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Extracting chemical–protein relations with ensembles of SVM and deep learning models

Yifan Peng, Anthony Rios, Ramakanth Kavuluru, Zhiyong Lu
2018 Database: The Journal of Biological Databases and Curation  
Extracting chemical-protein relations with ensembles of SVM and deep learning models.  ...  Abstract Mining relations between chemicals and proteins from the biomedical literature is an increasingly important task.  ...  Funding This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine [Y.P. and Z.L.] and through an extramural National Library of Medicine  ... 
doi:10.1093/database/bay073 pmid:30020437 pmcid:PMC6051439 fatcat:6qil2rmfqvbizg2uvfwelxn35i

De novo Prediction of Moonlighting Proteins Using Multimodal Deep Ensemble Learning

Ying Li, Jianing Zhao, Zhaoqian Liu, Cankun Wang, Lizheng Wei, Siyu Han, Wei Du
2021 Frontiers in Genetics  
First, we extract four sequence-based features: primary protein sequence information, evolutionary information, physical and chemical properties, and secondary protein structure information.  ...  In this paper, we propose a multimodal deep ensemble learning architecture, named MEL-MP, which is the first de novo computation model for predicting MPs.  ...  AUTHOR CONTRIBUTIONS YL designed the research plan and checked and revised the manuscript. JZ collected and analyzed the data and checked and revised the manuscript.  ... 
doi:10.3389/fgene.2021.630379 pmid:33828582 pmcid:PMC8019903 fatcat:kyjbbcnpnfh6rllqnpbleputeu

Potent pairing: ensemble of long short-term memory networks and support vector machine for chemical-protein relation extraction

Farrokh Mehryary, Jari Björne, Tapio Salakoski, Filip Ginter
2018 Database: The Journal of Biological Databases and Curation  
We participated in this challenge with a Support Vector Machine (SVM) system and a deep learning-based system (ST-ANN), and achieved an F-score of 60.99 for the task.  ...  Biomedical researchers regularly discover new interactions between chemical compounds/drugs and genes/proteins, and report them in research literature.  ...  Acknowledgements We would like to thank the anonymous reviewers who helped us to improve this paper with their valuable recommendations and feedback.  ... 
doi:10.1093/database/bay120 pmid:30576487 pmcid:PMC6310522 fatcat:pvke3q6hrzco3jt4l44ghxes44

A systematic review of computational methods for predicting long noncoding RNAs

Xinran Xu, Shuai Liu, Zhihao Yang, Xiaohan Zhao, Yaozhen Deng, Guangzhan Zhang, Jian Pang, Chengshuai Zhao, Wen Zhang
2021 Briefings in Functional Genomics  
methods based on binary classifiers, deep learning and ensemble learning.  ...  In this review, we introduce databases and features involved in the development of computational prediction models, and subsequently summarize existing state-of-the-art computational methods, including  ...  After extracting features from different categories, classifiers are constructed based on five algorithms, SVM, LR, RF, extreme learning machine and deep learning, and then LncFinder evaluated the performance  ... 
doi:10.1093/bfgp/elab016 pmid:33754153 fatcat:zj5uppfkrjdczdtnnjuxemzcke

Set of Approaches Based on Position Specific Scoring Matrix and Amino Acid Sequence for Primary Category Enzyme Classification

L. Nanni, DEI, University of Padua, viale Gradenigo 6, Padua, Italy, S. Brahnam, Information Technology and Cybersecurity, Missouri State University, 901 S. National, Springfield, MO 65804, USA
2020 Journal of Artificial Intelligence and Systems  
Each protein descriptor is classified by a Support Vector Machine (SVM), with the set of SVMs finally combined by sum rule.  ...  Protein databases, combined with functional annotations and machine learning (ML) techniques, offer many potential benefits, including significantly facilitating rapid pharmacological target identification  ...  Excellent results were achieved in [25] when protein structure and chemical information were combined with sequential information into a graph model from which features were extracted and classified  ... 
doi:10.33969/ais.2020.21004 fatcat:ic7kndsiqjdtpia5tpvwxxnnni

A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

Liyang Wang, Dantong Niu, Xinjie Zhao, Xiaoya Wang, Mengzhen Hao, Huilian Che
2021 Foods  
Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food proteins, this work proposed to introduce deep learning model—transformer  ...  The results of 5-fold cross-validation showed that the area under the receiver operating characteristic curve (AUC) of the deep model was the highest (0.9578), which was better than the ensemble learning  ...  Among them, the SVM achieved an accuracy of 0.7418 with an F1 score of 0.7303, and its AUC was 0.8457, which cannot make Compared with deep learning and ensemble learning models, the performance of the  ... 
doi:10.3390/foods10040809 pmid:33918556 fatcat:xqslbqqkzzhjtf2gbb6f26kjtq

Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes

Morteza Pourreza Shahri, Indika Kahanda
2021 BMC Bioinformatics  
Results In this study, we propose a novel deep semi-supervised ensemble framework that combines deep neural networks, semi-supervised, and ensemble learning for classifying human protein-phenotype co-mentions  ...  Conclusions This article presents a novel approach for human protein-phenotype co-mention classification based on deep, semi-supervised, and ensemble learning.  ...  Diane Bimczok from the department of Microbiology and Immunology at Montana State University for her guidance during this project.  ... 
doi:10.1186/s12859-021-04421-z pmid:34656098 fatcat:yr2nylu6uvf43ff4mms535arjq

CU-UD: text-mining drug and chemical-protein interactions with ensembles of BERT-based models [article]

Mehmet Efruz Karabulut, K. Vijay-Shanker, Yifan Peng
2021 arXiv   pre-print
Identifying the relations between chemicals and proteins is an important text mining task.  ...  between chemicals and proteins.  ...  Extracting chemi- 2 Stacking (MLP) 0.7700 0.7838 0.7764 cal–protein relations with ensembles of SVM and deep learning models. 3 Stacking (MLP)+Majority  ... 
arXiv:2112.03004v1 fatcat:svpjh324uzcm3gsdot4nctvuzi

A hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions

Bipin Nair B.J, Lijo Joy
2018 International Journal of Engineering & Technology  
In our research work we will collect the data of drugs as well as protein regarding hematic diseases, then applying feature extraction as well as classification, predict hot spot and non-hot spot then  ...  finally using machine learning algorithm predicting which drug will interact with the help of a hybrid approach.  ...  of machine learning in bioinformatics for the prediction of protein hotspots and drug, some of the related the work are summarized here.  ... 
doi:10.14419/ijet.v7i1.9.9752 fatcat:lh34szujqvecpokepx5kw7b774

Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs

Muhammad Nabeel Asim, Muhammad Ali Ibrahim, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed
2021 International Journal of Molecular Sciences  
We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence  ...  Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes.  ...  The lncLocator utilized k-mer based features along with high-level abstraction features extracted using unsupervised deep learning models.  ... 
doi:10.3390/ijms22168719 pmid:34445436 pmcid:PMC8395733 fatcat:l66viluvf5aqvimsyvmowvpjuy

Recent Trends in Machine Learning-based Protein Fold Recognition Methods

2020 Biointerface Research in Applied Chemistry  
This work also includes details of machine learning algorithms used with respective settings and protein fold recognition structures. Detailed performance comparison of well-known works is also given.  ...  The kind of features such as sequential, structural, functional, and evolutionary extracted for representing protein sequence and different methods of extracting these features.  ...  Acknowledgments The authors are thankful to the Research and Development Center of Dharmsinh Desai University for allowing the use of their tools and resources necessary for current research work.  ... 
doi:10.33263/briac114.1123311243 fatcat:ojzhveu5zvfbjgdrvy2btmgu5y

Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation

Rui Antunes, Sérgio Matos
2019 Database: The Journal of Biological Databases and Curation  
A significant aspect of our best method is the use of a simple deep learning model together with a very narrow representation of the relation instances, using only up to 10 words from the shortest dependency  ...  In this paper, we follow a deep learning approach for extracting mentions of chemical-protein interactions from biomedical articles, based on various enhancements over our participation in the BioCreative  ...  Acknowledgments We thank the organizers of the BioCreative VI CHEMPROT task, the authors of BioSentVec embeddings for making their models publicly available and the reviewers for their valuable comments  ... 
doi:10.1093/database/baz095 pmid:31622463 pmcid:PMC6796919 fatcat:3l6gyodd65b7joh6t3vzrny5li

Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing

Jari Björne, Tapio Salakoski
2018 Proceedings of the BioNLP 2018 workshop  
Using this system, our machine learning model can be easily applied to a large set of corpora from e.g. the BioNLP, DDI Extraction and BioCreative shared tasks.  ...  Where relation extraction concerns the detection of semantic connections between pairs of entities, event extraction expands this concept with the addition of trigger words, multiple arguments and nested  ...  In recent years, the advent of deep learning has resulted in advances in many fields, and relation and event extraction are no exception.  ... 
doi:10.18653/v1/w18-2311 dblp:conf/bionlp/BjorneS18 fatcat:d3jjs3g4dngk5eh2z4sqjvsfzu

MCP: a Multi-Component learning machine to Predict protein secondary structure [article]

Leila Khalatbari, Mohammad Reza Kangavari, Saeid Hosseini, Hongzhi Yin, Ngai-Man Cheung
2019 arXiv   pre-print
We conduct comprehensive experiments to compare our model with the current state-of-the-art approaches.  ...  The possible underlying reasons are the ambiguous sequence-structure relation, noise in input protein data, class imbalance, and the high dimensionality of the encoding schemes that represent the protein  ...  Moreover, machine learning-based methods such as ensemble models, and deep learning approaches have been recently exploited to increase the accuracy of the prediction results.  ... 
arXiv:1806.06394v4 fatcat:g77q4sottndxnj746ocvlf6s3i

Automatic extraction of gene-disease associations from literature using joint ensemble learning

Balu Bhasuran, Jeyakumar Natarajan, Enrique Hernandez-Lemus
2018 PLoS ONE  
In this current scenario one of the crucial technologies is biomedical text mining, and relation extraction shows the promising result to explore the research of genes associated with diseases.  ...  ensemble support vector machine for extracting gene-disease relations from four gold standard corpora.  ...  Some of the well-known applications such as named entity recognition (NER) [5] , relation extraction (protein-protein interaction, chemical-disease association) [6, 7] , identification of bio-events  ... 
doi:10.1371/journal.pone.0200699 pmid:30048465 pmcid:PMC6061985 fatcat:tsw4ptvrkbdclc5jbgyv5xhxqu
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