133 related articles for article (PubMed ID: 29961866)
1. Unraveling the role of low-frequency mutated genes in breast cancer.
Lusito E; Felice B; D'Ario G; Ogier A; Montani F; Di Fiore PP; Bianchi F
Bioinformatics; 2019 Jan; 35(1):36-46. PubMed ID: 29961866
[TBL] [Abstract][Full Text] [Related]
2. WeSME: uncovering mutual exclusivity of cancer drivers and beyond.
Kim YA; Madan S; Przytycka TM
Bioinformatics; 2017 Mar; 33(6):814-821. PubMed ID: 27153670
[TBL] [Abstract][Full Text] [Related]
3. A new correlation clustering method for cancer mutation analysis.
Hou JP; Emad A; Puleo GJ; Ma J; Milenkovic O
Bioinformatics; 2016 Dec; 32(24):3717-3728. PubMed ID: 27540270
[TBL] [Abstract][Full Text] [Related]
4. Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.
Suo C; Hrydziuszko O; Lee D; Pramana S; Saputra D; Joshi H; Calza S; Pawitan Y
Bioinformatics; 2015 Aug; 31(16):2607-13. PubMed ID: 25810432
[TBL] [Abstract][Full Text] [Related]
5. Extreme learning machines for reverse engineering of gene regulatory networks from expression time series.
Rubiolo M; Milone DH; Stegmayer G
Bioinformatics; 2018 Apr; 34(7):1253-1260. PubMed ID: 29182723
[TBL] [Abstract][Full Text] [Related]
6. MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules.
Ahmed R; Baali I; Erten C; Hoxha E; Kazan H
Bioinformatics; 2020 Feb; 36(3):872-879. PubMed ID: 31432076
[TBL] [Abstract][Full Text] [Related]
7. Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer.
Gendoo DM; Ratanasirigulchai N; Schröder MS; Paré L; Parker JS; Prat A; Haibe-Kains B
Bioinformatics; 2016 Apr; 32(7):1097-9. PubMed ID: 26607490
[TBL] [Abstract][Full Text] [Related]
8. Cancer Gene Discovery by Network Analysis of Somatic Mutations Using the MUFFINN Server.
Han H; Lehner B; Lee I
Methods Mol Biol; 2019; 1907():37-50. PubMed ID: 30542989
[TBL] [Abstract][Full Text] [Related]
9. Integrating Microarray Data and GRNs.
Koumakis L; Potamias G; Tsiknakis M; Zervakis M; Moustakis V
Methods Mol Biol; 2016; 1375():137-53. PubMed ID: 26134183
[TBL] [Abstract][Full Text] [Related]
10. Hidden Markov models lead to higher resolution maps of mutation signature activity in cancer.
Wojtowicz D; Sason I; Huang X; Kim YA; Leiserson MDM; Przytycka TM; Sharan R
Genome Med; 2019 Jul; 11(1):49. PubMed ID: 31349863
[TBL] [Abstract][Full Text] [Related]
11. Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett's esophagus.
Lv J; Guo L; Wang JH; Yan YZ; Zhang J; Wang YY; Yu Y; Huang YF; Zhao HP
World J Gastroenterol; 2019 Jan; 25(2):233-244. PubMed ID: 30670912
[TBL] [Abstract][Full Text] [Related]
12. BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions.
Dao P; Kim YA; Wojtowicz D; Madan S; Sharan R; Przytycka TM
PLoS Comput Biol; 2017 Oct; 13(10):e1005695. PubMed ID: 29023534
[TBL] [Abstract][Full Text] [Related]
13. Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism.
Angione C
Bioinformatics; 2018 Feb; 34(3):494-501. PubMed ID: 28968777
[TBL] [Abstract][Full Text] [Related]
14. GECO: gene expression correlation analysis after genetic algorithm-driven deconvolution.
Najafov J; Najafov A
Bioinformatics; 2019 Jan; 35(1):156-159. PubMed ID: 30010797
[TBL] [Abstract][Full Text] [Related]
15. An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.
Park S; Kim SJ; Yu D; Peña-Llopis S; Gao J; Park JS; Chen B; Norris J; Wang X; Chen M; Kim M; Yong J; Wardak Z; Choe K; Story M; Starr T; Cheong JH; Hwang TH
Bioinformatics; 2016 Jun; 32(11):1643-51. PubMed ID: 26635139
[TBL] [Abstract][Full Text] [Related]
16. CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data.
Zheng G; Xu Y; Zhang X; Liu ZP; Wang Z; Chen L; Zhu XG
BMC Bioinformatics; 2016 Dec; 17(Suppl 17):535. PubMed ID: 28155637
[TBL] [Abstract][Full Text] [Related]
17. LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer.
Melloni GE; de Pretis S; Riva L; Pelizzola M; Céol A; Costanza J; Müller H; Zammataro L
BMC Bioinformatics; 2016 Feb; 17():80. PubMed ID: 26860319
[TBL] [Abstract][Full Text] [Related]
18. The DNA walk and its demonstration of deterministic chaos-relevance to genomic alterations in lung cancer.
Hewelt B; Li H; Jolly MK; Kulkarni P; Mambetsariev I; Salgia R
Bioinformatics; 2019 Aug; 35(16):2738-2748. PubMed ID: 30615123
[TBL] [Abstract][Full Text] [Related]
19. MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations.
Liu S; Liu J; Xie Y; Zhai T; Hinderer EW; Stromberg AJ; Vanderford NL; Kolesar JM; Moseley HNB; Chen L; Liu C; Wang C
Bioinformatics; 2021 Jun; 37(9):1189-1197. PubMed ID: 33165532
[TBL] [Abstract][Full Text] [Related]
20. Cancer mutational signatures representation by large-scale context embedding.
Zhang Y; Xiao Y; Yang M; Ma J
Bioinformatics; 2020 Jul; 36(Suppl_1):i309-i316. PubMed ID: 32657413
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
