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MultiGATAE: A Novel Cancer Subtype Identification Method Based on Multi-Omics and Attention Mechanism
2022
Frontiers in Genetics
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This is considered as the main problem which limits the precision treatment of cancer. Thus, cancer subtypes identification is of great importance for cancer diagnosis and treatment. ...
Cancer is one of the leading causes of death worldwide, which brings an urgent need for its effective treatment. ...
All authors have read and approved the final version of manuscript.
FUNDING ...
doi:10.3389/fgene.2022.855629
pmid:35391797
pmcid:PMC8979770
fatcat:v5cs2wle6zcxxox6aewnnni2ku
Similarity Network Fusion Based on Random Walk and Relative Entropy for Cancer Subtype Prediction of Multigenomic Data
2021
Scientific Programming
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In this paper, we proposed similarity network fusion based on random walk and relative entropy (R2SNF) for cancer subtype prediction. ...
Among them, similarity network fusion (SNF) can integrate multiple types of genomic data to identify cancer subtypes, which improves the understanding of tumorigenesis. ...
Acknowledgments is work was supported by the National Natural Science Foundation of China (Grant nos. 61906198, 61976215, and 61772532) and the Natural Science Foundation of Jiangsu Province (Grant no. ...
doi:10.1155/2021/2292703
fatcat:iydddjsqbfgm5hmyusng5jhiqe
Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction
2021
Frontiers in Genetics
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In recent years, some multi-view clustering algorithms have been proposed and applied to the prediction of cancer subtypes. ...
It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data. ...
AUTHOR CONTRIBUTIONS JL and XW constructed the original idea and designed the experiments. JL and SG wrote the manuscript. JL and YC proofread the manuscript. ...
doi:10.3389/fgene.2021.718915
pmid:34552619
pmcid:PMC8450448
fatcat:gazyrxlrb5egth5vcdeqfifvx4
Editorial: Computational Learning Models and Methods Driven by Omics for Precision Medicine
2020
Frontiers in Genetics
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They include sequencing alignment, correlation detection between omics data and biological traits, prediction of biological functionality, computational methods for cancer subtyping, finding of pathogenic ...
The models combined with the deep learning method help to discover potential non-linear associations. ...
ACKNOWLEDGMENTS We thank all the authors who contributed to this topic. ...
doi:10.3389/fgene.2020.620976
pmid:33424938
pmcid:PMC7785880
fatcat:s4ya4eey7bh5ld7eqiae2hpoba
Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review
2022
Frontiers in Genetics
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Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer's Disease Neuroimaging Initiative, and Genotype-Tissue Expression ...
We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration. ...
To address this issue, DSSF (Deep Subspace Similarity Fusion) (Yang et al., 2018) employs an auto-encoder to improve the discriminative similarity between samples. ...
doi:10.3389/fgene.2022.854752
pmid:35391796
pmcid:PMC8981526
fatcat:ijmwfu264rbgtaen66tkbr4sgu
Integrative subspace clustering by common and specific decomposition for applications on cancer subtype identification
2019
BMC Medical Genomics
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Computational analysis of the multi-omics datasets could potentially reveal deep insights for a given disease. ...
Furthermore, they could not identify the conflicting parts for each view, which might be important in applications such as cancer subtype identification. ...
For example, the similarity network fusion (SNF) [4] fuses multiple networks to one network by iteratively updating a sequence of nonnegative status matrices. ...
doi:10.1186/s12920-019-0633-1
pmid:31874642
pmcid:PMC6929329
fatcat:qhifwgs4jjhfhe6i7pasdap7h4
2020 Index IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 17
2021
IEEE/ACM Transactions on Computational Biology & Bioinformatics
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P Pain Classification of Patients with Coronary Microvascular Dysfunction. ...
., +, TCBB March-April 2020 704-711 Pancreas Data-Driven Robust Control for a Closed-Loop Artificial Pancreas. 1981 -1993 ...
-Feb. 2020 149-157 Low-Rank Joint Subspace Construction for Cancer Subtype Discovery. ...
doi:10.1109/tcbb.2020.3047571
fatcat:x3kmrpexsve6bnjtd3dh6ntkyy
Gene Transformer: Transformers for the Gene Expression-based Classification of Lung Cancer Subtypes
[article]
2021
arXiv
pre-print
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Over the past decade, conventional ML algorithms and DL-based CNNs have been espoused for the classification of cancer subtypes from gene expression datasets. ...
The classification results show that Gene Transformer can be an efficient approach for classifying cancer subtypes, indicating that any improvement in deep learning models in computational biology can ...
The performance of deep cancer subtype classification (DeepCC) [21] , an innovative cancer molecular subtype classification framework, has been demonstrated for case studies on colorectal and breast cancer ...
arXiv:2108.11833v3
fatcat:we55g22pkvfvtiqhsi7tmvx6yq
2021 Index IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 18
2022
IEEE/ACM Transactions on Computational Biology & Bioinformatics
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
-Oct. 2021 1958-1969 Data fusion Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction. Yang, B., +, TCBB Jan. ...
doi:10.1109/tcbb.2021.3136340
fatcat:bjvb334webfovh4nsc7oeds3di
Joint Nonnegative Matrix Factorization Based on Sparse and Graph Laplacian Regularization for Clustering and Co-Differential Expression Genes Analysis
2020
Complexity
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First, SG-jNMF1 projects multiomics data into a common subspace and applies the multiomics fusion characteristic matrix to mine the important information closely related to diseases. ...
SG-jNMF provides an efficient integrative analysis method for mining the biological information hidden in heterogeneous multiomics data. ...
Acknowledgments is work was supported in part by the grants from the National Natural Science Foundation of China, nos. 61902215 and 61702299. ...
doi:10.1155/2020/3917812
fatcat:3lx464qyeraqjp5vgdieoe7nnq
Visual sentiment analysis via deep multiple clustered instance learning
2020
Journal of Intelligent & Fuzzy Systems
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A multi-head mechanism is integrated to form MIL ensembles, which enables to weigh the contribution of each clustered instance in different subspaces for generating more robust bag representation. ...
We propose a deep multiple clustered instance learning formulation, under which a deep multiple clustered instance learning network (DMCILN) is constructed for visual sentiment analysis. ...
(15) When fusing the output of each subspace, we consider two fusion operators for producing the integrated bag representation: average fusion and concatenation fusion. ...
doi:10.3233/jifs-200675
fatcat:ix3hqllr7vdb5b76nvtdt6lyhe
PRECISE+ predicts drug response in patients by non-linear subspace-based transfer from cell lines and PDX models
[article]
2020
bioRxiv
pre-print
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Pre-clinical models have been the workhorse of cancer research for decades. ...
The quest for biomarkers of drug response signatures has been particularly challenging, suffering from poor translatability from pre-clinical models to human tumors. ...
cancer 1 , the BRAF V600E mutation in skin melanoma 2 or the BCR/ABL fusion in Figure 1A ). ...
doi:10.1101/2020.06.29.177139
fatcat:dr4k3izdzje3hoaqcemyta6uum
Investigation of Capsule-Inspired Neural Network Approaches for DNA Methylation
[article]
2020
bioRxiv
pre-print
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DNAm deep-learning approaches can capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. ...
Our methodology presents opportunities to increase interpretability of disease mechanisms through utilization of biologically relevant annotations. ...
James 997 O'Malley for their thoughtful discussions. 998 999 ...
doi:10.1101/2020.08.14.251306
fatcat:fyu6yiot6rejtiohif5prlfimu
Multiview learning for understanding functional multiomics
2020
PLoS Computational Biology
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and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems. ...
Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits ...
, which could be used to explain the different mechanisms for each subtype of cancer development. ...
doi:10.1371/journal.pcbi.1007677
pmid:32240163
pmcid:PMC7117667
fatcat:jqpizdutnrgtnlymfmjhh4qo4q
A Novel Multispace Image Reconstruction Method for Pathological Image Classification Based on Structural Information
2019
BioMed Research International
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Pathological image classification is of great importance in various biomedical applications, such as for lesion detection, cancer subtype identification, and pathological grading. ...
Subsequently, the long short-term memory (LSTM) layer was used for feature selection and refinement while increasing its discrimination capability. ...
Acknowledgments This work was supported by the National Natural Science Foundation of China (No. 61872075). ...
doi:10.1155/2019/3530903
fatcat:bwnv2asl5naendyaizyppe46qm
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