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Development of a Machine Learning Framework for Biomedical Text Mining [chapter]

Ruben Rodrigues, Hugo Costa, Miguel Rocha
2016 Advances in Intelligent Systems and Computing  
Development of a machine learning framework for biomedical text mining Rodrigues R 1,2 , Costa H 2 , Rocha M 1 1 School of Engineering, University of Minho; 2 Silicolife, Lda The biomedical literature  ...  The framework was integrated in @Note2, an open-source computational framework for biomedical text mining based on the model-viewcontroller paradigm, in the form of a novel plug-in, which allows users  ... 
doi:10.1007/978-3-319-40126-3_5 fatcat:zuwfadaxl5fcvpix5jeu2ohxma

A Novel Framework for Biomedical Text Mining

Janyl Jumadinova, Oliver Bonham-Carter, Hanzhong Zheng, Michael Camara, Dejie Shi
2020 Journal on Big Data  
We developed a novel biomedical text mining model implemented by a multi-agent system and distributed computing mechanism.  ...  Our distributed system, TextMed, comprises of several software agents, where each agent uses a reinforcement learning method to update the sentiment of relevant text from a particular set of research articles  ...  We selected reinforcement learning as a suitable learning paradigm for biomedical text mining because there is no sentimental analysis on keywords scope in biomedical research.  ... 
doi:10.32604/jbd.2020.010090 fatcat:mobdvmwqyfhf5cx2fbi5zezclu

SparkText: Biomedical Text Mining on Big Data Framework

Zhan Ye, Ahmad P. Tafti, Karen Y. He, Kai Wang, Max M. He, Vladimir B. Bajic
2016 PLoS ONE  
Results In this study, we designed and developed an efficient text mining framework called Spark-Text on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning  ...  The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%.  ...  adapt NLP, text mining, and machine learning strategies in a large-scale fashion using a Big Data framework; and (c) to provide insights into this research area by identifying challenges and possible  ... 
doi:10.1371/journal.pone.0162721 pmid:27685652 pmcid:PMC5042555 fatcat:z6nmmvjdzjc6zfhmp5pnvhgjni

An Efficient Model for Medical Data Classification using Gene Features

Kosaraju Chaitanya, Rachakonda Venkatesh, Thulasi Bikku
2019 International Journal of Advanced Computer Science and Applications  
The primary point of the proposed ensemble learning models is to characterize the high dimensional information for gene/proteinbased disease expectation in light of substantial biomedical databases.  ...  A large number of MESH terms with gene and protein are utilized to characterize the patterns of a large number of medical documents from a large set of records.  ...  As a result, text mining has evolved in the field of biomedical systems where text mining techniques and machine learning models are integrated using high computational resources.  ... 
doi:10.14569/ijacsa.2019.0101160 fatcat:j34saznehjdkhdw4m4doej76ae

A Multi-Platform Annotation Ecosystem for Domain Adaptation

Richard Eckart de Castilho, Nancy Ide, Jin-Dong Kim, Jan-Christoph Klie, Keith Suderman
2019 Proceedings of the 13th Linguistic Annotation Workshop  
This paper describes an ecosystem consisting of three independent text annotation platforms.  ...  To demonstrate their ability to work in concert, we illustrate how to use them to address an interactive domain adaptation task in biomedical entity recognition.  ...  PubAnnotation is supported by the Database Integration Coordination Program funded by National Bioscience Database Center (NBDC) of Japan Science and Technology Agency (JST).  ... 
doi:10.18653/v1/w19-4021 dblp:conf/acllaw/CastilhoIKKS19 fatcat:y5ipm6ozdnf4lfomkv6qkpwb7q

A systematic study on latent semantic analysis model parameters for mining biomedical literature

Mohammed Yeasin, Haritha Malempati, Ramin Homayouni, Mohammad Sorower
2009 BMC Bioinformatics  
Probabilistic Programming for Advanced Machine Learning Summer School'.  ...  towards building a framework to recognize human emotion using multimodal fusion of audio, video and text features  ... 
doi:10.1186/1471-2105-10-s7-a6 fatcat:xhsrddwp6fhyhniezp4yvnu2aq

MBlab: Molecular Biodiversity Laboratory [chapter]

Corrado Loglisci, Annalisa Appice, Michelangelo Ceci, Donato Malerba, Floriana Esposito
2011 Communications in Computer and Information Science  
These integrate sophisticated technologies and innovative approaches of Information Extraction, Data Mining and Machine Learning to perform descriptive tasks of knowledge discovery from biomedical repositories  ...  Our contribution is the design of computational solutions for the analysis of biomedical documents and images.  ...  This work is in partial fulfillment of the research objectives of the project MBlab DM19410 "Molecular Biodiversity Laboratory".  ... 
doi:10.1007/978-3-642-27302-5_18 fatcat:semakj3pm5d3xbn43rc672crti

Table of Contents

2021 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS)  
....................... 473 Drug-target Interaction Prediction Using Machine Learning ......................................... 480 Software Engineering Extracting Software Design from Text: A Machine  ...  Survey on Learning-Based Intrusion Detection Systems for IoT Networks .............. 278 NoSurv: A Framework for Protection against Surveillance Attacks on Mobile Devices ..............................  ... 
doi:10.1109/icicis52592.2021.9694157 fatcat:qnwadgegdfbbrds3tvrz4wpr4q

Mining Biological Data on the Cloud – A MapReduce Approach [chapter]

Zafeiria-Marina Ioannou, Nikolaos Nodarakis, Spyros Sioutas, Athanasios Tsakalidis, Giannis Tzimas
2014 IFIP Advances in Information and Communication Technology  
In the context of this work, we propose an efficient and scalable solution, in the MapReduce framework, for mining and analyzing biological and biomedical data.  ...  To achieve this, a great number of text mining techniques have been developed that efficiently manage and disclose meaningful patterns and correlations from biological and biomedical data repositories.  ...  This research has been co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic  ... 
doi:10.1007/978-3-662-44722-2_11 fatcat:7kpfh6z6hverdba3utsnupbwae

A Short Survey of Biomedical Relation Extraction Techniques [article]

Elham Shahab
2017 arXiv   pre-print
In the current research, we focus on different aspects of relation extraction techniques in biomedical domain and briefly describe the state-of-the-art for relation extraction between a variety of biological  ...  Biomedical information is growing rapidly in the recent years and retrieving useful data through information extraction system is getting more attention.  ...  Text mining and knowledge extraction techniques along with statistical machine learning algorithms are widely used in medical and biomedical domain such as [45, 64] .  ... 
arXiv:1707.05850v3 fatcat:snyvtomcxbbeplkspqaucmpely

novel deep neural network framework for biomedical named entity recognition

Adyasha Dash, Manjusha Pandey, Siddharth Swarup Rautaray
2022 International Journal of Health Sciences  
Recently, studies have demonstrated the application of deep learning based approaches for biomedical named entity recognition (BioNER) and shown promising results.  ...  Biomedical Named Entity Recognition (BNER) gets more and more attention from the researchers since it is a fundamental task in biomedical information extraction.  ...  Figure 1 . 1 A 11 Figure 1.1 A typical Bio-NER workflow Figure 2 . 1 21 Figure 2.1 Machine learning framework for classification 3.  ... 
doi:10.53730/ijhs.v6ns5.9557 fatcat:pzmgdcgg7fcploqwjz7otidi5q

Methods and Trends in Information Retrieval in Big Data Genomic Research

There was a surge of genomic information from the different literature and the production of genome datasets that catapulted the development of several tools for analyzing and presenting new found knowledge  ...  This paper described information retrieval (IR) and the common methods of finding, extracting, and mining information in genomic research through text mining, and natural language processing (NLP).  ...  The application of machine learning techniques to develop a model and structure of the information of text of interest as described in [20] .  ... 
doi:10.35940/ijitee.i1109.0789s219 fatcat:j2uramagd5a75jusrcor75w7ue

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Alshreef Abed, Jingling Yuan, Lin Li
2017 International Journal of Advanced Computer Science and Applications  
Text mining can help us mine information and knowledge from a mountain of text, and is now widely applied in biomedical research.  ...  The fast development of these accumulations makes it progressively troublesome for people to get to the required information in an advantageous and viable way.  ...  Tan and Lambri [52] suggested a framework for the purpose of selecting a suitable ontology for a specific application for biomedical text mining.  ... 
doi:10.14569/ijacsa.2017.080804 fatcat:juylimcz4jdbhmifhrz6so7ciq

Special issue on semantic data analytics and bioinformatics

Haiying Wang, Man-Wai Mak, Hui Wang
2017 International Journal of Machine Learning and Cybernetics  
The first paper in this special issue focuses on named entity recognition (NER), which is one of the key tasks in biomedical text mining.  ...  We would like to thank the Editors of the International Journal of Machine Learning and Cybernetics for their support and encouragement.  ...  The first paper in this special issue focuses on named entity recognition (NER), which is one of the key tasks in biomedical text mining.  ... 
doi:10.1007/s13042-017-0749-6 fatcat:avywwtmaanc6zochhyzo4mfrbq

Selected Extended Papers of the 12th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB)

Florentino Fdez-Riverola, Miguel Rocha
2019 Journal of Integrative Bioinformatics  
highthroughput data integration, analysis and mining, biological networks and models, or biomedical text mining, just to name a few topics.  ...  The second paper, by Sebastián-Pérez et al. [2] deals with the development of machine learning methods for a QSAR (quantitative structure-activity relationship) task, related with the identification of  ...  highthroughput data integration, analysis and mining, biological networks and models, or biomedical text mining, just to name a few topics.  ... 
doi:10.1515/jib-2019-0004 fatcat:7koqzdxs6fagfor7xwgmbg3t6q
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