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An end-to-end deep learning architecture for extracting protein–protein interactions affected by genetic mutations

Tung Tran, Ramakanth Kavuluru
2018 Database: The Journal of Biological Databases and Curation  
In this study, we explore an end-to-end approach for PPIm relation extraction by deploying a three-component pipeline involving deep learning-based namedentity recognition and relation classification models  ...  Specifically, such pairs represent proteins participating in a binary protein-protein interaction relation where the interaction is additionally affected by a genetic mutation-referred to as a PPIm relation  ...  Concretely, this track focuses on mining biomedical literature for protein-protein interactions (PPIs) that are affected by the presence of a genetic mutation.  ... 
doi:10.1093/database/bay092 pmid:30239680 pmcid:PMC6146129 fatcat:rbqd5xhkivfkbgnzprnxf3vhbm

Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine

Rezarta Islamaj Doğan, Sun Kim, Andrew Chatr-aryamontri, Chih-Hsuan Wei, Donald C Comeau, Rui Antunes, Sérgio Matos, Qingyu Chen, Aparna Elangovan, Nagesh C Panyam, Karin Verspoor, Hongfang Liu (+15 others)
2019 Database: The Journal of Biological Databases and Curation  
containing experimentally verified protein-protein interactions (PPIs) affected by genetic mutations and (ii) relation extraction task, focused on extracting the affected interactions (protein pairs).  ...  Submitted systems explored a wide range of methods, from traditional rule-based, statistical and machine learning systems to state-of-the-art deep learning methods.  ...  Chang and Lorrie Boucher for annotating the evaluation data set. Conflict of interest. None declared.  ... 
doi:10.1093/database/bay147 pmid:30689846 pmcid:PMC6348314 fatcat:jvsf4xwr7jbbnb75d3oqczrjrq

A Review of Protein Structure Prediction using Deep Learning

Meredita Susanty, Tati Erawati Rajab, Rukman Hertadi, Gunadi, T. Yamada, A.A.C. Pramana, Y. Ophinni, A. Gusnanto, W.A. Kusuma, J. Yunus, Afiahayati, R. Dharmastiti (+3 others)
2021 BIO Web of Conferences  
We discuss various deep learning approaches used to predict protein structure and future achievements and challenges.  ...  In this review, we summarize recent work in applying deep learning techniques to tackle problems in protein structural prediction.  ...  This work was supported by Indonesia Endowment Fund for Education (LPDP), Ministry of Finance, Republic of Indonesia.  ... 
doi:10.1051/bioconf/20214104003 fatcat:cwab4ukudvgx5dnny3dk64g3uq

Lasagna: Multifaceted Protein-Protein Interaction Prediction Based on Siamese Residual RCNN [article]

Muhao Chen, Chelsea Jui-Ting Ju, Guangyu Zhou, Tianran Zhang, Xuelu Chen, Kai-Wei Chang, Carlo Zaniolo, Wei Wang
2018 bioRxiv   pre-print
Hence, we present an end-to-end framework, Lasagna, for PPI predictions using only the primary sequences of a protein pair.  ...  Lasagna incorporates a deep residual recurrent convolutional neural network in the Siamese learning architecture, which leverages both robust local features and contextualized information that are significant  ...  This degenerates our framework to a similar architecture to DPPI, but differs in directly conducting an end-to-end training on primary sequences instead of requiring the protein profiles constructed by  ... 
doi:10.1101/501791 fatcat:ktcoz64rirc7vat6tqkqxh34zi

Neural networks to learn protein sequence-function relationships from deep mutational scanning data [article]

Sam Gelman, Philip A Romero, Anthony Gitter
2020 bioRxiv   pre-print
We present a supervised deep learning framework to learn the sequence-function mapping from deep mutational scanning data and make predictions for new, uncharacterized sequence variants.  ...  We test multiple neural network architectures, including a graph convolutional network that incorporates protein structure, to explore how a network's internal representation affects its ability to learn  ...  Acknowledgements We thank Zhiyuan Duan for his assistance running Rosetta.  ... 
doi:10.1101/2020.10.25.353946 fatcat:4fc6l7utsvagtfcixfnjdcyzim

Neural networks to learn protein sequence–function relationships from deep mutational scanning data

Sam Gelman, Sarah A. Fahlberg, Pete Heinzelman, Philip A. Romero, Anthony Gitter
2021 Proceedings of the National Academy of Sciences of the United States of America  
We present a supervised deep learning framework to learn the sequence–function mapping from deep mutational scanning data and make predictions for new, uncharacterized sequence variants.  ...  We test multiple neural network architectures, including a graph convolutional network that incorporates protein structure, to explore how a network's internal representation affects its ability to learn  ...  This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). ACKNOWLEDGMENTS. We thank Zhiyuan  ... 
doi:10.1073/pnas.2104878118 pmid:34815338 pmcid:PMC8640744 fatcat:s3zkimuxo5axlho2pnarxrq6fq

Leveraging prior knowledge for protein–protein interaction extraction with memory network

Huiwei Zhou, Zhuang Liu, Shixian Ning, Yunlong Yang, Chengkun Lang, Yingyu Lin, Kun Ma
2018 Database: The Journal of Biological Databases and Curation  
Automatically extracting Protein-Protein Interactions (PPI) from biomedical literature provides additional support for precision medicine efforts.  ...  This paper proposes a novel memory network-based model (MNM) for PPI extraction, which leverages prior knowledge about protein-protein pairs with memory networks.  ...  However, few researches have paid attention to extracting protein-protein interaction affected by mutations (PPIm) (5) .  ... 
doi:10.1093/database/bay071 pmid:30010731 pmcid:PMC6047414 fatcat:ejjfns2vtvcqpg2ic3n5bqwcay

Deep Learning for Genomics: A Concise Overview [article]

Tianwei Yue, Haohan Wang
2018 arXiv   pre-print
of developing modern deep learning architectures for genomics.  ...  Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome.  ...  A collaboratively written review paper on deep learning, genomics, and precision medicine, now available at https://greenelab.github.io/deep-review/  ... 
arXiv:1802.00810v2 fatcat:u6s7pz2p6jdxzodz5k34it2hiu

Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics

Joel Markus Vaz, S. Balaji
2021 Molecular diversity  
The review is organized into specific research domains that participate in pharmacogenomics for a more comprehensive understanding. Furthermore, the future intentions of deep learning are outlined.  ...  Due to the rise of interest in precision and personalized medicine, in this review, we have provided a brief overview of the possibilities of implementing CNNs as an effective tool for analyzing one-dimensional  ...  Hence, there is a need to apply NLP with deep learning architectures that can detect essential features automatically.  ... 
doi:10.1007/s11030-021-10225-3 pmid:34031788 pmcid:PMC8342355 fatcat:nlgc564vu5dzxmcyormls6jbka

Precise Prediction of Calpain Cleavage Sites and Their Aberrance Caused by Mutations in Cancer

Ze-Xian Liu, Kai Yu, Jingsi Dong, Linhong Zhao, Zekun Liu, Qingfeng Zhang, Shihua Li, Yimeng Du, Han Cheng
2019 Frontiers in Genetics  
, which demonstrated that the calpain-mediated cleavage events were affected by mutations and heavily implicated in the regulation of cancer cells.  ...  Meanwhile, we found that protein interactions could enrich the calpain-substrate regulatory relationship.  ...  However, to date, an available deep-learning framework for calpain cleavage site prediction is still lacking.  ... 
doi:10.3389/fgene.2019.00715 pmid:31440276 pmcid:PMC6694742 fatcat:6ux6s3b6iffsxpjglcdkxfxsei

Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

Michael K. K. Leung, Andrew Delong, Babak Alipanahi, Brendan J. Frey
2016 Proceedings of the IEEE  
| In this paper, we provide an introduction to machine learning tasks that address important problems in genomic medicine.  ...  Modern biology allows high-throughput measurement of many such cell variables, including gene expression, splicing, and proteins binding to nucleic acids, which can all be treated as training targets for  ...  The authors would also like to thank the reviewers for their contributions to improve the paper.  ... 
doi:10.1109/jproc.2015.2494198 fatcat:esu2dpq52jgmjmxhy2vr7yslm4

Hierarchical bi-directional attention-based RNNs for supporting document classification on protein–protein interactions affected by genetic mutations

Aris Fergadis, Christos Baziotis, Dimitris Pappas, Haris Papageorgiou, Alexandros Potamianos
2018 Database: The Journal of Biological Databases and Curation  
Hierarchical bi-directional attention-based RNNs for supporting document classification on protein-protein interactions affected by genetic mutations.  ...  The sequence encoder is composed of two bi-directional RNN equipped with an attention mechanism that identifies and captures the most important elements, words or sentences, in a document followed by a  ...  Acknowledgements We acknowledge support of this work by the project "Computational Science and Technologies: Data, Content and Interaction" (MIS 5002437) which is implemented under the Action "Reinforcement  ... 
doi:10.1093/database/bay076 pmid:30137284 pmcid:PMC6105093 fatcat:lmoi72c25ncljomrro64dkkhii

Learning the Regulatory Code of Gene Expression

Jan Zrimec, Filip Buric, Mariia Kokina, Victor Garcia, Aleksej Zelezniak
2021 Frontiers in Molecular Biosciences  
Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well  ...  Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts  ...  ACKNOWLEDGMENTS We thank Sandra Viknander for insightful discussions on deep neural networks and their applications.  ... 
doi:10.3389/fmolb.2021.673363 pmid:34179082 pmcid:PMC8223075 fatcat:xtrouhqxvnerlcrto63e734dwa

Deep Learning Based Prediction of Autism Spectrum Disorder using Codon Encoding of Gene Sequences

2019 International Journal of Engineering and Advanced Technology  
This work utilizes codon encoding and one hot encoding technique to transform the mutated gene sequences which are exploited for self learning the features by deep network.  ...  The development of computational tools to recognize Autism Spectrum Disorder (ASD) originated by genetic mutations is vital to the development of disease-specific targeted therapies.  ...  Given that a candidate ASD gene is affected by various mutations, the classification of genes helps in early diagnosis and hence for targeted therapies.  ... 
doi:10.35940/ijeat.a1817.109119 fatcat:uamjojmwezab3kg4xmpfpw5pxu

Deep Learning in the Biomedical Applications: Recent and Future Status

Ryad Zemouri, Noureddine Zerhouni, Daniel Racoceanu
2019 Applied Sciences  
This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI.  ...  Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain.  ...  Among all the deep neural architectures, there is a growing interest in an end-to-end convolutional neural network, replacing the traditional handcrafted machine learning methods.  ... 
doi:10.3390/app9081526 fatcat:srjvngtufbhstfcvn4mvhmrdve
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