evidently show that an improved use of residual stresses in fatigue strength approximation enables a better evaluation of peening processes as well as of material adjusted welding procedures or post-weld stress relief treatments. The concept shows that it is generally possible to overcome the existing extremely conservative but although unsafe rules and regulations and may also enable the improved use of high strength steels.

scholarly journals Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

2019 ◽  
Vol 35 (14) ◽  
pp. i305-i314 ◽  
Author(s):  
Muhao Chen ◽  
Chelsea J -T Ju ◽  
Guangyu Zhou ◽  
Xuelu Chen ◽  
Tianran Zhang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Mutual Influence ◽  
State Of The Art ◽  
Supplementary Information ◽  
Interaction Type ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
Ppi Prediction ◽  
Limited Coverage

AbstractMotivationSequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.ResultsWe present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.Availability and implementationThe implementation is available at https://github.com/muhaochen/seq_ppi.git.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature

2016 ◽  
Vol 23 (4) ◽  
pp. 766-772 ◽  
Author(s):  
Ayush Singhal ◽  
Michael Simmons ◽  
Zhiyong Lu
Keyword(s):  
Precision Medicine ◽  
State Of The Art ◽  
Biomedical Literature ◽  
Cancer Disease ◽  
Disease Mutation ◽  
Relationship Extraction ◽  
Disease Associations ◽  
Target Disease ◽  
Database Curation ◽  
Cancer Mutations

Abstract Objective Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine. Materials and Methods We developed a machine-learning (ML) based method to automatically identify the mutations mentioned in the biomedical literature related to a particular disease. In order to predict a relationship between the mutation and the target disease, several features, such as statistical features, distance features, and sentiment features, were constructed. Our ML model was trained with a pre-labeled dataset consisting of manually curated information about mutation-disease associations. The model was subsequently used to extract disease-related mutations from larger biomedical literature corpora. Results The performance of the proposed approach was assessed using a benchmarking dataset. Results show that our proposed approach gains significant improvement over the previous state of the art and obtains F-measures of 0.880 and 0.845 for prostate and breast cancer mutations, respectively. Discussion To demonstrate its utility, we applied our approach to all abstracts in PubMed for 3 diseases (including a non-cancer disease). The mutations extracted were then manually validated against human-curated databases. The validation results show that the proposed approach is useful in a real-world setting to extract uncurated disease mutations from the biomedical literature. Conclusions The proposed approach improves the state of the art for mutation-disease extraction from text. It is scalable and generalizable to identify mutations for any disease at a PubMed scale.

scholarly journals Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions

2019 ◽  
Vol 33 ◽  
pp. 6276-6283 ◽  
Author(s):  
Zhipeng Chen ◽  
Yiming Cui ◽  
Wentao Ma ◽  
Shijin Wang ◽  
Guoping Hu
Keyword(s):  
Reading Comprehension ◽  
Mutual Information ◽  
Spatial Attention ◽  
State Of The Art ◽  
Multiple Choice ◽  
Multiple Choice Questions ◽  
Attention Model ◽  
Novel Approach ◽  
Proposed Model ◽  
Machine Reading

Machine Reading Comprehension (MRC) with multiplechoice questions requires the machine to read given passage and select the correct answer among several candidates. In this paper, we propose a novel approach called Convolutional Spatial Attention (CSA) model which can better handle the MRC with multiple-choice questions. The proposed model could fully extract the mutual information among the passage, question, and the candidates, to form the enriched representations. Furthermore, to merge various attention results, we propose to use convolutional operation to dynamically summarize the attention values within the different size of regions. Experimental results show that the proposed model could give substantial improvements over various state-of- the-art systems on both RACE and SemEval-2018 Task11 datasets.

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