Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools
暂无分享,去创建一个
Francesca Vitali | Nophar Geifman | Arianna Dagliati | Giovanna Nicora | Riccardo Bellazzi | R. Bellazzi | N. Geifman | Francesca Vitali | G. Nicora | A. Dagliati | F. Vitali
[1] A. Jemal,et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.
[2] Minseon Kim,et al. An Improved Method for Prediction of Cancer Prognosis by Network Learning , 2018, Genes.
[3] N. Pochet,et al. Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response , 2017, bioRxiv.
[4] Aedín C. Culhane,et al. A multivariate approach to the integration of multi-omics datasets , 2014, BMC Bioinformatics.
[5] Zhao-yang Yang,et al. Multiomics analysis on DNA methylation and the expression of both messenger RNA and microRNA in lung adenocarcinoma , 2018, Journal of cellular physiology.
[6] Riccardo Bellazzi,et al. A Network-Based Data Integration Approach to Support Drug Repurposing and Multi-Target Therapies in Triple Negative Breast Cancer , 2016, PloS one.
[7] Dan-Yu Lin,et al. Consistency and overfitting of multi-omics methods on experimental data , 2019, Briefings Bioinform..
[8] Camilla R. Sharkey,et al. Overcoming the loss of blue sensitivity through opsin duplication in the largest animal group, beetles , 2017, Scientific Reports.
[9] Lana X. Garmire,et al. Deep Learning based multi-omics integration robustly predicts survival in liver cancer , 2017, bioRxiv.
[10] Jung-Hwan Yoon,et al. Integrative analysis of genomic and epigenomic regulation of the transcriptome in liver cancer , 2017, Nature Communications.
[11] Wei-Chung Cheng,et al. DriverDBv3: a multi-omics database for cancer driver gene research , 2019, Nucleic Acids Res..
[12] Lana X. Garmire,et al. More Is Better: Recent Progress in Multi-Omics Data Integration Methods , 2017, Front. Genet..
[13] Eric F Lock,et al. JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES. , 2011, The annals of applied statistics.
[14] Jing Wang,et al. LinkedOmics: analyzing multi-omics data within and across 32 cancer types , 2017, Nucleic Acids Res..
[15] Florian Rohart,et al. mixOmics: an R package for ‘omics feature selection and multiple data integration , 2017 .
[16] Jin-Xing Liu,et al. An Integrated Graph Regularized Non-Negative Matrix Factorization Model for Gene Co-Expression Network Analysis , 2019, IEEE Access.
[17] Aidong Zhang,et al. Affinity network fusion and semi-supervised learning for cancer patient clustering. , 2018, Methods.
[18] N. Srinivasan,et al. Use of designed sequences in protein structure recognition , 2018, Biology direct.
[19] Lawrence Carin,et al. Bayesian joint analysis of heterogeneous genomics data , 2014, Bioinform..
[20] Kumardeep Chaudhary,et al. Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer , 2017, Clinical Cancer Research.
[21] Yan Zhao,et al. Drug repositioning: a machine-learning approach through data integration , 2013, Journal of Cheminformatics.
[22] Kim-Anh Lê Cao,et al. mixOmics: An R package for ‘omics feature selection and multiple data integration , 2017, bioRxiv.
[23] Michael Q. Zhang,et al. Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification , 2015, BMC Genomics.
[24] Marina Vannucci,et al. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. , 2018, Biostatistics.
[25] I. Attrée,et al. CLIQ-BID: A method to quantify bacteria-induced damage to eukaryotic cells by automated live-imaging of bright nuclei , 2017, Scientific Reports.
[26] Tieliu Shi,et al. Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma , 2018, Front. Genet..
[27] 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..
[28] Natasa Przulj,et al. Patient-Specific Data Fusion for Cancer Stratification and Personalised Treatment , 2016, PSB.
[29] Eric F. Lock,et al. R.JIVE for exploration of multi-source molecular data , 2016, Bioinform..
[30] S. Knox. From 'omics' to complex disease: a systems biology approach to gene-environment interactions in cancer , 2010, Cancer Cell International.
[31] Jin Gu,et al. Integrative clustering methods of multi-omics data for molecule-based cancer classifications , 2016, Quantitative Biology.
[32] S. Acosta-Jurado,et al. Transcriptomic Studies of the Effect of nod Gene-Inducing Molecules in Rhizobia: Different Weapons, One Purpose , 2017, Genes.
[33] David Warde-Farley,et al. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function , 2008, Genome Biology.
[34] Robert Azencott,et al. Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers , 2015, PloS one.
[35] Zheng Guo,et al. An individualized prognostic signature and multi-omics distinction for early stage hepatocellular carcinoma patients with surgical resection , 2016, Oncotarget.
[36] Xihong Lin,et al. Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma , 2018, EBioMedicine.
[37] Anita Sathyanarayanan,et al. A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping , 2019, Briefings Bioinform..
[38] Pei Wang,et al. Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis* , 2018, Molecular & Cellular Proteomics.
[39] Nathalie Villa-Vialaneix,et al. Unsupervised multiple kernel learning for heterogeneous data integration , 2017, bioRxiv.
[40] Kening Li,et al. ICan: An Integrated Co-Alteration Network to Identify Ovarian Cancer-Related Genes , 2015, PloS one.
[41] E. Diamandis,et al. Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients* , 2019, Molecular & Cellular Proteomics.
[42] Marinka Zitnik,et al. Gene network inference by fusing data from diverse distributions , 2015, Bioinform..
[43] Tim Sprosen,et al. UK Biobank: from concept to reality. , 2005, Pharmacogenomics.
[44] Cesare Furlanello,et al. Multi-omics integration for neuroblastoma clinical endpoint prediction , 2018, Biology Direct.
[45] Stephen T. C. Wong,et al. Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction , 2019, Bioinform..
[46] S. Feo,et al. A multiomics analysis of S100 protein family in breast cancer , 2018, Oncotarget.
[47] H. Ryu,et al. Reconstruction of pathway modification induced by nicotinamide using multi-omic network analyses in triple negative breast cancer , 2017, Scientific Reports.
[48] Laura M. Heiser,et al. A community effort to assess and improve drug sensitivity prediction algorithms , 2014, Nature Biotechnology.
[49] Stephan Beck,et al. Making multi-omics data accessible to researchers , 2019, Scientific Data.
[50] S. Drăghici,et al. A novel approach for data integration and disease subtyping , 2017, Genome research.
[51] A. Goldenberg,et al. Intertumoral Heterogeneity within Medulloblastoma Subgroups. , 2017, Cancer cell.
[52] Tom Michoel,et al. Integrative Multi-omics Module Network Inference with Lemon-Tree , 2014, PLoS Comput. Biol..
[53] Nci Dream Community. A community effort to assess and improve drug sensitivity prediction algorithms , 2014 .
[54] Ron Shamir,et al. NEMO: cancer subtyping by integration of partial multi-omic data , 2018, bioRxiv.
[55] Marcelo A Soares,et al. Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer , 2017, Scientific Reports.
[56] Binhua Tang,et al. Recent Advances of Deep Learning in Bioinformatics and Computational Biology , 2019, Front. Genet..
[57] Hyungwon Choi,et al. iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery , 2019, npj Systems Biology and Applications.
[58] Paula J. Griffin,et al. Detection of multiple perturbations in multi‐omics biological networks , 2015, Biometrics.
[59] Zhuowen Tu,et al. Similarity network fusion for aggregating data types on a genomic scale , 2014, Nature Methods.
[60] Bonnie Berger,et al. Compact Integration of Multi-Network Topology for Functional Analysis of Genes. , 2016, Cell systems.
[61] R. Sharan,et al. PREDICT: a method for inferring novel drug indications with application to personalized medicine , 2011, Molecular systems biology.
[62] Qihua Tan,et al. Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data , 2014, J. Integr. Bioinform..
[63] Benjamin J. Raphael,et al. Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin , 2014, Cell.
[64] Ting Chen,et al. Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[65] Ron Shamir,et al. Constructing module maps for integrated analysis of heterogeneous biological networks , 2014, Nucleic acids research.
[66] Kyungsook Han,et al. Integration of Multi-Omics Data for Gene Regulatory Network Inference and Application to Breast Cancer , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[67] Shimin Zhao,et al. Sirtinol promotes PEPCK1 degradation and inhibits gluconeogenesis by inhibiting deacetylase SIRT2 , 2017, Scientific Reports.
[68] Asoke K. Talukder,et al. Multi-omics Multi-scale Big Data Analytics for Cancer Genomics , 2015, BDA.
[69] Alioune Ngom,et al. A review on machine learning principles for multi-view biological data integration , 2016, Briefings Bioinform..
[70] Niko Beerenwinkel,et al. Network-based integration of multi-omics data for prioritizing cancer genes , 2018, Bioinform..
[71] Yu Jiang,et al. A Selective Review of Multi-Level Omics Data Integration Using Variable Selection , 2019, High-throughput.
[72] Maher Rizkalla,et al. SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer , 2019, Front. Genet..
[73] Kyung-Ah Sohn,et al. Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies , 2019, Biology Direct.
[74] Debashis Ghosh,et al. Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers , 2017, Scientific Reports.
[75] Gary D. Bader,et al. netDx: Interpretable patient classification using integrated patient similarity networks , 2016, bioRxiv.
[76] Lorenz Wernisch,et al. Clusternomics: Integrative context-dependent clustering for heterogeneous datasets , 2017, bioRxiv.
[77] H. Xia,et al. Detecting the potential cancer association or metastasis by multi-omics data analysis. , 2016, Genetics and molecular research : GMR.
[78] Su-In Lee,et al. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia , 2018, Nature Communications.
[79] Emmanuel Barillot,et al. Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients , 2019, Front. Physiol..
[80] J. Marioni,et al. Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets , 2018, Molecular systems biology.
[81] J. Nielsen,et al. Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis , 2018, EBioMedicine.
[82] Nico Pfeifer,et al. Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery , 2015, Bioinform..
[83] Hao Wu,et al. VAMPnets for deep learning of molecular kinetics , 2017, Nature Communications.
[84] Steven J. M. Jones,et al. Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma. , 2017, Cancer cell.
[85] Feng Li,et al. Pan-cancer analysis identifies telomerase-associated signatures and cancer subtypes , 2019, Molecular Cancer.