A citation-based method for searching scientific literature

Hai Yang, Rui Chen, Dongdong Li, Zhe Wang. Bioinformatics 2021
Times Cited: 13







List of co-cited articles
94 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Similarity network fusion for aggregating data types on a genomic scale.
Bo Wang, Aziz M Mezlini, Feyyaz Demir, Marc Fiume, Zhuowen Tu, Michael Brudno, Benjamin Haibe-Kains, Anna Goldenberg. Nat Methods 2014
706
76

Multi-omic and multi-view clustering algorithms: review and cancer benchmark.
Nimrod Rappoport, Ron Shamir. Nucleic Acids Res 2018
145
61


NEMO: cancer subtyping by integration of partial multi-omic data.
Nimrod Rappoport, Ron Shamir. Bioinformatics 2019
60
61

Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.
Kumardeep Chaudhary, Olivier B Poirion, Liangqun Lu, Lana X Garmire. Clin Cancer Res 2018
343
53

A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.
Qianxing Mo, Ronglai Shen, Cui Guo, Marina Vannucci, Keith S Chan, Susan G Hilsenbeck. Biostatistics 2018
74
46



Pattern discovery and cancer gene identification in integrated cancer genomic data.
Qianxing Mo, Sijian Wang, Venkatraman E Seshan, Adam B Olshen, Nikolaus Schultz, Chris Sander, R Scott Powers, Marc Ladanyi, Ronglai Shen. Proc Natl Acad Sci U S A 2013
226
38

More Is Better: Recent Progress in Multi-Omics Data Integration Methods.
Sijia Huang, Kumardeep Chaudhary, Lana X Garmire. Front Genet 2017
311
38

PINSPlus: a tool for tumor subtype discovery in integrated genomic data.
Hung Nguyen, Sangam Shrestha, Sorin Draghici, Tin Nguyen. Bioinformatics 2019
36
38

Multi-omics Data Integration, Interpretation, and Its Application.
Indhupriya Subramanian, Srikant Verma, Shiva Kumar, Abhay Jere, Krishanpal Anamika. Bioinform Biol Insights 2020
247
38

A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data.
Jing Xu, Peng Wu, Yuehui Chen, Qingfang Meng, Hussain Dawood, Hassan Dawood. BMC Bioinformatics 2019
29
30

A multivariate approach to the integration of multi-omics datasets.
Chen Meng, Bernhard Kuster, Aedín C Culhane, Amin Moghaddas Gholami. BMC Bioinformatics 2014
132
30

Methods of integrating data to uncover genotype-phenotype interactions.
Marylyn D Ritchie, Emily R Holzinger, Ruowang Li, Sarah A Pendergrass, Dokyoon Kim. Nat Rev Genet 2015
469
30

Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival.
Daniele Ramazzotti, Avantika Lal, Bo Wang, Serafim Batzoglou, Arend Sidow. Nat Commun 2018
66
30

Unsupervised multiple kernel learning for heterogeneous data integration.
Jérôme Mariette, Nathalie Villa-Vialaneix. Bioinformatics 2018
39
30

A novel approach for data integration and disease subtyping.
Tin Nguyen, Rebecca Tagett, Diana Diaz, Sorin Draghici. Genome Res 2017
73
30

Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data.
Qianqian Shi, Chuanchao Zhang, Minrui Peng, Xiangtian Yu, Tao Zeng, Juan Liu, Luonan Chen. Bioinformatics 2017
36
30

moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets.
Chen Meng, Dominic Helm, Martin Frejno, Bernhard Kuster. J Proteome Res 2016
51
30

Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.
Katherine A Hoadley, Christina Yau, Denise M Wolf, Andrew D Cherniack, David Tamborero, Sam Ng, Max D M Leiserson, Beifang Niu, Michael D McLellan, Vladislav Uzunangelov,[...]. Cell 2014
893
30

The Cancer Genome Atlas Pan-Cancer analysis project.
John N Weinstein, Eric A Collisson, Gordon B Mills, Kenna R Mills Shaw, Brad A Ozenberger, Kyle Ellrott, Ilya Shmulevich, Chris Sander, Joshua M Stuart. Nat Genet 2013
30


Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.
Ricard Argelaguet, Britta Velten, Damien Arnol, Sascha Dietrich, Thorsten Zenz, John C Marioni, Florian Buettner, Wolfgang Huber, Oliver Stegle. Mol Syst Biol 2018
289
23

Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data.
Edian F Franco, Pratip Rana, Aline Cruz, Víctor V Calderón, Vasco Azevedo, Rommel T J Ramos, Preetam Ghosh. Cancers (Basel) 2021
10
30

Discovery of multi-dimensional modules by integrative analysis of cancer genomic data.
Shihua Zhang, Chun-Chi Liu, Wenyuan Li, Hui Shen, Peter W Laird, Xianghong Jasmine Zhou. Nucleic Acids Res 2012
188
23


Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data.
Yuqi Lin, Wen Zhang, Huanshen Cao, Gaoyang Li, Wei Du. Genes (Basel) 2020
18
23

JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES.
Eric F Lock, Katherine A Hoadley, J S Marron, Andrew B Nobel. Ann Appl Stat 2013
190
23

MOLI: multi-omics late integration with deep neural networks for drug response prediction.
Hossein Sharifi-Noghabi, Olga Zolotareva, Colin C Collins, Martin Ester. Bioinformatics 2019
89
23

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.
Giovanna Nicora, Francesca Vitali, Arianna Dagliati, Nophar Geifman, Riccardo Bellazzi. Front Oncol 2020
56
23

A review on machine learning principles for multi-view biological data integration.
Yifeng Li, Fang-Xiang Wu, Alioune Ngom. Brief Bioinform 2018
155
23

Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration.
Morgane Pierre-Jean, Jean-François Deleuze, Edith Le Floch, Florence Mauger. Brief Bioinform 2020
20
23

Multi-omics approaches to disease.
Yehudit Hasin, Marcus Seldin, Aldons Lusis. Genome Biol 2017
736
23

Methods for the integration of multi-omics data: mathematical aspects.
Matteo Bersanelli, Ettore Mosca, Daniel Remondini, Enrico Giampieri, Claudia Sala, Gastone Castellani, Luciano Milanesi. BMC Bioinformatics 2016
187
23

Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data.
Runpu Chen, Le Yang, Steve Goodison, Yijun Sun. Bioinformatics 2020
37
23

Bayesian consensus clustering.
Eric F Lock, David B Dunson. Bioinformatics 2013
116
23

Extensions of sparse canonical correlation analysis with applications to genomic data.
Daniela M Witten, Robert J Tibshirani. Stat Appl Genet Mol Biol 2009
218
23

Bayesian correlated clustering to integrate multiple datasets.
Paul Kirk, Jim E Griffin, Richard S Savage, Zoubin Ghahramani, David L Wild. Bioinformatics 2012
120
23

Integrating multi-omics data through deep learning for accurate cancer prognosis prediction.
Hua Chai, Xiang Zhou, Zhongyue Zhang, Jiahua Rao, Huiying Zhao, Yuedong Yang. Comput Biol Med 2021
14
23



Interpretation of deep learning in genomics and epigenomics.
Amlan Talukder, Clayton Barham, Xiaoman Li, Haiyan Hu. Brief Bioinform 2021
20
15

Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma.
Li Zhang, Chenkai Lv, Yaqiong Jin, Ganqi Cheng, Yibao Fu, Dongsheng Yuan, Yiran Tao, Yongli Guo, Xin Ni, Tieliu Shi. Front Genet 2018
63
15

A Comprehensive Survey on Graph Neural Networks.
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S Yu. IEEE Trans Neural Netw Learn Syst 2021
270
15

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling.
Marco Chierici, Nicole Bussola, Alessia Marcolini, Margherita Francescatto, Alessandro Zandonà, Lucia Trastulla, Claudio Agostinelli, Giuseppe Jurman, Cesare Furlanello. Front Oncol 2020
15
15


Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach.
Marie de Tayrac, Sébastien Lê, Marc Aubry, Jean Mosser, François Husson. BMC Genomics 2009
62
15

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer.
Laura Cantini, Pooya Zakeri, Celine Hernandez, Aurelien Naldi, Denis Thieffry, Elisabeth Remy, Anaïs Baudot. Nat Commun 2021
30
15



Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.