An Integrated Approach to Uncover Drivers of Cancer

Cited by (484)

Items 201–300 of 484

1. Prognostic significance of Rab27 expression in solid cancer: a systematic review and meta-analysis
   Hyun Min Koh, Bo Gun Jang, Dong Chul Kim · 2020 · Scientific Reports
2. Deciphering oncogenic drivers: from single genes to integrated pathways
   J. Chen, Min Sun, Bairong Shen · 2014 · Briefings in Bioinformatics
3. Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network
   Olfat Al‐Harazi, Sadiq Al Insaif, Monirah Al-Ajlan, Namik Kaya, Nduna Dzimiri, Dilek Çolak · 2015 · Journal of genetics and genomics/Journal of Genetics and Genomics
4. MutComFocal: an integrative approach to identifying recurrent and focal genomic alterations in tumor samples
   Vladimir Trifonov, Laura Pasqualucci, Riccardo Dalla Favera, Raúl Rabadán · 2013 · BMC Systems Biology
5. Generating and Analyzing Fish Models of Melanoma
   E. Elizabeth Patton, Marie E. Mathers, Manfred Schartl · 2011 · Methods in cell biology
6. CaMoDi: a new method for cancer module discovery
   Alexandros Manolakos, Idoia Ochoa, Kartik Venkat, Andrea J. Goldsmith, Olivier Gevaert · 2014 · BMC Genomics
7. Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification
   Abolfazl Doostparast Torshizi, Linda Ruth Petzold · 2017 · Journal of the American Medical Informatics Association
8. ConsensusDriver Improves upon Individual Algorithms for Predicting Driver Alterations in Different Cancer Types and Individual Patients
   Denis Bertrand, Sibyl Drissler, Burton Kuan Hui Chia, Jia Yu Koh, Chenhao Li, Chayaporn Suphavilai, Iain Beehuat Tan, Niranjan Nagarajan · 2017 · Cancer Research
9. Estimation of Copy Number Alterations from Exome Sequencing Data
   Rafael Valdés‐Mas, Sı́lvia Beà, Diana A. Puente, Carlos López-Otı́n, Xosé S. Puente · 2012 · PLoS ONE
10. Network-based methods for identifying critical pathways of complex diseases: a survey
    Qiaosheng Eric Zhang, Jie Li, Hanqing Xue, Leilei Kong, Yadong Wang · 2016 · Molecular BioSystems
11. A novel function of Prohibitin on melanosome transport in melanocytes
    Chan Song Jo, Hye In Park, Hyun Jin Jung, Jong‐Il Park, Ji‐Eun Lee, Cheol Hwan Myung, Jae Sung Hwang · 2020 · Theranostics
12. Importance of rare gene copy number alterations for personalized tumor characterization and survival analysis
    Michael Seifert, Betty Friedrich, Andreas Beyer · 2016 · Genome biology
13. Mechanisms of chromosomal instability in melanoma
    William K. Kaufmann, Craig C. Carson, Bernard Omolo, Adam J. Filgo, Maria J. Sambade, Dennis A. Simpson, Janiel M. Shields, Joseph G. Ibrahim, Nancy E. Thomas · 2014 · Environmental and Molecular Mutagenesis
14. Myosin-Va Contributes to Manifestation of Malignant-Related Properties in Melanoma Cells
    Cleidson Padua Alves, Milene Höehr de Moraes, Josane F. Sousa, Carmen Lucia Salla Pontes, Anelisa Ramão, Satoru Yokoyama, Daniel Maragno Trindade, David E. Fisher, Enilza Maria Espreáfico · 2013 · Journal of Investigative Dermatology
15. Strategies for Integrated Analysis of Genetic, Epigenetic, and Gene Expression Variation in Cancer: Addressing the Challenges
    Louise Bruun Thingholm, Lars van Brakel Andersen, Enes Makalic, Melissa C. Southey, Mads Thomassen, Lise Lotte Hansen · 2016 · Frontiers in Genetics
16. Overcoming MITF-conferred drug resistance through dual AURKA/MAPK targeting in human melanoma cells
    Gaurav Pathria, Bhavuk Garg, Viola Borgdorff, Kanika Garg, C Wagner, Giulio Superti‐Furga, Stephan N. Wagner · 2016 · Cell Death and Disease
17. Identification of mutated driver pathways in cancer using a multi-objective optimization model
    Chun-Hou Zheng, Yang Wu, Yanwen Chong, Junfeng Xia · 2016 · Computers in Biology and Medicine
18. Significance of the E3 ubiquitin protein UBR5 as an oncogene and a prognostic biomarker in colorectal cancer
    Zhongdong Xie, Han Liang, Jinmeng Wang, Xiaowen Xu, Yan Zhu, Aizhen Guo, Xian Shen, Fuao Cao, Wenjun Chang · 2017 · Oncotarget
19. A data-driven interactome of synergistic genes improves network-based cancer outcome prediction
    Amin Allahyar, Joske Ubels, Jeroen de Ridder · 2019 · PLoS Computational Biology
20. Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction
    Bo Yang, Yupei Zhang, Shanmin Pang, Xuequn Shang, Xueqing Zhao, Minghui Han · 2019 · IEEE Transactions on Computational Biology and Bioinformatics
21. Network Analysis for Complex Neurodegenerative Diseases
    Claudia Manzoni, Patrick Alfryn Lewis, Raffaele Ferrari · 2020 · Current Genetic Medicine Reports
22. Chromatin remodeling system, cancer stem-like attractors, and cellular reprogramming
    Yue Zhang, Hisashi Moriguchi · 2011 · Cellular and Molecular Life Sciences
23. Efficient Dimensionality Reduction for High-Dimensional Network Estimation
    Safiye Çelik, Benjamin A. Logsdon, Su‐In Lee · 2014 · International Conference on Machine Learning
24. Identification of cancer genes that are independent of dominant proliferation and lineage programs
    Laura M. Selfors, Daniel G. Stover, Isaac S. Harris, Joan S. Brugge, Jonathan L. Coloff · 2017 · Proceedings of the National Academy of Sciences
25. IDENTIFYING MASTER REGULATORS OF CANCER AND THEIR DOWNSTREAM TARGETS BY INTEGRATING GENOMIC AND EPIGENOMIC FEATURES
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26. Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks
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27. Integrative molecular analysis of metastatic hepatocellular carcinoma
    Dongfang Wang, Yanjing Zhu, Jing Ping Tang, Qiuyu Lian, Guijuan Luo, Wen Wen, Michael Q. Zhang, Hongyang Wang, Lei Chen, Jin Gu · 2019 · BMC Medical Genomics
28. Computational approaches leveraging integrated connections of multi-omic data toward clinical applications
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29. Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer
    Hyun-Hwan Jeong, Sangseob Leem, Kyubum Wee, Kyung-Ah Sohn · 2015 · Journal of Ovarian Research
30. Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model
    Dong-Yeon Cho, Teresa M. Przytycka · 2013 · Nucleic Acids Research
31. Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content
    Teresia Kling, Patrik Johansson, José Sánchez, Voichita Dana Marinescu, Rebecka J. Jörnsten, Sven Nelander · 2015 · Nucleic Acids Research
32. A new molecular signature method for prediction of driver cancer pathways from transcriptional data
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33. Targeting protein-trafficking pathways alters melanoma treatment sensitivity
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34. Computational medicine: quantitative modeling of complex diseases
    Basant Kumar Tiwary · 2019 · Briefings in Bioinformatics
35. Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients
    Choong Jae Lee, Bin Baek, Sang‐Hee Cho, Tae‐Young Jang, So‐El Jeon, Sunjae Lee, Hyunju Lee, Jeong‐Seok Nam · 2022 · Cancer Medicine
36. The shaping and functional consequences of the dosage effect landscape in multiple myeloma
    Mehmet Samur, Parantu K. Shah, Xujun Wang, Stéphane Minvielle, Florence Magrangeas, Hervé Avet‐Loiseau, Nikhil C. Munshi, Li Cheng · 2013 · BMC Genomics
37. DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method
    Bayarbaatar Amgalan, Hyunju Lee · 2015 · Bioinformatics
38. Computational cancer biology: education is a natural key to many locks
    Frank Emmert‐Streib, Shudong Zhang, Peter Hamilton · 2015 · BMC Cancer
39. Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer
    Safiye Çelik, Benjamin A. Logsdon, Stephanie L. Battle, Charles W. Drescher, Mara Hester Rendi, R. David Hawkins, Su‐In Lee · 2016 · Genome Medicine
40. Identification of Novel Breast Cancer Subtype-Specific Biomarkers by Integrating Genomics Analysis of DNA Copy Number Aberrations and miRNA-mRNA Dual Expression Profiling
    Dongguo Li, Hong Xia, Zhen-ya Li, Lin Hua, Lin Li · 2015 · BioMed Research International
41. Genetics and Epigenetics of the Skin Meet Deep Sequence
    Jeffrey B. Cheng, Raymond Jaihyun Cho · 2012 · Journal of Investigative Dermatology
42. Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo
    Yongsoo Kim, Tycho Bismeijer, Wilbert T. Zwart, Lodewyk F.A. Wessels, Daniël J. Vis · 2019 · Nature Communications
43. Genomic approaches in breast cancer research
    Henry J. Donahue, Damian C. Genetos · 2013 · Briefings in Functional Genomics
44. Prognostic Significance of Rab27A and Rab27B Expression in Esophageal Squamous Cell Cancer
    Fengqiang Yu, Weihan Wu, Mingqiang Liang, Yu Huang, Chun Chen · 2020 · Cancer Management and Research
45. High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI)
    Cong Liu, Jianping Jiang, Jianlei Gu, Zhangsheng Yu, Tao Wang, Hui Juan Lu · 2016 · BMC Systems Biology
46. BHMPS Inhibits Breast Cancer Migration and Invasion by Disrupting Rab27a-Mediated EGFR and Fibronectin Secretion
    Jeong‐In Park, Kyung‐Hee Song, Seong-Mook Kang, Jeeyong Lee, Seong-Jun Cho, Hyun Kyung Choi, Jiyeon Ahn, Jongkuk Park, Jae‐Sung Kim, Sang‐Gu Hwang et al. · 2022 · Cancers
47. Exploring the role of sporadic BRAF and KRAS mutations during colorectal cancer pathogenesis: A spotlight on the contribution of the endosome-lysosome system
    Jingying Tang, Giang T. Lam, Robert D. Brooks, Mark A. Miles, Zivile Useckaite, Ian R. Johnson, Benjamin S.‐Y. Ung, Carmela Martini, Litsa Karageorgos, Shane M. Hickey et al. · 2024 · Cancer Letters
48. Network-Based Interpretation of Genomic Variation Data
    Bjarni V. Halldórsson, Roded Sharan · 2013 · Journal of Molecular Biology
49. Leveraging a Multi-Omics Strategy for Prioritizing Personalized Candidate Mutation-Driver Genes: A Proof-of-Concept Study
    Keyue Ding, Songfeng Wu, Wantao Ying, Qi Pan, Xiaoyuan Li, Dachun Zhao, Xianyu Li, Qing Zhao, Yunping Zhu, Hong Ren et al. · 2015 · Scientific Reports
50. Dissecting the Functional Mechanisms of Somatic Copy-Number Alterations Based on Dysregulated ceRNA Networks across Cancers
    Yanyan Ping, Yao Zhou, Jing Rong Hu, Lin Pang, Chaohan Xu, Yun Xiao · 2020 · Molecular Therapy — Nucleic Acids
51. An Integrative Approach Uncovers Biomarkers that Associate with Clinically Relevant Disease Outcomes in Vulvar Carcinoma
    André Mourão Lavorato-Rocha, Erica Mie Akagi, Beatriz de Melo Maia, Iara S. Rodrigues, Mayara Caroline Silva Botelho, Fábio Albuquerque Marchi, Gabriel da Rocha Fernandes, Glauco Baiocchi, Fernando Augusto Soares, Sílvia Regina Rogatto et al. · 2016 · Molecular Cancer Research
52. Comparison of single and module-based methods for modeling gene regulatory networks
    Mikel Hernáez, Charles A. Blatti, Olivier Gevaert · 2019 · Bioinformatics
53. RAB27A/Melanophilin Blocker Inhibits Melanoma Cell Motility and Invasion
    Dajiang Guo, Rohit Jain, Jae Sung Hwang, Wolfgang Weninger, Kimberley A. Beaumont, Shweta Tikoo · 2020 · Journal of Investigative Dermatology
54. Big Data Analytics and Machine Learning Technologies for HPC Applications
    Sukeshini, Priyanka Sharma, Mohit Ved, Janaki Chintalapti, Supriya N. Pal · 2020 · Lecture notes in electrical engineering
55. Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data
    Sheida Nabavi · 2016 · BMC Genomics
56. A dynamic programing approach to integrate gene expression data and network information for pathway model generation
    Yuexu Jiang, Yanchun Liang, Duolin Wang, Dong He Xu, Trupti Joshi · 2019 · Bioinformatics
57. Next Generation Sequencing in Cancer Research and Clinical Application
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58. Dysfunctions Associated with Methylation, MicroRNA Expression and Gene Expression in Lung Cancer
    Tao Huang, Min Jiang, Xiangyin Kong, Yu‐Dong Cai · 2012 · PLoS ONE
59. PINCAGE: probabilistic integration of cancer genomics data for perturbed gene identification and sample classification
    Michał Świtnicki, Malene Juul, Tobias Lynge Madsen, Karina Dalsgaard Sørensen, Jakob Skou Pedersen · 2016 · Bioinformatics
60. Ras superfamily GTPase activating proteins in cancer: Potential therapeutic targets?
    Huan Xiao, Guan Wang, Min Zhao, Shuai Wen, Liang Ouyang, Qiu Sun · 2023 · European Journal of Medicinal Chemistry
61. Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem
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62. Identification of Novel Pathways in Plant Lectin-Induced Cancer Cell Apoptosis
    Zheng Shi, Rong Sun, Yu Tian, Rong Liu, Lijia Cheng, Jinku Bao, Liang Zou, Yong Tang · 2016 · International Journal of Molecular Sciences
63. Combined analysis of genome-wide expression and copy number profiles to identify key altered genomic regions in cancer
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64. MelanomaDB: A Web Tool for Integrative Analysis of Melanoma Genomic Information to Identify Disease-Associated Molecular Pathways
    Alexander J. Trevarton, Michael B. Mann, Christoph Knapp, Hiromitsu Araki, Jonathan D. Wren, Steven Stones-Havas, Michael A. Black, Cristin Gregor Print · 2013 · Frontiers in Oncology
65. Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer
    Charles A. Blatti, Jesús de la Fuente, Huanyao Gao, Irene Marín-Goñi, Zikun Chen, Sihai Dave Zhao, Winston W. Tan, Richard M. Weinshilboum, Krishna R. Kalari, Liewei Wang et al. · 2023 · Cancer Research
66. Trends in computational biology—2010
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67. Inference of modules associated to eQTLs
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68. An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes
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69. The Genomic Landscape of Cancers
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70. Rab1a and Rab5a preferentially bind to binary lipid compositions with higher stored curvature elastic energy
    Marie L. Kirsten, Rudi Baron, Miguel C. Seabra, Oscar Ces · 2013 · Molecular Membrane Biology
71. Computational methods to dissect gene regulatory networks in cancer
    Archana S. Iyer, Hatice U. Osmanbeyoglu, Christina S. Leslie · 2017 · Current Opinion in Systems Biology
72. A Novel Method for Identifying the Potential Cancer Driver Genes Based on Molecular Data Integration
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73. Rab27a and melanosomes: a model to investigate the membrane targeting of Rabs
    Antonia E. G. Booth, Miguel C. Seabra, Alistair N. Hume · 2012 · Biochemical Society Transactions
74. Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis
    Gaojianyong Wang, Dimitris Anastassiou · 2020 · Scientific Reports
75. Protein deep profile and model predictions for identifying the causal genes of male infertility based on deep learning
    Fang Xu, Ganggang Guo, Feida Zhu, Xiaojun Tan, Liqing Fan · 2021 · Information Fusion
76. Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer
    Runmin Wei, Immaculata De Vivo, Sijia Huang, Xun Zhu, Harvey A. Risch, Jason H. Moore, Herbert Yu, Lana Xia Garmire · 2016 · Oncotarget
77. IMPRes-Pro: A high dimensional multiomics integration method for in silico hypothesis generation
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78. Estimation of Gene Regulatory Networks
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79. A comparative survey of functional footprints of EGFR pathway mutations in human cancers
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80. Chromatin Structure Profiling Identifies Crucial Regulators of Tumor Maintenance
    Constanze Bonifer, Peter N. Cockerill · 2015 · Trends in cancer
81. Versatile Role of Rab27a in Glioma: Effects on Release of Extracellular Vesicles, Cell Viability, and Tumor Progression
    Thomas S. van Solinge, Erik R. Abels, Lieke L. van de Haar, Killian S. Hanlon, Sybren L. N. Maas, Rosalie Schnoor, Jeroen de Vrij, Xandra O. Breakefield, Marike L. D. Broekman · 2020 · Frontiers in Molecular Biosciences
82. The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?
    Vijay Rajagopal, Senthil Arumugam, Peter John Hunter, Afshin Khadangi, Joshua Chung, Michael Pan · 2022 · Annual Review of Biomedical Data Science
83. IndividualizedPath: identifying genetic alterations contributing to the dysfunctional pathways in glioblastoma individuals
    Yanyan Ping, Hongyi Zhang, Yulan Deng, Li Wang, Hongying Zhao, Lin Pang, Huihui Fan, Chaohan Xu, Feng Li, Yong Zhang et al. · 2014 · Molecular BioSystems
84. Integrative Network-based Association Studies: Leveraging cell regulatory models in the post-GWAS era
    Andrea Califano, Atul Janardhan Butte, Stephen Henry Friend, Trey Ideker, Eric E. Schadt · 2011 · Nature Precedings
85. Statistical completion of a partially identified graph with applications for the estimation of gene regulatory networks
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86. Abrogation of RAB27A expression transiently affects melanoma cell proliferation
    Dajiang Guo, Kimberley A. Beaumont, Danae M. Sharp, Goldie Y.L. Lui, Wolfgang Weninger, Nikolas K. Haass, Shweta Tikoo · 2020 · Pigment Cell & Melanoma Research
87. Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma
    Yao Zhou, Shuai Wang, Haoteng Yan, Bo Pang, Xinxin Zhang, Lin Pang, Yihan Wang, Jinyuan Xu, Jing Rong Hu, Yujia Lan et al. · 2021 · Frontiers in Genetics
88. SHARING INFORMATION TO RECONSTRUCT PATIENT-SPECIFIC PATHWAYS IN HETEROGENEOUS DISEASES
    Anthony Gitter, Alfredo Braunstein, Andrea Pagnani, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Riccardo Zecchina, Ernest Fraenkel · 2013 · Pacific Symposium on Biocomputing (PSB)
89. Identification of potential COPD genes based on multi-omics data at the functional level
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90. Upregulated, 7q21–22 amplicon candidate gene SHFM1 confers oncogenic advantage by suppressing p53 function in gastric cancer
    Sembulingam Tamilzhalagan, Muthulakshmi Muthuswami, Jayaprakash Periasamy, Ming Hui Lee, Sun Young Rha, Patrick B. O. Tan, Kumaresan Ganesan · 2015 · Cellular Signalling
91. Comparative Transcriptomes and EVO-DEVO Studies Depending on Next Generation Sequencing
    Tiancheng Liu, Lin Yu, Lei Liu, Hong Li, Yixue Li · 2015 · Computational and Mathematical Methods in Medicine
92. Unraveling the regulatory connections between two controllers of breast cancer cell fate
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93. The transformative potential of an integrative approach to pregnancy
    Haley R. Eidem, Kriston L. McGary, John A. Capra, Patrick Abbot, Antonis Rokas · 2017 · Placenta
94. Effects of miR‑340 overexpression and knockdown on the proliferation and metastasis of NSCLC cell lines
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95. ICan: An Integrated Co-Alteration Network to Identify Ovarian Cancer-Related Genes
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96. Protective effects of inhalation of essential oils from Mentha piperita leaf on tight junctions and inflammation in allergic rhinitis
    Nayoung Park, Jae Yoon Chung, Mi Hye Kim, Woong Mo Yang · 2022 · Frontiers in Allergy
97. Ovarian cancer data analysis using deep learning: A systematic review
    Muta Tah Hira, Mohammad A. Razzaque, Mosharraf H. Sarker · 2024 · Engineering Applications of Artificial Intelligence
98. Voting-Based Cancer Module Identification by Combining Topological and Data-Driven Properties
    AKM Azad, Hyunju Lee · 2013 · PLoS ONE
99. Integrated genomics approach to identify biologically relevant alterations in fewer samples
    Pratik Chandrani, Pawan Upadhyay, Prajish Sundaram Iyer, Mayur Tanna, Madhur Shetty, Gorantala Venkata Raghuram, Ninad Oak, Ankita Singh, Rohan Chaubal, Manoj P. Ramteke et al. · 2015 · BMC Genomics
100. Probabilistic Methods in Cancer Biology
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