Learning vector quantized representation for cancer subtypes identification

[1]  Chen Li,et al.  Hierarchical Categorical Generative Modeling for Multi-omics Cancer Subtyping , 2022, 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[2]  Huanming Yang,et al.  Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale , 2022, Nature Machine Intelligence.

[3]  Shanmin Pang,et al.  Deep Subspace Mutual Learning for cancer subtypes prediction , 2021, Bioinform..

[4]  Jiawei Luo,et al.  Cancer subtype identification by consensus guided graph autoencoders , 2021, Bioinform..

[5]  Yike Guo,et al.  XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data , 2021, Briefings Bioinform..

[6]  Muta Tah Hira,et al.  Integrated multi-omics analysis of ovarian cancer using variational autoencoders , 2021, Scientific Reports.

[7]  Barbara E. Engelhardt,et al.  Contrastive latent variable modeling with application to case-control sequencing experiments , 2021, The Annals of Applied Statistics.

[8]  Sangseon Lee,et al.  Cancer subtype classification and modeling by pathway attention and propagation , 2020, Bioinform..

[9]  Yike Guo,et al.  Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification , 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[10]  Wei Wang,et al.  DeepCC: a novel deep learning-based framework for cancer molecular subtype classification , 2019, Oncogenesis.

[11]  Wenbin Hu,et al.  FSM: A Fast Similarity Measurement for Gene Regulatory Networks via Genes' Influence Power , 2019, IJCAI.

[12]  Mainak Biswas,et al.  Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms , 2019, Comput. Methods Programs Biomed..

[13]  Diederik P. Kingma,et al.  An Introduction to Variational Autoencoders , 2019, Found. Trends Mach. Learn..

[14]  Jiazhou Chen,et al.  Simultaneous Interrogation of Cancer Omics to Identify Subtypes With Significant Clinical Differences , 2019, Front. Genet..

[15]  Sergey Levine,et al.  Wasserstein Dependency Measure for Representation Learning , 2019, NeurIPS.

[16]  Yadong Wang,et al.  Exploring DNA Methylation Data of Lung Cancer Samples with Variational Autoencoders , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[17]  Karl Stratos,et al.  Formal Limitations on the Measurement of Mutual Information , 2018, AISTATS.

[18]  Dongdong Sun,et al.  Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome , 2018, Comput. Methods Programs Biomed..

[19]  Ashton C. Berger,et al.  A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers. , 2018, Cancer cell.

[20]  Oriol Vinyals,et al.  Neural Discrete Representation Learning , 2017, NIPS.

[21]  Alexander A. Alemi,et al.  Fixing a Broken ELBO , 2017, ICML.

[22]  Juan Liu,et al.  Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data , 2017, Bioinform..

[23]  Shuiwang Ji,et al.  Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation , 2017, SDM.

[24]  Kumardeep Chaudhary,et al.  Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer , 2017, Clinical Cancer Research.

[25]  P. Boutros,et al.  Comparing continuous and discrete analyses of breast cancer survival information. , 2016, Genomics.

[26]  Zhonghu Bai,et al.  Breast cancer intrinsic subtype classification, clinical use and future trends. , 2015, American journal of cancer research.

[27]  E. Mardis,et al.  Development and verification of the PAM50-based Prosigna breast cancer gene signature assay , 2015, BMC Medical Genomics.

[28]  Nico Pfeifer,et al.  Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery , 2015, Bioinform..

[29]  N. Novère Quantitative and logic modelling of molecular and gene networks , 2015, Nature Reviews Genetics.

[30]  Kevin Y. Yip,et al.  FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer , 2014, Genome Biology.

[31]  Zhuowen Tu,et al.  Similarity network fusion for aggregating data types on a genomic scale , 2014, Nature Methods.

[32]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[33]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[34]  C. Sander,et al.  Pattern discovery and cancer gene identification in integrated cancer genomic data , 2013, Proceedings of the National Academy of Sciences.

[35]  Donald A. Berry,et al.  PAM50 proliferation score as a predictor of weekly paclitaxel benefit in breast cancer , 2013, Breast Cancer Research and Treatment.

[36]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[37]  E. Mardis,et al.  A 50-Gene Intrinsic Subtype Classifier for Prognosis and Prediction of Benefit from Adjuvant Tamoxifen , 2012, Clinical Cancer Research.

[38]  L. Pusztai,et al.  Gene expression profi ling in breast cancer: classifi cation, prognostication, and prediction , 2011 .

[39]  R. Gelber,et al.  Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011 , 2011, Annals of oncology : official journal of the European Society for Medical Oncology.

[40]  Mark T. W. Ebbert,et al.  A Comparison of PAM50 Intrinsic Subtyping with Immunohistochemistry and Clinical Prognostic Factors in Tamoxifen-Treated Estrogen Receptor–Positive Breast Cancer , 2010, Clinical Cancer Research.

[41]  L. Wessels,et al.  Molecular subtyping of breast cancer: ready to use? , 2010, The Lancet. Oncology.

[42]  Lajos Pusztai,et al.  Gene-expression signatures in breast cancer. , 2009, The New England journal of medicine.

[43]  E. Ott,et al.  The effect of network topology on the stability of discrete state models of genetic control , 2009, Proceedings of the National Academy of Sciences.

[44]  Markus Ringnér,et al.  What is principal component analysis? , 2008, Nature Biotechnology.

[45]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[46]  K. Aldape,et al.  Integrated array-comparative genomic hybridization and expression array profiles identify clinically relevant molecular subtypes of glioblastoma. , 2005, Cancer research.

[47]  Pablo Tamayo,et al.  Metagenes and molecular pattern discovery using matrix factorization , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[48]  R. Tibshirani,et al.  Repeated observation of breast tumor subtypes in independent gene expression data sets , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[49]  D. Botstein,et al.  For Personal Use. Only Reproduce with Permission from the Lancet Publishing Group , 2022 .

[50]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[51]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[52]  R. Hruban,et al.  Alterations in pancreatic, biliary, and breast carcinomas support MKK4 as a genetically targeted tumor suppressor gene. , 1998, Cancer research.

[53]  OUP accepted manuscript , 2021, Briefings In Bioinformatics.

[54]  Marina Vannucci,et al.  A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. , 2018, Biostatistics.

[55]  Rony Seger,et al.  The MAP kinase signaling cascades: a system of hundreds of components regulates a diverse array of physiological functions. , 2010, Methods in molecular biology.

[56]  Adam B. Olshen,et al.  Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis , 2009, Bioinform..

[57]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .