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A practical application of generative adversarial networks for RNA-seq analysis to predict the molecular progress of Alzheimer's disease

Jinhee Park, Hyerin Kim, Jaekwang Kim, Mookyung Cheon, Hugues Berry
2020 PLoS Computational Biology  
In this study, we applied a deep learning technique called generative adversarial networks (GANs) to predict the molecular progress of Alzheimer's disease (AD).  ...  Next-generation sequencing (NGS) technology has become a powerful tool for dissecting the molecular and pathological signatures of a variety of human diseases.  ...  Here, we suggest GANs may be a practical approach for predicting disease progression.  ... 
doi:10.1371/journal.pcbi.1008099 pmid:32706788 fatcat:6mz2p5j6hrdr7nr4k5zdljpbwy

The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease

Rohan Mishra, Bin Li
2020 Aging and Disease  
The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for  ...  Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects.  ...  Acknowledgements This study was supported by the Washington Institute for Health Sciences grant (G20190710).  ... 
doi:10.14336/ad.2020.0312 pmid:33269107 pmcid:PMC7673858 fatcat:72rkx7bjvbaf7earfiu5d44rqm

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.  ...  We end our analysis with a critical discussion, interpretation and relevant open challenges.  ...  In [304] , a conditional GAN is explored to augment artificially generated lung nodules to improve the robustness of the progressive holistically nested network (P-HNN) model for pathological lung segmentation  ... 
doi:10.3390/app9081526 fatcat:srjvngtufbhstfcvn4mvhmrdve

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

Jiajia Liu, Zhiwei Fan, Weiling Zhao, Xiaobo Zhou
2021 Frontiers in Genetics  
regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data.  ...  In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis.  ...  ACKNOWLEDGMENTS We thank the members of the Center for Computational Systems Medicine (CCSM) for valuable discussion.  ... 
doi:10.3389/fgene.2021.655536 pmid:34135939 pmcid:PMC8203333 fatcat:tp5v7gtdwnezjfrfef45ctidye

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  
One of the goals of genomic medicine is to determine how variations in the DNA of individuals can affect the risk of different diseases, and to find causal explanations so that targeted therapies can be  ...  With the growing availability of large-scale data sets and advanced computational techniques such as deep learning, researchers can help to usher in a new era of effective genomic medicine.  ...  Acknowledgment The authors would like to acknowledge members of the Frey Lab, especially H. Y. Xiong, for helpful discussions and comments.  ... 
doi:10.1109/jproc.2015.2494198 fatcat:esu2dpq52jgmjmxhy2vr7yslm4

Computational Methods for Single-Cell Imaging and Omics Data Integration

Ebony Rose Watson, Atefeh Taherian Fard, Jessica Cara Mar
2022 Frontiers in Molecular Biosciences  
Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile  ...  This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further  ...  The applications of methods for sc-RNA data analysis have begun to evolve into a predictable workflow.  ... 
doi:10.3389/fmolb.2021.768106 pmid:35111809 pmcid:PMC8801747 fatcat:zrqpododa5gyxoti5fnx3llepq

A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines – From Medical to Remote Sensing [article]

Ankan Dash, Junyi Ye, Guiling Wang
2021 arXiv   pre-print
We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors.  ...  GANs can be used to perform image processing, video generation and prediction, among other computer vision applications.  ...  [130] applied GANs to predict the molecular progress of Alzheimer's disease (AD) by successfully analyzing RNA-seq data from a 5xFAD mouse model of AD.  ... 
arXiv:2110.01442v1 fatcat:mqpnqw2ysfdz7dneajiw33dbga

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data [article]

Yifan Zhao, Huiyu Cai, Zuobai Zhang, Jian Tang, Yue Li
2021 bioRxiv   pre-print
However, integrative analysis of scRNA-seq data remains a challenge largely due to batch effects.  ...  The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies.  ...  Emerged as a key application of single-cell RNA sequencing (scRNA-seq) data, unsupervised clustering allows for cell-type identification in a data-driven manner.  ... 
doi:10.1101/2021.01.13.426593 fatcat:r4miiy6kxnfufh2h7mbij3rbwy

Artificial Intelligence in Nutrients Science Research: A Review

Jarosław Sak, Magdalena Suchodolska
2021 Nutrients  
The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.  ...  Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental  ...  Acknowledgments: This study was supported by grants from the Medical University of Lublin, no. DS 507. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/nu13020322 pmid:33499405 pmcid:PMC7911928 fatcat:3qssvkrtpfernauajowizhxzp4

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Rohan Gupta, Devesh Srivastava, Mehar Sahu, Swati Tiwari, Rashmi K Ambasta, Pravir Kumar
2021 Molecular diversity  
The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms  ...  Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative  ...  Acknowledgements We would like to thank the senior management of Delhi Technological University for their constant support and guidance.  ... 
doi:10.1007/s11030-021-10217-3 pmid:33844136 pmcid:PMC8040371 fatcat:yltthjorrvfrjgyrnszpxgpb2q

Abstracts from the 52nd European Society of Human Genetics (ESHG) Conference: Oral Presentations

2019 European Journal of Human Genetics  
In a cohort of 39 patients, next generation sequencing of 52 cancer-driver genes on cell free-DNA was able to pick up clones responsible for disease progression in 60% of cases.  ...  To generate a critical mass patients to find further clinical characteristics of the disease as well as to ensure a rapid progression towards future interventional studies we developed a standardized registry  ...  The Center for Undiagnosed Diseases at Stanford (CUD), a clinical site of the NIH funded Undiagnosed Diseases Network, evaluates patients with rare, undiagnosed diseases in an effort to diagnose patients  ... 
doi:10.1038/s41431-019-0492-4 fatcat:wuudroxpgfe6vfpv2ddzyr7vhi

Graph Representation Learning in Biomedicine [article]

Michelle M. Li, Kexin Huang, Marinka Zitnik
2021 arXiv   pre-print
Biomedical networks are universal descriptors of systems of interacting elements, from protein interactions to disease networks, all the way to healthcare systems and scientific knowledge.  ...  We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces, and capture the breadth of ways in which representation learning  ...  Acknowledgements We gratefully acknowledge the support of NSF under nos.  ... 
arXiv:2104.04883v2 fatcat:7raztbocfngm3pv57l2iwadgre

Artificial Intelligence and Medicine: A literature review [article]

Chottiwatt Jittprasong
2022 arXiv   pre-print
In practically every industry today, artificial intelligence is one of the most effective ways for machines to assist humans.  ...  The tremendous increase in computer and human resources has hastened progress in the 21st century, and it will continue to do so for many years to come.  ...  Generative Adversarial Network (GAN) can be clinically applied along with Transmission Electron Microscopy to detect the infection of COVID-19.  ... 
arXiv:2205.00322v2 fatcat:5f2qcmezjrajbok56xdnl5n4ou

Deep Learning in Science [article]

Stefano Bianchini, Moritz Müller, Pierre Pelletier
2020 arXiv   pre-print
However, the 'DL principle' qualifies for its versatility as the nucleus of a general scientific method that advances science in a measurable way.  ...  interdisciplinary DL applications to disciplinary research within application domains.  ...  Most of the DL applications have been deployed for the accurate prediction of splicing patterns and gene variations, which is a key to providing early diagnosis of various diseases and disorders such as  ... 
arXiv:2009.01575v2 fatcat:4ttqgjdjfjbydp7flnhcgg5p7m

Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications [article]

Kexin Ding, Mu Zhou, Zichen Wang, Qiao Liu, Corey W. Arnold, Shaoting Zhang, Dimitri N. Metaxas
2022 arXiv   pre-print
We discuss the fast-growing use of graph network architectures in medical image analysis to improve disease diagnosis and patient outcomes in clinical practice.  ...  These GCNs capabilities have spawned a new wave of research in medical imaging analysis with the overarching goal of improving quantitative disease understanding, monitoring, and diagnosis.  ...  Interestingly, a combination model of a variational autoencoder and generative adversarial network (VAE-GAN) (Larsen et al.)  ... 
arXiv:2202.08916v3 fatcat:zskcqvgjpnb6vdklmyy5rozswq
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