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Scaling up data curation using deep learning: An application to literature triage in genomic variation resources

Kyubum Lee, Maria Livia Famiglietti, Aoife McMahon, Chih-Hsuan Wei, Jacqueline Ann Langdon MacArthur, Sylvain Poux, Lionel Breuza, Alan Bridge, Fiona Cunningham, Ioannis Xenarios, Zhiyong Lu, Rong Xu
2018 PLoS Computational Biology  
Scaling up data curation using deep learning PLOS Computational Biology | https://doi.  ...  Here, we suggest a deep learning-based literature triage method for genomic variation resources. Our method achieves state-of-the-art performance on the triage task.  ...  We would like to thank Anthony Rios for his assistance with the deep learning-based text classification.  ... 
doi:10.1371/journal.pcbi.1006390 pmid:30102703 fatcat:i7hmr5ee5rebpckyayw5fazkuq

Literature Triage on Genomic Variation Publications by Knowledge-enhanced Multi-channel CNN [article]

Chenhui Lv and Qian Lu and Xiang Zhang
2020 arXiv   pre-print
In addition, knowledge embeddings learned from UMLS is also used to provide extra medical knowledge beyond textual features in the process of triage.  ...  In order to cut down the cost of literature triage, machine-learning models were adopted to automatically identify biomedical publications.  ...  We would also like to thank Mr. Da Tong and Mr. Weijian Ye for their valuable suggestions on our work.  ... 
arXiv:2005.04044v1 fatcat:6yjo7vrysvbgxg5bu4l3d7fh2q

UPCLASS: a deep learning-based classifier for UniProtKB entry publications

2020 Database: The Journal of Biological Databases and Curation  
We believe that such an approach could be used to systematically categorize the computationally mapped bibliography in UniProtKB, which represents a significant set of the publications, and help curators  ...  , based on the type of data they contain.  ...  Lee et al. used a convolutional neural network (CNN) model, supported by word2vec representations, in the triage phase of genomic variation resources, outperforming the precision of SVM models by up to  ... 
doi:10.1093/database/baaa026 pmid:32367111 pmcid:PMC7198315 fatcat:mkgx4v36w5dyjp7w5xtzuuaetu

Stochastic semi-supervised learning to prioritise genes from high-throughput genomic screens [article]

Dimitrios Vitsios, Slave Petrovski
2019 bioRxiv   pre-print
Access to large-scale genomics datasets has increased the utility of hypothesis-free genome-wide analyses that result in candidate lists of genes.  ...  We believe that mantis-ml is a novel easy-to-use tool to support objectively triaging gene discovery and overall enhancing our understanding of complex genotype-phenotype associations.  ...  application of large-scale genomic studies by large genomic and/or healthcare institutions for research and diagnostic purposes.  ... 
doi:10.1101/655449 fatcat:t6aniuof65c47ekniziygkr2y4

Mantis-ml: Disease-Agnostic Gene Prioritization from High-Throughput Genomic Screens by Stochastic Semi-supervised Learning

Dimitrios Vitsios, Slavé Petrovski
2020 American Journal of Human Genetics  
Access to large-scale genomics datasets has increased the utility of hypothesis-free genome-wide analyses.  ...  Mantis-ml is an intuitive tool that supports the objective triaging of large-scale genomic discovery studies and enhances our understanding of complex genotype-phenotype associations.  ...  Acknowledgments We thank Quanli Wang for useful discussions and his valuable feedback. Declaration of Interests The authors declare no competing interests.  ... 
doi:10.1016/j.ajhg.2020.03.012 pmid:32386536 pmcid:PMC7212270 fatcat:6w4dwqvet5drfkwnab4hu356mu

Deep Learning applications for COVID-19

Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
2021 Journal of Big Data  
Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19.  ...  We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology.  ...  Acknowledgements We would like to thank the reviewers in the Data Mining and Machine Learning Laboratory at Florida Atlantic University.  ... 
doi:10.1186/s40537-020-00392-9 pmid:33457181 pmcid:PMC7797891 fatcat:aokxo63z2rhdpfxo3egyf3xpcm

A deep learning relation extraction approach to support a biomedical semi-automatic curation task: the case of the gluten bibliome

Martín Pérez-Pérez, Tânia Ferreira, Gilberto Igrejas, Florentino Fdez-Riverola
2022 Expert systems with applications  
a novel vector-space integrated into a deep learning model to assists manual curators in a real curation task by learning from their previous decisions.  ...  The different resources used in this work as well as the manually curated corpus are public available on our GitHub repository.  ...  In this regard, many real-world data mining applications with an extremely large pool of unlabeled data available such as document triage, image classification, or e-mail classification usually apply methodologies  ... 
doi:10.1016/j.eswa.2022.116616 fatcat:7mera4djvbd7rgqglpvmn4gdti

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci

Hannah L. Nicholls, Christopher R. John, David S. Watson, Patricia B. Munroe, Michael R. Barnes, Claudia P. Cabrera
2020 Frontiers in Genetics  
Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase.  ...  , as well as deep learning models, i.e., neural networks.  ...  ACKNOWLEDGMENTS We would like to dedicate this manuscript to our friend and colleague, Chris John.  ... 
doi:10.3389/fgene.2020.00350 pmid:32351543 pmcid:PMC7174742 fatcat:7h47rgpunvaa5grbz43aswwzey

Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research

Laurianne David, Josep Arús-Pous, Johan Karlsson, Ola Engkvist, Esben Jannik Bjerrum, Thierry Kogej, Jan M. Kriegl, Bernd Beck, Hongming Chen
2019 Frontiers in Pharmacology  
In this regard, artificial intelligence technologies such as deep learning and machine learning methods play a key role in cheminformatics and bio-image analytics fields to address activity prediction,  ...  , with a specific focus on deep-learning technologies.  ...  These fast-growing data have fuelled the application of data-savvy ML methods, and in particular deep learning, in order to detect patterns that allow to derive hypotheses for compound-mediated effects  ... 
doi:10.3389/fphar.2019.01303 pmid:31749705 pmcid:PMC6848277 fatcat:x7eomckmjzdhpplthsbdctie7i

Mouse Genome Informatics (MGI): latest news from MGD and GXD

Martin Ringwald, Joel E. Richardson, Richard M. Baldarelli, Judith A. Blake, James A. Kadin, Cynthia Smith, Carol J. Bult
2021 Mammalian Genome  
AbstractThe Mouse Genome Informatics (MGI) database system combines multiple expertly curated community data resources into a shared knowledge management ecosystem united by common metadata annotation  ...  MGI's mission is to facilitate the use of the mouse as an experimental model for understanding the genetic and genomic basis of human health and disease.  ...  Xiangying Jiang (Amazon) for discussions about machine learning approaches to MGI's literature curation processes.  ... 
doi:10.1007/s00335-021-09921-0 pmid:34698891 pmcid:PMC8913530 fatcat:zwlwn4gb5vhwzfnkx5qszjn7ii

Digital Health COVID-19 Impact Assessment: Lessons Learned and Compelling Needs

Peter Lee, Microsoft, Amy Abernethy, David Shaywitz, Adi Gundlapalli, Jim Weinstein, P. Murali Doraiswamy, Kevin Schulman, Subha Madhavan, Verily, Astounding HealthTech, U.S. Centers for Disease Control and Prevention (+4 others)
2022 NAM Perspectives  
The authors would like to thank Mahnoor Ahmed and Ariana Bailey from the National Academy of Medicine for their assistance in producing this manuscript.  ...  Acknowledgments The authors would like to acknowledge the members and leadership of the U.S. CDC COVID-19 Response for their insights on public health modernization.  ...  Stemming from learnings tracing back at least to the human genome project two decades ago, these scientifi c communities now have deep experience sharing data, both technically and culturally, and have  ... 
doi:10.31478/202201c pmid:35402858 pmcid:PMC8970223 fatcat:eurd6ad7xnga7gt7da5xhs6krq

Computer‐aided diagnosis in the era of deep learning

Heang‐Ping Chan, Lubomir M. Hadjiiski, Ravi K. Samala
2020 Medical Physics (Lancaster)  
CAD uses machine learning methods to analyze imaging and/or nonimaging patient data and makes assessment of the patient's condition, which can then be used to assist clinicians in their decision-making  ...  In this paper, we discuss the potential and challenges in developing CAD tools using deep learning technology or artificial intelligence (AI) in general, the pitfalls and lessons learned from CAD in screening  ...  One of the major challenges in developing an accurate and generalizable DCNN for a given task is a large well-curated data set for training.  ... 
doi:10.1002/mp.13764 pmid:32418340 fatcat:7mz3d6kvwnf6tpsj35qwfwbyje

Neuro-Symbolic Learning: Principles and Applications in Ophthalmology [article]

Muhammad Hassan, Haifei Guan, Aikaterini Melliou, Yuqi Wang, Qianhui Sun, Sen Zeng, Wen Liang, Yiwei Zhang, Ziheng Zhang, Qiuyue Hu, Yang Liu, Shunkai Shi (+15 others)
2022 arXiv   pre-print
This review presents a comprehensive survey on the state-of-the-art NeSyL approaches, their principles, advances in machine and deep learning algorithms, applications such as opthalmology, and most importantly  ...  Attempts have been made to overcome the challenges in neural network computing by representing and embedding domain knowledge in terms of symbolic representations.  ...  Requiring large scale, high quality, and labeled datasets are the hurdles of deep learning applications. To train an end-to-end neural-to-symbolic model, Agarwal et al.  ... 
arXiv:2208.00374v1 fatcat:pktmnomj3bbwpjyj7lmu37rl7i

Integrating image caption information into biomedical document classification in support of biocuration

2020 Database: The Journal of Biological Databases and Curation  
Acknowledgements We are grateful to Dr Cecilia Arighi for helpful discussions. Conflict of interest. None declared.  ...  GXD collects all the gene expression information in the Mouse Genome Informatics (MGI) resource.  ...  To employ such deep learning-based classifier, we first trained a 100-dimensional word embedding model using FastText over the training set, as such embedding configuration was reported to obtain the best  ... 
doi:10.1093/database/baaa024 pmid:32294192 pmcid:PMC7159034 fatcat:btqaznfepncutewhzeebithgnu

PGxMine: Text mining for curation of PharmGKB

Jake Lever, Julia M Barbarino, Li Gong, Rachel Huddart, Katrin Sangkuhl, Ryan Whaley, Michelle Whirl-Carrillo, Mark Woon, Teri E Klein, Russ B Altman
2020 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  
Precision medicine tailors treatment to individuals personal data including differences in their genome.  ...  We present the PGxMine resource, a text-mined resource of pharmacogenomic associations from all accessible published literature to assist in the curation of PharmGKB.  ...  Machine learning methods are used to assist in biomedical knowledge base curation in two different ways.  ... 
pmid:31797632 pmcid:PMC6917032 fatcat:josnedartne3jdtlyyljcup4m4
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