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A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients

Weizhou Guo, Wenbin Liang, Qingchun Deng, Xianchun Zou
2021 Frontiers in Genetics  
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

Ying Li, Jianing Zhao, Zhaoqian Liu, Cankun Wang, Lizheng Wei, Siyu Han, Wei Du
2021 Frontiers in Genetics  
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?

Lefteris Koumakis
2020 Computational and Structural Biotechnology Journal  
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

Eugene Lin, Chieh-Hsin Lin, Hsien-Yuan Lane
2021 International Journal of Molecular Sciences  
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

Khoa A. Tran, Olga Kondrashova, Andrew Bradley, Elizabeth D. Williams, John V. Pearson, Nicola Waddell
2021 Genome Medicine  
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

Ryuji Hamamoto, Masaaki Komatsu, Ken Takasawa, Ken Asada, Syuzo Kaneko
2019 Biomolecules  
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

Qingxue Zhang, Vincenzo Piuri, Edward A. Clancy, Dian Zhou, Thomas Penzel, Wenchuang Walter Hu
2021 IEEE Access  
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

Jeffrey D. Rudie, Andreas M. Rauschecker, R. Nick Bryan, Christos Davatzikos, Suyash Mohan
2019 Radiology  
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

Daniel Berrar, Werner Dubitzky
2021 Briefings in Bioinformatics  
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)

Eleftherios Trivizakis, Georgios Papadakis, Ioannis Souglakos, Nikolaos Papanikolaou, Lefteris Koumakis, Demetrios Spandidos, Aristidis Tsatsakis, Apostolos Karantanas, Kostas Marias
2020 International Journal of Oncology  
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

Alexandr A Kalinin, Gerald A Higgins, Narathip Reamaroon, Sayedmohammadreza Soroushmehr, Ari Allyn-Feuer, Ivo D Dinov, Kayvan Najarian, Brian D Athey
2018 Pharmacogenomics (London)  
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

Xiao-Xia Yin, Mingyong Gao, Wei Wang, Yanchun Zhang, Le Sun
2022 Computational Intelligence and Neuroscience  
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

Andreas S. Panayides, Marios Pattichis, Stephanos Leandrou, Costas Pitris, Anastasia Constantinidou, Constantinos S. Pattichis
2019 IEEE journal of biomedical and health informatics  
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

Shangran Qiu, Megan S. Heydari, Matthew I. Miller, Prajakta S. Joshi, Benjamin C. Wong, Rhoda Au, Vijaya B. Kolachalama
2019 Alzheimer's & Dementia  
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]

Tao Zhou, Kim-Han Thung, Xiaofeng Zhu, Dinggang Shen
2017 Lecture Notes in Computer Science  
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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