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A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients
2021
Frontiers in Genetics
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Additionally, our method can be extended to the survival prediction of other cancer diseases, providing a new strategy for other diseases prognosis. ...
We propose a multimodal data fusion model based on a novel multimodal affinity fusion network (MAFN) for survival prediction of breast cancer by integrating gene expression, copy number alteration, and ...
At the same time, the feasibility of deep neural network with multimodal data fusion and the practicability of multimodal data in the prediction of breast cancer prognosis are further proved. ...
doi:10.3389/fgene.2021.709027
pmid:34490038
pmcid:PMC8417828
fatcat:qjzmc4sn4bbiben7pyhpd62waq
De novo Prediction of Moonlighting Proteins Using Multimodal Deep Ensemble Learning
2021
Frontiers in Genetics
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Furthermore, the distribution of predicted MPs on different chromosomes, the evolution of MPs, the association of MPs with diseases, and the functional enrichment of MPs are also explored. ...
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
Deep learning models in genomics; are we there yet?
2020
Computational and Structural Biotechnology Journal
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We foresee deep learning accelerating changes in the area of genomics, especially for multi-scale and multimodal data analysis for precision medicine. ...
Since genomics produce big data, most of the bioinformatics algorithms are based on machine learning methodologies, and lately deep learning, to identify patterns, make predictions and model the progression ...
Acknowledgments We acknowledge support of this work by the project ''ELIXIR-GR: Hellenic Research Infrastructure for the Management and Analysis of Data from the Biological Sciences" (MIS 5002780) which ...
doi:10.1016/j.csbj.2020.06.017
pmid:32637044
pmcid:PMC7327302
fatcat:txpr265ynbhkzellqdraywmgoi
Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease
2021
International Journal of Molecular Sciences
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A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). ...
In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. ...
GANs with Gene Expression Park, J. et al. [42] used a deep learning-based model to predict the virtual disease/molecular progress of AD using gene expression data from the AD model of mouse data. ...
doi:10.3390/ijms22157911
fatcat:x5rhz7wlx5gmbjgeezjy3mbl34
Deep learning in cancer diagnosis, prognosis and treatment selection
2021
Genome Medicine
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The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the ...
Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning. ...
Acknowledgements Khoa Tran was the recipient of the Maureen and Barry Stevenson PhD Scholarship, we are grateful to Maureen Stevenson for her support. ...
doi:10.1186/s13073-021-00968-x
pmid:34579788
pmcid:PMC8477474
fatcat:y73fumwdazft3pw47gqdcncnue
Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine
2019
Biomolecules
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Importantly, the current progress of artificial intelligence (AI) technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data. ...
treatment of a wide range of diseases is promoted. ...
The authors show a great gratitude to the past and present members of Hamamoto Laboratory.
Conflicts of Interest: The authors declare that they have no conflicts of interest. ...
doi:10.3390/biom10010062
pmid:31905969
pmcid:PMC7023005
fatcat:k255jp2lvvhvjpqgygjlf3ezoa
IEEE Access Special Section Editorial: Advanced Information Sensing and Learning Technologies for Data-Centric Smart Health Applications
2021
IEEE Access
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CLANCY (Senior Member, IEEE) received the B.S. degree from Worcester Polytechnic Institute (WPI), and the S.M. and Ph.D. degrees from the Massachusetts Institute of Technology (MIT), all in electrical ...
He is currently a Professor of electrical and computer engineering, and of biomedical engineering, WPI. ...
The results suggest an effective association between genes and related diseases. ...
doi:10.1109/access.2021.3057527
fatcat:fpjlv5c4qfbdnp5aroj2srsqhq
Emerging Applications of Artificial Intelligence in Neuro-Oncology
2019
Radiology
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Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding ...
The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. ...
Acknowledgments: The authors thank Saima Rathore, PhD, and the Center for Biomedical Image Computing and Analytics for providing several of the images used in this review. ...
doi:10.1148/radiol.2018181928
pmid:30667332
pmcid:PMC6389268
fatcat:o62zg4uqajhnpds443vurz4p3u
Deep learning in bioinformatics and biomedicine
2021
Briefings in Bioinformatics
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As many problems in the life sciences require the decoding of complex interactions between entities (e.g. genes, proteins), and given the wealth of multimodal data, deep learning methods are believed to ...
In their article titled 'Biological network analysis with deep learning', Muzio et al. review the state of the art of deep learning methods for the analysis of graph data. ...
doi:10.1093/bib/bbab087
pmid:33693457
pmcid:PMC8485073
fatcat:znwucse3gzfcfoywxq4qp66l6m
Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)
2020
International Journal of Oncology
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The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications. ...
The main deep learning radiogenomics architectures, together with the clinical questions addressed, and the achieved genetic or molecular correlations are presented, while a performance comparison of the ...
Technologies in the Preservation of Cultural Heritage and the Tackling of Societal Challenges'). ...
doi:10.3892/ijo.2020.5063
pmid:32467997
pmcid:PMC7252460
fatcat:dwgljcnhfzhllbfmb2p4ujtb3e
Deep learning in pharmacogenomics: from gene regulation to patient stratification
2018
Pharmacogenomics (London)
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Deep learning encapsulates a family of machine learning algorithms that over the last decade has transformed many important subfields of artificial intelligence (AI) and has demonstrated breakthrough performance ...
We anticipate that in the future deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular ...
Other recent applications of deep learning models to prediction of regulatory elements and their interactions with the state-of-the-art performance include enhancer prediction [80] [81] [82] ; classification ...
doi:10.2217/pgs-2018-0008
pmid:29697304
fatcat:tkhmrqkevjfqxdty6ttbw33jam
MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method
2022
Computational Intelligence and Neuroscience
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Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging ...
of extensive exploration on it over the past years. ...
Acknowledgments is work was funded by Science and Technology Projects in Guangzhou, China (Grant no. 202102010472) and National Natural Science Foundation of China (NSFC) (Grant no. 62176071). ...
doi:10.1155/2022/2703350
pmid:35845886
pmcid:PMC9282990
fatcat:ttv3jgryozauhmxqonpef3qngi
Radiogenomics for Precision Medicine With A Big Data Analytics Perspective
2019
IEEE journal of biomedical and health informatics
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The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. ...
More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational ...
Deep Learning The early growth of Deep learning methods greatly benefited from the availability of ImageNet, a big data source, associated with the Large Scale Visual Recognition Challenge (ILSVRC) [55 ...
doi:10.1109/jbhi.2018.2879381
pmid:30596591
fatcat:rqmjhmdmr5h3rdaody264ogs24
ENHANCING DEEP LEARNING MODEL PERFORMANCE FOR AD DIAGNOSIS USING ROI-BASED SELECTION
2019
Alzheimer's & Dementia
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Conclusions: Identification of highly predictive ROI from whole brain volume is an efficient paradigm for enhancing AD classification using deep learning. ...
These findings prompted the question whether the M129V polymorphism of the PRNP gene is associated with the risk of MCI in the Rotterdam Study. ...
doi:10.1016/j.jalz.2019.06.674
fatcat:pnfk6cnqi5fb3cs7w2a66vqczq
Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-status Dementia Diagnosis
[chapter]
2017
Lecture Notes in Computer Science
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In this paper, we aim to maximally utilize multimodality neuroimaging and genetic data to predict Alzheimer's disease (AD) and its prodromal status, i.e., a multi-status dementia diagnosis problem. ...
To this end, we present a novel three-stage deep feature learning and fusion framework, where the deep neural network is trained stage-wise. ...
[4] used unsupervised deep learning methods such as Stack Auto-Encoder (SAE) to enhance cancer diagnosis using gene expression data, and Suk et al. [13] and Liu et al. ...
doi:10.1007/978-3-319-67389-9_16
pmid:29376149
pmcid:PMC5786435
fatcat:4rienl77gnfa5fd6coyymfblxy
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