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For: Ding L, Wendl MC, McMichael JF, Raphael BJ. Expanding the computational toolbox for mining cancer genomes. Nat Rev Genet. 2014;15:556-570. [PMID: 25001846 DOI: 10.1038/nrg3767] [Cited by in Crossref: 148] [Cited by in F6Publishing: 146] [Article Influence: 16.4] [Reference Citation Analysis]
Number Citing Articles
1 Xi J, Deng Z, Liu Y, Wang Q, Shi W. Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery. PeerJ 2023;11:e14843. [PMID: 36755866 DOI: 10.7717/peerj.14843] [Reference Citation Analysis]
2 Wani S, Humaira, Farooq I, Ali S, Rehman MU, Arafah A. Proteomic profiling and its applications in cancer research. Proteomics 2023. [DOI: 10.1016/b978-0-323-95072-5.00015-8] [Reference Citation Analysis]
3 Hajam YA, Ganie SY, Diksha, Reshi MS, Rai S, Kumar R. Cancer proteomics: An overview. Proteomics 2023. [DOI: 10.1016/b978-0-323-95072-5.00009-2] [Reference Citation Analysis]
4 Wang D, Cao W, Yang W, Jin W, Luo H, Niu X, Gong J. Pancan-MNVQTLdb: systematic identification of multi-nucleotide variant quantitative trait loci in 33 cancer types. NAR Cancer 2022;4:zcac043. [PMID: 36568962 DOI: 10.1093/narcan/zcac043] [Reference Citation Analysis]
5 Morazán-fernández D, Mora J, Molina-mora JA. In Silico Pipeline to Identify Tumor-Specific Antigens for Cancer Immunotherapy Using Exome Sequencing Data. Phenomics 2022. [DOI: 10.1007/s43657-022-00084-9] [Reference Citation Analysis]
6 Wang W, Yuan T, Ma L, Zhu Y, Bao J, Zhao X, Zhao Y, Zong Y, Zhang Y, Yang S, Qiu X, Shen S, Wu R, Wu T, Wang H, Gao D, Wang P, Chen L. Hepatobiliary Tumor Organoids Reveal HLA Class I Neoantigen Landscape and Antitumoral Activity of Neoantigen Peptide Enhanced with Immune Checkpoint Inhibitors. Adv Sci (Weinh) 2022;:e2105810. [PMID: 35665491 DOI: 10.1002/advs.202105810] [Reference Citation Analysis]
7 Tagore S, Frenkel-morgenstern M. Mutated Tumor Suppressors Follow Oncogenes Profile by the Gene Hypermethylation of Partners in the Protein Interaction Networks.. [DOI: 10.1101/2022.03.28.486156] [Reference Citation Analysis]
8 Hussen BM, Abdullah ST, Salihi A, Sabir DK, Sidiq KR, Rasul MF, Hidayat HJ, Ghafouri-Fard S, Taheri M, Jamali E. The emerging roles of NGS in clinical oncology and personalized medicine. Pathol Res Pract 2022;230:153760. [PMID: 35033746 DOI: 10.1016/j.prp.2022.153760] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
9 Liu H, Yin H, Li G, Li J, Wang X. Aperture: alignment-free detection of structural variations and viral integrations in circulating tumor DNA. Brief Bioinform 2021:bbab290. [PMID: 34368852 DOI: 10.1093/bib/bbab290] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
10 Liu H, Yin H, Li G, Li J, Wang X. Aperture: Accurate detection of structural variations and viral integrations in circulating tumor DNA using an alignment-free algorithm.. [DOI: 10.1101/2020.12.04.409508] [Reference Citation Analysis]
11 Vellichirammal NN, Chaturvedi NK, Joshi SS, Coulter DW, Guda C. Fusion genes as biomarkers in pediatric cancers: A review of the current state and applicability in diagnostics and personalized therapy. Cancer Lett 2021;499:24-38. [PMID: 33248210 DOI: 10.1016/j.canlet.2020.11.015] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
12 Leko V, Rosenberg SA. Identifying and Targeting Human Tumor Antigens for T Cell-Based Immunotherapy of Solid Tumors. Cancer Cell 2020;38:454-72. [PMID: 32822573 DOI: 10.1016/j.ccell.2020.07.013] [Cited by in Crossref: 102] [Cited by in F6Publishing: 53] [Article Influence: 34.0] [Reference Citation Analysis]
13 Alizadeh Savareh B, Asadzadeh Aghdaie H, Behmanesh A, Bashiri A, Sadeghi A, Zali M, Shams R. A machine learning approach identified a diagnostic model for pancreatic cancer through using circulating microRNA signatures. Pancreatology 2020;20:1195-204. [PMID: 32800647 DOI: 10.1016/j.pan.2020.07.399] [Cited by in Crossref: 21] [Cited by in F6Publishing: 20] [Article Influence: 7.0] [Reference Citation Analysis]
14 Han XJ, Ma XL, Yang L, Wei YQ, Peng Y, Wei XW. Progress in Neoantigen Targeted Cancer Immunotherapies. Front Cell Dev Biol 2020;8:728. [PMID: 32850843 DOI: 10.3389/fcell.2020.00728] [Cited by in Crossref: 22] [Cited by in F6Publishing: 24] [Article Influence: 7.3] [Reference Citation Analysis]
15 Huang J, Li Z, Fu L, Lin D, Wang C, Wang X, Zhang L. RETRACTED ARTICLE: Comprehensive characterization of tumor mutation burden in clear cell renal cell carcinoma based on the three independent cohorts. J Cancer Res Clin Oncol 2021;147:1745. [PMID: 32617702 DOI: 10.1007/s00432-020-03299-3] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
16 Srinivasan S, Kalinava N, Aldana R, Li Z, van Hagen S, Rodenburg SY, Wind-rotolo M, Sasson AS, Tang H, Qian X, Kirov S. Mis-annotated multi nucleotide variants in public cancer genomics datasets can lead to inaccurate mutation calls with significant implications.. [DOI: 10.1101/2020.06.05.136549] [Reference Citation Analysis]
17 Singer J, Irmisch A, Ruscheweyh HJ, Singer F, Toussaint NC, Levesque MP, Stekhoven DJ, Beerenwinkel N. Bioinformatics for precision oncology. Brief Bioinform 2019;20:778-88. [PMID: 29272324 DOI: 10.1093/bib/bbx143] [Cited by in Crossref: 32] [Cited by in F6Publishing: 35] [Article Influence: 10.7] [Reference Citation Analysis]
18 Liu P, Tan F, Liu H, Li B, Lei T, Zhao X. The Use of Molecular Subtypes for Precision Therapy of Recurrent and Metastatic Gastrointestinal Stromal Tumor. Onco Targets Ther 2020;13:2433-47. [PMID: 32273716 DOI: 10.2147/OTT.S241331] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
19 Bokhari Y, Alhareeri A, Arodz T. QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency. BMC Bioinformatics 2020;21:122. [PMID: 32293263 DOI: 10.1186/s12859-020-3449-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
20 Zhang G, Tang X, Liang L, Zhang W, Li D, Li X, Zhao D, Zheng Y, Chen Y, Hao B, Wang K, Tang N, Ding K. DNA and RNA sequencing identified a novel oncogene VPS35 in liver hepatocellular carcinoma. Oncogene 2020;39:3229-44. [PMID: 32071398 DOI: 10.1038/s41388-020-1215-6] [Cited by in Crossref: 20] [Cited by in F6Publishing: 20] [Article Influence: 6.7] [Reference Citation Analysis]
21 Liu Y, Guo J, Huang L. Modulation of tumor microenvironment for immunotherapy: focus on nanomaterial-based strategies. Theranostics 2020;10:3099-117. [PMID: 32194857 DOI: 10.7150/thno.42998] [Cited by in Crossref: 39] [Cited by in F6Publishing: 42] [Article Influence: 13.0] [Reference Citation Analysis]
22 Lancaster EM, Jablons D, Kratz JR. Applications of Next-Generation Sequencing in Neoantigen Prediction and Cancer Vaccine Development. Genetic Testing and Molecular Biomarkers 2020;24:59-66. [DOI: 10.1089/gtmb.2018.0211] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 5.7] [Reference Citation Analysis]
23 Liu CC, Steen CB, Newman AM. Computational approaches for characterizing the tumor immune microenvironment. Immunology 2019;158:70-84. [PMID: 31347163 DOI: 10.1111/imm.13101] [Cited by in Crossref: 17] [Cited by in F6Publishing: 21] [Article Influence: 5.7] [Reference Citation Analysis]
24 Morganti S, Tarantino P, Ferraro E, D’amico P, Viale G, Trapani D, Duso BA, Curigliano G. Role of Next-Generation Sequencing Technologies in Personalized Medicine. P5 eHealth: An Agenda for the Health Technologies of the Future 2020. [DOI: 10.1007/978-3-030-27994-3_8] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
25 Schürch CM, Rasche L, Frauenfeld L, Weinhold N, Fend F. A review on tumor heterogeneity and evolution in multiple myeloma: pathological, radiological, molecular genetics, and clinical integration. Virchows Arch 2020;476:337-51. [PMID: 31848687 DOI: 10.1007/s00428-019-02725-3] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 4.8] [Reference Citation Analysis]
26 Benvenuto M, Focaccetti C, Izzi V, Masuelli L, Modesti A, Bei R. Tumor antigens heterogeneity and immune response-targeting neoantigens in breast cancer. Semin Cancer Biol 2021;72:65-75. [PMID: 31698088 DOI: 10.1016/j.semcancer.2019.10.023] [Cited by in Crossref: 13] [Cited by in F6Publishing: 19] [Article Influence: 3.3] [Reference Citation Analysis]
27 Khan W, Varma Saripella G, Ludwig T, Cuppens T, Thibord F, Génin E, Deleuze JF, Trégouët DA. MACARON: a python framework to identify and re-annotate multi-base affected codons in whole genome/exome sequence data. Bioinformatics 2018;34:3396-8. [PMID: 29726922 DOI: 10.1093/bioinformatics/bty382] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
28 Lindberg M, Boström M, Elliott K, Larsson E. Intragenomic variability and extended sequence patterns in the mutational signature of ultraviolet light. Proc Natl Acad Sci U S A 2019;116:20411-7. [PMID: 31548379 DOI: 10.1073/pnas.1909021116] [Cited by in Crossref: 12] [Cited by in F6Publishing: 15] [Article Influence: 3.0] [Reference Citation Analysis]
29 Finotello F, Rieder D, Hackl H, Trajanoski Z. Next-generation computational tools for interrogating cancer immunity. Nat Rev Genet 2019;20:724-46. [PMID: 31515541 DOI: 10.1038/s41576-019-0166-7] [Cited by in Crossref: 85] [Cited by in F6Publishing: 88] [Article Influence: 21.3] [Reference Citation Analysis]
30 Kamdar MR, Fernández JD, Polleres A, Tudorache T, Musen MA. Enabling Web-scale data integration in biomedicine through Linked Open Data. NPJ Digit Med 2019;2:90. [PMID: 31531395 DOI: 10.1038/s41746-019-0162-5] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 3.0] [Reference Citation Analysis]
31 Kumar S, Clarke D, Gerstein MB. Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures. Proc Natl Acad Sci U S A 2019;116:18962-70. [PMID: 31462496 DOI: 10.1073/pnas.1901156116] [Cited by in Crossref: 17] [Cited by in F6Publishing: 19] [Article Influence: 4.3] [Reference Citation Analysis]
32 Hu X, Wang Q, Tang M, Barthel F, Amin S, Yoshihara K, Lang FM, Martinez-Ledesma E, Lee SH, Zheng S, Verhaak RGW. TumorFusions: an integrative resource for cancer-associated transcript fusions. Nucleic Acids Res 2018;46:D1144-9. [PMID: 29099951 DOI: 10.1093/nar/gkx1018] [Cited by in Crossref: 126] [Cited by in F6Publishing: 133] [Article Influence: 31.5] [Reference Citation Analysis]
33 Garcia-Garijo A, Fajardo CA, Gros A. Determinants for Neoantigen Identification. Front Immunol 2019;10:1392. [PMID: 31293573 DOI: 10.3389/fimmu.2019.01392] [Cited by in Crossref: 68] [Cited by in F6Publishing: 70] [Article Influence: 17.0] [Reference Citation Analysis]
34 Schulze S, Stengel R, Jaekel N, Wang SY, Franke GN, Roskos M, Schneider M, Niederwieser D, Al-Ali HK. Concomitant and noncanonical JAK2 and MPL mutations in JAK2V617F- and MPLW515 L-positive myelofibrosis. Genes Chromosomes Cancer 2019;58:747-55. [PMID: 31135094 DOI: 10.1002/gcc.22781] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
35 Agajanian S, Oluyemi O, Verkhivker GM. Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations. Front Mol Biosci 2019;6:44. [PMID: 31245384 DOI: 10.3389/fmolb.2019.00044] [Cited by in Crossref: 28] [Cited by in F6Publishing: 28] [Article Influence: 7.0] [Reference Citation Analysis]
36 Lindberg M, Boström M, Elliott K, Larsson E. Intragenomic variability and extended sequence patterns in the mutational signature of ultraviolet light.. [DOI: 10.1101/640722] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
37 Ramón Y Cajal S, Hümmer S, Peg V, Guiu XM, De Torres I, Castellvi J, Martinez-Saez E, Hernandez-Losa J. Integrating clinical, molecular, proteomic and histopathological data within the tissue context: tissunomics. Histopathology 2019;75:4-19. [PMID: 30667539 DOI: 10.1111/his.13828] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 1.8] [Reference Citation Analysis]
38 Nussinov R, Jang H, Tsai CJ, Cheng F. Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers. PLoS Comput Biol 2019;15:e1006658. [PMID: 30921324 DOI: 10.1371/journal.pcbi.1006658] [Cited by in Crossref: 52] [Cited by in F6Publishing: 58] [Article Influence: 13.0] [Reference Citation Analysis]
39 Qu Z, Lau CW, Nguyen QV, Zhou Y, Catchpoole DR. Visual Analytics of Genomic and Cancer Data: A Systematic Review. Cancer Inform 2019;18:1176935119835546. [PMID: 30890859 DOI: 10.1177/1176935119835546] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 3.0] [Reference Citation Analysis]
40 Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B, Ng PK, Jeong KJ, Cao S, Wang Z, Gao J, Gao Q, Wang F, Liu EM, Mularoni L, Rubio-Perez C, Nagarajan N, Cortés-Ciriano I, Zhou DC, Liang WW, Hess JM, Yellapantula VD, Tamborero D, Gonzalez-Perez A, Suphavilai C, Ko JY, Khurana E, Park PJ, Van Allen EM, Liang H, Lawrence MS, Godzik A, Lopez-Bigas N, Stuart J, Wheeler D, Getz G, Chen K, Lazar AJ, Mills GB, Karchin R, Ding L; MC3 Working Group., Cancer Genome Atlas Research Network. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 2018;173:371-385.e18. [PMID: 29625053 DOI: 10.1016/j.cell.2018.02.060] [Cited by in Crossref: 1060] [Cited by in F6Publishing: 1105] [Article Influence: 265.0] [Reference Citation Analysis]
41 Long NP, Jung KH, Anh NH, Yan HH, Nghi TD, Park S, Yoon SJ, Min JE, Kim HM, Lim JH, Kim JM, Lim J, Lee S, Hong SS, Kwon SW. An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer. Cancers (Basel) 2019;11:E155. [PMID: 30700038 DOI: 10.3390/cancers11020155] [Cited by in Crossref: 25] [Cited by in F6Publishing: 28] [Article Influence: 6.3] [Reference Citation Analysis]
42 Jan S, Ahmad P. Introducing Metabolomics. Ecometabolomics 2019. [DOI: 10.1016/b978-0-12-814872-3.00001-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
43 Fazal S, Azam S. Robotics in Single-Cell Omics. Single-Cell Omics 2019. [DOI: 10.1016/b978-0-12-814919-5.00017-8] [Reference Citation Analysis]
44 Zeira R, Shamir R. Genome Rearrangement Problems with Single and Multiple Gene Copies: A Review. Bioinformatics and Phylogenetics 2019. [DOI: 10.1007/978-3-030-10837-3_10] [Cited by in Crossref: 5] [Article Influence: 1.3] [Reference Citation Analysis]
45 Morganti S, Tarantino P, Ferraro E, D’amico P, Duso BA, Curigliano G. Next Generation Sequencing (NGS): A Revolutionary Technology in Pharmacogenomics and Personalized Medicine in Cancer. Translational Research and Onco-Omics Applications in the Era of Cancer Personal Genomics 2019. [DOI: 10.1007/978-3-030-24100-1_2] [Cited by in Crossref: 64] [Cited by in F6Publishing: 65] [Article Influence: 16.0] [Reference Citation Analysis]
46 Kumar S, Clarke D, Gerstein MB. Leveraging protein dynamics to identify cancer mutational hotspots in 3D-structures.. [DOI: 10.1101/508788] [Reference Citation Analysis]
47 Sapna G, Gokul S. Next generation sequencing in oral disease diagnostics. World J Stomatol 2018; 6(2): 6-10 [DOI: 10.5321/wjs.v6.i2.6] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
48 Morganti S, Tarantino P, Ferraro E, D'Amico P, Viale G, Trapani D, Duso BA, Curigliano G. Complexity of genome sequencing and reporting: Next generation sequencing (NGS) technologies and implementation of precision medicine in real life. Crit Rev Oncol Hematol 2019;133:171-82. [PMID: 30661654 DOI: 10.1016/j.critrevonc.2018.11.008] [Cited by in Crossref: 58] [Cited by in F6Publishing: 60] [Article Influence: 11.6] [Reference Citation Analysis]
49 Ewing A, Semple C. Breaking point: the genesis and impact of structural variation in tumours. F1000Res 2018;7:F1000 Faculty Rev-1814. [PMID: 30519450 DOI: 10.12688/f1000research.16079.1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 1.2] [Reference Citation Analysis]
50 Yi M, Qin S, Zhao W, Yu S, Chu Q, Wu K. The role of neoantigen in immune checkpoint blockade therapy. Exp Hematol Oncol 2018;7:28. [PMID: 30473928 DOI: 10.1186/s40164-018-0120-y] [Cited by in Crossref: 69] [Cited by in F6Publishing: 74] [Article Influence: 13.8] [Reference Citation Analysis]
51 Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28:1747-1756. [PMID: 30341162 DOI: 10.1101/gr.239244.118] [Cited by in Crossref: 1228] [Cited by in F6Publishing: 1375] [Article Influence: 245.6] [Reference Citation Analysis]
52 Agajanian S, Odeyemi O, Bischoff N, Ratra S, Verkhivker GM. Machine Learning Classification and Structure–Functional Analysis of Cancer Mutations Reveal Unique Dynamic and Network Signatures of Driver Sites in Oncogenes and Tumor Suppressor Genes. J Chem Inf Model 2018;58:2131-50. [DOI: 10.1021/acs.jcim.8b00414] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 3.2] [Reference Citation Analysis]
53 Zhang M, Liu D, Tang J, Feng Y, Wang T, Dobbin KK, Schliekelman P, Zhao S. SEG - A Software Program for Finding Somatic Copy Number Alterations in Whole Genome Sequencing Data of Cancer. Comput Struct Biotechnol J 2018;16:335-41. [PMID: 30258547 DOI: 10.1016/j.csbj.2018.09.001] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 0.8] [Reference Citation Analysis]
54 Barros L, Pretti MA, Chicaybam L, Abdo L, Boroni M, Bonamino MH. Immunological-based approaches for cancer therapy. Clinics (Sao Paulo) 2018;73:e429s. [PMID: 30133560 DOI: 10.6061/clinics/2018/e429s] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.4] [Reference Citation Analysis]
55 Hammerbacher J, Snyder A. Informatics for cancer immunotherapy. Ann Oncol 2017;28:xii56-73. [PMID: 29253114 DOI: 10.1093/annonc/mdx682] [Cited by in Crossref: 12] [Cited by in F6Publishing: 15] [Article Influence: 2.4] [Reference Citation Analysis]
56 Xi J, Wang M, Li A. Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network. BMC Bioinformatics 2018;19:214. [PMID: 29871594 DOI: 10.1186/s12859-018-2218-y] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 3.4] [Reference Citation Analysis]
57 Zeira R, Shamir R. Sorting cancer karyotypes using double-cut-and-joins, duplications and deletions. Bioinformatics 2018. [PMID: 29726899 DOI: 10.1093/bioinformatics/bty381] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 0.8] [Reference Citation Analysis]
58 Behr M, Holmes C, Munk A. Multiscale blind source separation. Ann Statist 2018;46. [DOI: 10.1214/17-aos1565] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 1.8] [Reference Citation Analysis]
59 Pannuti A, Filipovic A, Hicks C, Lefkowitz E, Ptacek T, Stebbing J, Miele L. Novel putative drivers revealed by targeted exome sequencing of advanced solid tumors. PLoS One 2018;13:e0194790. [PMID: 29570743 DOI: 10.1371/journal.pone.0194790] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.4] [Reference Citation Analysis]
60 De S, Ganesan S. Looking beyond drivers and passengers in cancer genome sequencing data. Ann Oncol 2017;28:938-45. [PMID: 27998972 DOI: 10.1093/annonc/mdw677] [Cited by in Crossref: 21] [Cited by in F6Publishing: 21] [Article Influence: 4.2] [Reference Citation Analysis]
61 Karczewski KJ, Snyder MP. Integrative omics for health and disease. Nat Rev Genet 2018;19:299-310. [PMID: 29479082 DOI: 10.1038/nrg.2018.4] [Cited by in Crossref: 440] [Cited by in F6Publishing: 457] [Article Influence: 88.0] [Reference Citation Analysis]
62 Kotelnikova EA, Pyatnitskiy M, Paleeva A, Kremenetskaya O, Vinogradov D. Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine. Oncotarget 2016;7:52493-516. [PMID: 27191992 DOI: 10.18632/oncotarget.9370] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 2.6] [Reference Citation Analysis]
63 Kanterakis A, Potamias G, Swertz MA, Patrinos GP. Creating Transparent and Reproducible Pipelines: Best Practices for Tools, Data, and Workflow Management Systems. Human Genome Informatics 2018. [DOI: 10.1016/b978-0-12-809414-3.00002-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
64 Efremova M, Finotello F, Rieder D, Trajanoski Z. Neoantigens Generated by Individual Mutations and Their Role in Cancer Immunity and Immunotherapy. Front Immunol. 2017;8:1679. [PMID: 29234329 DOI: 10.3389/fimmu.2017.01679] [Cited by in Crossref: 118] [Cited by in F6Publishing: 124] [Article Influence: 19.7] [Reference Citation Analysis]
65 Lauschke VM, Milani L, Ingelman-sundberg M. Pharmacogenomic Biomarkers for Improved Drug Therapy—Recent Progress and Future Developments. AAPS J 2018;20. [DOI: 10.1208/s12248-017-0161-x] [Cited by in Crossref: 84] [Cited by in F6Publishing: 88] [Article Influence: 14.0] [Reference Citation Analysis]
66 Bokhari Y, Arodz T. QuaDMutEx: quadratic driver mutation explorer. BMC Bioinformatics 2017;18:458. [PMID: 29065872 DOI: 10.1186/s12859-017-1869-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
67 Li Y, Heavican TB, Vellichirammal NN, Iqbal J, Guda C. ChimeRScope: a novel alignment-free algorithm for fusion transcript prediction using paired-end RNA-Seq data. Nucleic Acids Res 2017;45:e120. [PMID: 28472320 DOI: 10.1093/nar/gkx315] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 3.2] [Reference Citation Analysis]
68 Ramón Y Cajal S, Capdevila C, Hernandez-Losa J, De Mattos-Arruda L, Ghosh A, Lorent J, Larsson O, Aasen T, Postovit LM, Topisirovic I. Cancer as an ecomolecular disease and a neoplastic consortium. Biochim Biophys Acta Rev Cancer 2017;1868:484-99. [PMID: 28947238 DOI: 10.1016/j.bbcan.2017.09.004] [Cited by in Crossref: 5] [Cited by in F6Publishing: 10] [Article Influence: 0.8] [Reference Citation Analysis]
69 Przytycki PF, Singh M. Differential analysis between somatic mutation and germline variation profiles reveals cancer-related genes. Genome Med 2017;9:79. [PMID: 28841835 DOI: 10.1186/s13073-017-0465-6] [Cited by in Crossref: 20] [Cited by in F6Publishing: 24] [Article Influence: 3.3] [Reference Citation Analysis]
70 Ghanat Bari M, Ung CY, Zhang C, Zhu S, Li H. Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks. Sci Rep 2017;7:6993. [PMID: 28765560 DOI: 10.1038/s41598-017-07481-5] [Cited by in Crossref: 24] [Cited by in F6Publishing: 28] [Article Influence: 4.0] [Reference Citation Analysis]
71 Porta-Pardo E, Kamburov A, Tamborero D, Pons T, Grases D, Valencia A, Lopez-Bigas N, Getz G, Godzik A. Comparison of algorithms for the detection of cancer drivers at subgene resolution. Nat Methods 2017;14:782-8. [PMID: 28714987 DOI: 10.1038/nmeth.4364] [Cited by in Crossref: 60] [Cited by in F6Publishing: 63] [Article Influence: 10.0] [Reference Citation Analysis]
72 Crispatzu G, Kulkarni P, Toliat MR, Nürnberg P, Herling M, Herling CD, Frommolt P. Semi-automated cancer genome analysis using high-performance computing. Hum Mutat 2017;38:1325-35. [PMID: 28598576 DOI: 10.1002/humu.23275] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.2] [Reference Citation Analysis]
73 Zeng H, Zhang L, Xiao M. A Review. Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science 2017. [DOI: 10.1145/3121138.3121174] [Reference Citation Analysis]
74 Misyura M, Zhang T, Sukhai MA, Thomas M, Garg S, Kamel-Reid S, Stockley TL. Comparison of Next-Generation Sequencing Panels and Platforms for Detection and Verification of Somatic Tumor Variants for Clinical Diagnostics. J Mol Diagn 2016;18:842-50. [PMID: 27770852 DOI: 10.1016/j.jmoldx.2016.06.004] [Cited by in Crossref: 31] [Cited by in F6Publishing: 32] [Article Influence: 5.2] [Reference Citation Analysis]
75 Xi J, Li A, Wang M. A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity. Sci Rep 2017;7:2855. [PMID: 28588243 DOI: 10.1038/s41598-017-03141-w] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 1.2] [Reference Citation Analysis]
76 Wong VC, Ko JM, Lam CT, Lung ML. Succinct workflows for circulating tumor cells after enrichment: From systematic counting to mutational profiling. PLoS One 2017;12:e0177276. [PMID: 28481895 DOI: 10.1371/journal.pone.0177276] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 1.3] [Reference Citation Analysis]
77 Bertrand D, Drissler S, Chia B, Koh JY, Chenhao L, Suphavilai C, Tan IB, Nagarajan N. ConsensusDriver improves upon individual algorithms for predicting driver alterations in different cancer types and individual patients – a toolbox for precision oncology.. [DOI: 10.1101/127985] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
78 Yi S, Lin S, Li Y, Zhao W, Mills GB, Sahni N. Functional variomics and network perturbation: connecting genotype to phenotype in cancer. Nat Rev Genet 2017;18:395-410. [PMID: 28344341 DOI: 10.1038/nrg.2017.8] [Cited by in Crossref: 65] [Cited by in F6Publishing: 70] [Article Influence: 10.8] [Reference Citation Analysis]
79 Treviño V, Martínez-Ledesma E, Tamez-Peña J. Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions. Sci Rep 2017;7:43350. [PMID: 28240231 DOI: 10.1038/srep43350] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 0.8] [Reference Citation Analysis]
80 Pan Y, Yan C, Hu Y, Fan Y, Pan Q, Wan Q, Torcivia-Rodriguez J, Mazumder R. Distribution bias analysis of germline and somatic single-nucleotide variations that impact protein functional site and neighboring amino acids. Sci Rep 2017;7:42169. [PMID: 28176830 DOI: 10.1038/srep42169] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
81 Mizrachi E, Verbeke L, Christie N, Fierro AC, Mansfield SD, Davis MF, Gjersing E, Tuskan GA, Van Montagu M, Van de Peer Y, Marchal K, Myburg AA. Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing. Proc Natl Acad Sci U S A 2017;114:1195-200. [PMID: 28096391 DOI: 10.1073/pnas.1620119114] [Cited by in Crossref: 36] [Cited by in F6Publishing: 38] [Article Influence: 6.0] [Reference Citation Analysis]
82 Waks Z, Weissbrod O, Carmeli B, Norel R, Utro F, Goldschmidt Y. Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins. Sci Rep 2016;6:38988. [PMID: 28008934 DOI: 10.1038/srep38988] [Cited by in Crossref: 15] [Cited by in F6Publishing: 17] [Article Influence: 2.1] [Reference Citation Analysis]
83 Cai L, Yuan W, Zhang Z, He L, Chou KC. In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data. Sci Rep 2016;6:36540. [PMID: 27874022 DOI: 10.1038/srep36540] [Cited by in Crossref: 74] [Cited by in F6Publishing: 79] [Article Influence: 10.6] [Reference Citation Analysis]
84 Beaumeunier S, Audoux J, Boureux A, Ruffle F, Commes T, Philippe N, Alves R. On the evaluation of the fidelity of supervised classifiers in the prediction of chimeric RNAs. BioData Min 2016;9:34. [PMID: 27822312 DOI: 10.1186/s13040-016-0112-6] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 0.7] [Reference Citation Analysis]
85 Chen C, He M, Zhu Y, Shi L, Wang X. Five critical elements to ensure the precision medicine. Cancer Metastasis Rev. 2015;34:313-318. [PMID: 25920354 DOI: 10.1007/s10555-015-9555-3] [Cited by in Crossref: 36] [Cited by in F6Publishing: 32] [Article Influence: 5.1] [Reference Citation Analysis]
86 Rubio-Camarillo M, López-Fernández H, Gómez-López G, Carro Á, Fernández JM, Torre CF, Fdez-Riverola F, Glez-Peña D. RUbioSeq+: A multiplatform application that executes parallelized pipelines to analyse next-generation sequencing data. Comput Methods Programs Biomed 2017;138:73-81. [PMID: 27886717 DOI: 10.1016/j.cmpb.2016.10.008] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 1.3] [Reference Citation Analysis]
87 Bertier G, Carrot-Zhang J, Ragoussis V, Joly Y. Integrating precision cancer medicine into healthcare-policy, practice, and research challenges. Genome Med 2016;8:108. [PMID: 27776531 DOI: 10.1186/s13073-016-0362-4] [Cited by in Crossref: 37] [Cited by in F6Publishing: 39] [Article Influence: 5.3] [Reference Citation Analysis]
88 Song JL, Chen C, Yuan JP, Sun SR. Progress in the clinical detection of heterogeneity in breast cancer. Cancer Med 2016;5:3475-88. [PMID: 27774765 DOI: 10.1002/cam4.943] [Cited by in Crossref: 35] [Cited by in F6Publishing: 40] [Article Influence: 5.0] [Reference Citation Analysis]
89 Kumar S, Clarke D, Gerstein M. Localized structural frustration for evaluating the impact of sequence variants. Nucleic Acids Res 2016;44:10062-73. [PMID: 27915290 DOI: 10.1093/nar/gkw927] [Cited by in Crossref: 4] [Cited by in F6Publishing: 9] [Article Influence: 0.6] [Reference Citation Analysis]
90 Mansfield AS, Murphy SJ, Harris FR, Robinson SI, Marks RS, Johnson SH, Smadbeck JB, Halling GC, Yi ES, Wigle D, Vasmatzis G, Jen J. Chromoplectic TPM3-ALK rearrangement in a patient with inflammatory myofibroblastic tumor who responded to ceritinib after progression on crizotinib. Ann Oncol 2016;27:2111-7. [PMID: 27742657 DOI: 10.1093/annonc/mdw405] [Cited by in Crossref: 47] [Cited by in F6Publishing: 48] [Article Influence: 6.7] [Reference Citation Analysis]
91 Tamura T, Akutsu T, Lin C, Yang J. Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks. 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE) 2016. [DOI: 10.1109/bibe.2016.25] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.1] [Reference Citation Analysis]
92 Treviño V, Martínez-ledesma E, Tamez-peña J. Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions.. [DOI: 10.1101/075408] [Reference Citation Analysis]
93 Koh J, Allbritton NL, Sosa JA. Single-cell approaches for molecular classification of endocrine tumors. Curr Opin Oncol 2016;28:43-9. [PMID: 26632769 DOI: 10.1097/CCO.0000000000000246] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.4] [Reference Citation Analysis]
94 Shologu N, Szegezdi E, Lowery A, Kerin M, Pandit A, Zeugolis DI. Recreating complex pathophysiologies in vitro with extracellular matrix surrogates for anticancer therapeutics screening. Drug Discovery Today 2016;21:1521-31. [DOI: 10.1016/j.drudis.2016.06.001] [Cited by in Crossref: 25] [Cited by in F6Publishing: 26] [Article Influence: 3.6] [Reference Citation Analysis]
95 Guernet A, Mungamuri SK, Cartier D, Sachidanandam R, Jayaprakash A, Adriouch S, Vezain M, Charbonnier F, Rohkin G, Coutant S, Yao S, Ainani H, Alexandre D, Tournier I, Boyer O, Aaronson SA, Anouar Y, Grumolato L. CRISPR-Barcoding for Intratumor Genetic Heterogeneity Modeling and Functional Analysis of Oncogenic Driver Mutations. Mol Cell 2016;63:526-38. [PMID: 27453044 DOI: 10.1016/j.molcel.2016.06.017] [Cited by in Crossref: 43] [Cited by in F6Publishing: 43] [Article Influence: 6.1] [Reference Citation Analysis]
96 Hackl H, Charoentong P, Finotello F, Trajanoski Z. Computational genomics tools for dissecting tumour–immune cell interactions. Nat Rev Genet 2016;17:441-58. [DOI: 10.1038/nrg.2016.67] [Cited by in Crossref: 184] [Cited by in F6Publishing: 188] [Article Influence: 26.3] [Reference Citation Analysis]
97 Cao S, Wendl MC, Wyczalkowski MA, Wylie K, Ye K, Jayasinghe R, Xie M, Wu S, Niu B, Grubb R 3rd, Johnson KJ, Gay H, Chen K, Rader JS, Dipersio JF, Chen F, Ding L. Divergent viral presentation among human tumors and adjacent normal tissues. Sci Rep 2016;6:28294. [PMID: 27339696 DOI: 10.1038/srep28294] [Cited by in Crossref: 49] [Cited by in F6Publishing: 52] [Article Influence: 7.0] [Reference Citation Analysis]
98 Sancho-Martinez I, Izpisua Belmonte JC. Reprogramming strategies for the establishment of novel human cancer models. Cell Cycle 2016;15:2393-7. [PMID: 27314153 DOI: 10.1080/15384101.2016.1196305] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 0.3] [Reference Citation Analysis]
99 Leiserson MD, Vandin F, Wu HT, Raphael BJ. Reply: Co-occurrence of MYC amplification and TP53 mutations in human cancer. Nat Genet 2016;48:106-8. [PMID: 26813760 DOI: 10.1038/ng.3491] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.3] [Reference Citation Analysis]
100 Kumar S, Clarke D, Gerstein M. Localized structural frustration for evaluating the impact of sequence variants.. [DOI: 10.1101/052027] [Reference Citation Analysis]
101 Mészáros B, Zeke A, Reményi A, Simon I, Dosztányi Z. Systematic analysis of somatic mutations driving cancer: uncovering functional protein regions in disease development. Biol Direct 2016;11:23. [PMID: 27150584 DOI: 10.1186/s13062-016-0125-6] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 1.9] [Reference Citation Analysis]
102 Ryu D, Joung J, Kim NKD, Kim K, Park W. Deciphering intratumor heterogeneity using cancer genome analysis. Hum Genet 2016;135:635-42. [DOI: 10.1007/s00439-016-1670-x] [Cited by in Crossref: 20] [Cited by in F6Publishing: 16] [Article Influence: 2.9] [Reference Citation Analysis]
103 Kim J, Cho H, Han SI, Han KH. Single-Cell Isolation of Circulating Tumor Cells from Whole Blood by Lateral Magnetophoretic Microseparation and Microfluidic Dispensing. Anal Chem 2016;88:4857-63. [PMID: 27093098 DOI: 10.1021/acs.analchem.6b00570] [Cited by in Crossref: 55] [Cited by in F6Publishing: 55] [Article Influence: 7.9] [Reference Citation Analysis]
104 Richardson S, Tseng GC, Sun W. Statistical Methods in Integrative Genomics. Annu Rev Stat Appl 2016;3:181-209. [PMID: 27482531 DOI: 10.1146/annurev-statistics-041715-033506] [Cited by in Crossref: 65] [Cited by in F6Publishing: 65] [Article Influence: 9.3] [Reference Citation Analysis]
105 Niroula A, Vihinen M. Variation Interpretation Predictors: Principles, Types, Performance, and Choice. Human Mutation 2016;37:579-97. [DOI: 10.1002/humu.22987] [Cited by in Crossref: 90] [Cited by in F6Publishing: 90] [Article Influence: 12.9] [Reference Citation Analysis]
106 Mazor T, Pankov A, Song JS, Costello JF. Intratumoral Heterogeneity of the Epigenome. Cancer Cell 2016;29:440-51. [PMID: 27070699 DOI: 10.1016/j.ccell.2016.03.009] [Cited by in Crossref: 151] [Cited by in F6Publishing: 150] [Article Influence: 21.6] [Reference Citation Analysis]
107 Heitzer E. Ein vielversprechendes Werkzeug. Wien klin Mag 2016;19:36-43. [DOI: 10.1007/s00740-016-0099-0] [Reference Citation Analysis]
108 Beadling C, Wald AI, Warrick A, Neff TL, Zhong S, Nikiforov YE, Corless CL, Nikiforova MN. A Multiplexed Amplicon Approach for Detecting Gene Fusions by Next-Generation Sequencing. The Journal of Molecular Diagnostics 2016;18:165-75. [DOI: 10.1016/j.jmoldx.2015.10.002] [Cited by in Crossref: 53] [Cited by in F6Publishing: 51] [Article Influence: 7.6] [Reference Citation Analysis]
109 Sancho-Martinez I, Nivet E, Xia Y, Hishida T, Aguirre A, Ocampo A, Ma L, Morey R, Krause MN, Zembrzycki A, Ansorge O, Vazquez-Ferrer E, Dubova I, Reddy P, Lam D, Hishida Y, Wu MZ, Esteban CR, O'Leary D, Wahl GM, Verma IM, Laurent LC, Izpisua Belmonte JC. Establishment of human iPSC-based models for the study and targeting of glioma initiating cells. Nat Commun 2016;7:10743. [PMID: 26899176 DOI: 10.1038/ncomms10743] [Cited by in Crossref: 43] [Cited by in F6Publishing: 46] [Article Influence: 6.1] [Reference Citation Analysis]
110 Kanagal-Shamanna R, Singh RR, Routbort MJ, Patel KP, Medeiros LJ, Luthra R. Principles of analytical validation of next-generation sequencing based mutational analysis for hematologic neoplasms in a CLIA-certified laboratory. Expert Rev Mol Diagn 2016;16:461-72. [PMID: 26765348 DOI: 10.1586/14737159.2016.1142374] [Cited by in Crossref: 24] [Cited by in F6Publishing: 25] [Article Influence: 3.4] [Reference Citation Analysis]
111 Yamagata K, Yamanishi A, Kokubu C, Takeda J, Sese J. COSMOS: accurate detection of somatic structural variations through asymmetric comparison between tumor and normal samples. Nucleic Acids Res 2016;44:e78. [PMID: 26833260 DOI: 10.1093/nar/gkw026] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.4] [Reference Citation Analysis]
112 Ren G, Krawetz R. Applying computation biology and "big data" to develop multiplex diagnostics for complex chronic diseases such as osteoarthritis. Biomarkers 2015;20:533-9. [PMID: 26809774 DOI: 10.3109/1354750X.2015.1105499] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 1.6] [Reference Citation Analysis]
113 De Maeyer D, Weytjens B, De Raedt L, Marchal K. Network-Based Analysis of eQTL Data to Prioritize Driver Mutations. Genome Biol Evol 2016;8:481-94. [PMID: 26802430 DOI: 10.1093/gbe/evw010] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 1.4] [Reference Citation Analysis]
114 Roman T, Xie L, Schwartz R. Medoidshift clustering applied to genomic bulk tumor data. BMC Genomics 2016;17 Suppl 1:6. [PMID: 26817708 DOI: 10.1186/s12864-015-2302-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 0.3] [Reference Citation Analysis]
115 Bomersbach A, Chiarandini M, Vandin F. An Efficient Branch and Cut Algorithm to Find Frequently Mutated Subnetworks in Cancer. Lecture Notes in Computer Science 2016. [DOI: 10.1007/978-3-319-43681-4_3] [Cited by in Crossref: 5] [Article Influence: 0.7] [Reference Citation Analysis]
116 Tong P, Li H. Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms. Big Data Analytics in Genomics 2016. [DOI: 10.1007/978-3-319-41279-5_10] [Reference Citation Analysis]
117 Jameson JL, Kopp P. Applications of Genetics in Endocrinology. Endocrinology: Adult and Pediatric 2016. [DOI: 10.1016/b978-0-323-18907-1.00004-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.1] [Reference Citation Analysis]
118 Prieto T, Alves JM, Posada D. NGS Analysis of Somatic Mutations in Cancer Genomes. Big Data Analytics in Genomics 2016. [DOI: 10.1007/978-3-319-41279-5_11] [Reference Citation Analysis]
119 Roychowdhury S, Chinnaiyan AM. Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J Clin 2016;66:75-88. [PMID: 26528881 DOI: 10.3322/caac.21329] [Cited by in Crossref: 115] [Cited by in F6Publishing: 119] [Article Influence: 16.4] [Reference Citation Analysis]
120 Rubio-camarillo M, López-fernández H, Gómez-lópez G, Carro Á, Fernández JM, Fdez-riverola F, Glez-peña D, Pisano DG. RUbioSeq+: An Application that Executes Parallelized Pipelines to Analyse Next-Generation Sequencing Data. Advances in Intelligent Systems and Computing 2016. [DOI: 10.1007/978-3-319-40126-3_15] [Reference Citation Analysis]
121 Araya CL, Cenik C, Reuter JA, Kiss G, Pande VS, Snyder MP, Greenleaf WJ. Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations. Nat Genet 2016;48:117-25. [PMID: 26691984 DOI: 10.1038/ng.3471] [Cited by in Crossref: 69] [Cited by in F6Publishing: 71] [Article Influence: 8.6] [Reference Citation Analysis]
122 Vicini P, Fields O, Lai E, Litwack ED, Martin AM, Morgan TM, Pacanowski MA, Papaluca M, Perez OD, Ringel MS. Precision medicine in the age of big data: The present and future role of large-scale unbiased sequencing in drug discovery and development. Clin Pharmacol Ther. 2016;99:198-207. [PMID: 26536838 DOI: 10.1002/cpt.293] [Cited by in Crossref: 32] [Cited by in F6Publishing: 34] [Article Influence: 4.0] [Reference Citation Analysis]
123 Myneni S, Patel VL, Bova GS, Wang J, Ackerman CF, Berlinicke CA, Chen SH, Lindvall M, Zack DJ. Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry-academia collaboration. Comput Methods Programs Biomed 2016;126:160-70. [PMID: 26652980 DOI: 10.1016/j.cmpb.2015.11.001] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 0.5] [Reference Citation Analysis]
124 Yang R, Nelson AC, Henzler C, Thyagarajan B, Silverstein KA. ScanIndel: a hybrid framework for indel detection via gapped alignment, split reads and de novo assembly. Genome Med 2015;7:127. [PMID: 26643039 DOI: 10.1186/s13073-015-0251-2] [Cited by in Crossref: 33] [Cited by in F6Publishing: 33] [Article Influence: 4.1] [Reference Citation Analysis]
125 Verigos J, Magklara A. Revealing the Complexity of Breast Cancer by Next Generation Sequencing. Cancers (Basel) 2015;7:2183-200. [PMID: 26561834 DOI: 10.3390/cancers7040885] [Cited by in Crossref: 24] [Cited by in F6Publishing: 25] [Article Influence: 3.0] [Reference Citation Analysis]
126 Liu Y, Hu X, Han C, Wang L, Zhang X, He X, Lu X. Targeting tumor suppressor genes for cancer therapy. Bioessays 2015;37:1277-86. [PMID: 26445307 DOI: 10.1002/bies.201500093] [Cited by in Crossref: 41] [Cited by in F6Publishing: 43] [Article Influence: 5.1] [Reference Citation Analysis]
127 LeBlanc VG, Marra MA. Next-Generation Sequencing Approaches in Cancer: Where Have They Brought Us and Where Will They Take Us? Cancers (Basel) 2015;7:1925-58. [PMID: 26404381 DOI: 10.3390/cancers7030869] [Cited by in Crossref: 41] [Cited by in F6Publishing: 44] [Article Influence: 5.1] [Reference Citation Analysis]
128 Cheng F, Zhao J, Zhao Z. Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes. Brief Bioinform 2016;17:642-56. [PMID: 26307061 DOI: 10.1093/bib/bbv068] [Cited by in Crossref: 89] [Cited by in F6Publishing: 99] [Article Influence: 11.1] [Reference Citation Analysis]
129 Niroula A, Vihinen M. Harmful somatic amino acid substitutions affect key pathways in cancers. BMC Med Genomics 2015;8:53. [PMID: 26282678 DOI: 10.1186/s12920-015-0125-x] [Cited by in Crossref: 13] [Cited by in F6Publishing: 14] [Article Influence: 1.6] [Reference Citation Analysis]
130 Arsuaga J, Borrman T, Cavalcante R, Gonzalez G, Park C. Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology. Microarrays (Basel) 2015;4:339-69. [PMID: 27600228 DOI: 10.3390/microarrays4030339] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 1.4] [Reference Citation Analysis]
131 Leiserson MD, Gramazio CC, Hu J, Wu HT, Laidlaw DH, Raphael BJ. MAGI: visualization and collaborative annotation of genomic aberrations. Nat Methods 2015;12:483-4. [PMID: 26020500 DOI: 10.1038/nmeth.3412] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 2.3] [Reference Citation Analysis]
132 Wei L, Liu LT, Conroy JR, Hu Q, Conroy JM, Morrison CD, Johnson CS, Wang J, Liu S. MAC: identifying and correcting annotation for multi-nucleotide variations. BMC Genomics 2015;16:569. [PMID: 26231518 DOI: 10.1186/s12864-015-1779-7] [Cited by in Crossref: 23] [Cited by in F6Publishing: 23] [Article Influence: 2.9] [Reference Citation Analysis]
133 Gross A, Schoendube J, Zimmermann S, Steeb M, Zengerle R, Koltay P. Technologies for Single-Cell Isolation. Int J Mol Sci 2015;16:16897-919. [PMID: 26213926 DOI: 10.3390/ijms160816897] [Cited by in Crossref: 240] [Cited by in F6Publishing: 250] [Article Influence: 30.0] [Reference Citation Analysis]
134 Kang X, Chen K, Li Y, Li J, D'Amico TA, Chen X. Personalized targeted therapy for esophageal squamous cell carcinoma. World J Gastroenterol 2015; 21(25): 7648-7658 [PMID: 26167067 DOI: 10.3748/wjg.v21.i25.7648] [Cited by in CrossRef: 29] [Cited by in F6Publishing: 34] [Article Influence: 3.6] [Reference Citation Analysis]
135 Lim JS, Lee JH. Molecular genetic decoding of malformations of cortical development. J Genet Med 2015;12:12-8. [DOI: 10.5734/jgm.2015.12.1.12] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.1] [Reference Citation Analysis]
136 Wills QF, Mead AJ. Application of single-cell genomics in cancer: promise and challenges. Hum Mol Genet 2015;24:R74-84. [PMID: 26113645 DOI: 10.1093/hmg/ddv235] [Cited by in Crossref: 48] [Cited by in F6Publishing: 49] [Article Influence: 6.0] [Reference Citation Analysis]
137 Tian R, Basu MK, Capriotti E. Computational methods and resources for the interpretation of genomic variants in cancer. BMC Genomics 2015;16 Suppl 8:S7. [PMID: 26111056 DOI: 10.1186/1471-2164-16-S8-S7] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 1.8] [Reference Citation Analysis]
138 Sallam RM. Proteomics in cancer biomarkers discovery: challenges and applications. Dis Markers 2015;2015:321370. [PMID: 25999657 DOI: 10.1155/2015/321370] [Cited by in Crossref: 59] [Cited by in F6Publishing: 61] [Article Influence: 7.4] [Reference Citation Analysis]
139 Hastings RJ, Bown N, Tibiletti MG, Debiec-Rychter M, Vanni R, Espinet B, van Roy N, Roberts P, van den Berg-de-Ruiter E, Bernheim A, Schoumans J, Chatters S, Zemanova Z, Stevens-Kroef M, Simons A, Heim S, Salido M, Ylstra B, Betts DR; Tumour Best Practice meeting., Eurogentest. Guidelines for cytogenetic investigations in tumours. Eur J Hum Genet 2016;24:6-13. [PMID: 25804401 DOI: 10.1038/ejhg.2015.35] [Cited by in Crossref: 21] [Cited by in F6Publishing: 22] [Article Influence: 2.6] [Reference Citation Analysis]
140 Beck AH. Open access to large scale datasets is needed to translate knowledge of cancer heterogeneity into better patient outcomes. PLoS Med 2015;12:e1001794. [PMID: 25710538 DOI: 10.1371/journal.pmed.1001794] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 1.5] [Reference Citation Analysis]
141 McGranahan N, Swanton C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 2015;27:15-26. [PMID: 25584892 DOI: 10.1016/j.ccell.2014.12.001] [Cited by in Crossref: 761] [Cited by in F6Publishing: 612] [Article Influence: 95.1] [Reference Citation Analysis]
142 Lavi O. Redundancy: a critical obstacle to improving cancer therapy. Cancer Res 2015;75:808-12. [PMID: 25576083 DOI: 10.1158/0008-5472.CAN-14-3256] [Cited by in Crossref: 41] [Cited by in F6Publishing: 41] [Article Influence: 5.1] [Reference Citation Analysis]
143 Farah CS, Jessri M, Kordbacheh F, Bennett NC, Dalley A. Next-Generation Sequencing Applications in Head and Neck Oncology. Next Generation Sequencing in Cancer Research, Volume 2 2015. [DOI: 10.1007/978-3-319-15811-2_23] [Reference Citation Analysis]
144 Freed D, Stevens EL, Pevsner J. Somatic mosaicism in the human genome. Genes (Basel) 2014;5:1064-94. [PMID: 25513881 DOI: 10.3390/genes5041064] [Cited by in Crossref: 96] [Cited by in F6Publishing: 101] [Article Influence: 10.7] [Reference Citation Analysis]
145 Bennett NC, Farah CS. Next-generation sequencing in clinical oncology: next steps towards clinical validation. Cancers (Basel) 2014;6:2296-312. [PMID: 25412366 DOI: 10.3390/cancers6042296] [Cited by in Crossref: 39] [Cited by in F6Publishing: 41] [Article Influence: 4.3] [Reference Citation Analysis]
146 Ma T. Integrative and interdisciplinary challenges in translational bioinformatics. ACM SIGBioinformatics Rec 2014;4:1-6. [DOI: 10.1145/2661732.2661733] [Cited by in Crossref: 1] [Article Influence: 0.1] [Reference Citation Analysis]
147 [DOI: 10.1101/612762] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
148 [DOI: 10.1101/152264] [Cited by in Crossref: 1] [Reference Citation Analysis]