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Items: 1 to 20 of 108

1.

EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma.

Shao B, Bjaanæs MM, Helland Å, Schütte C, Conrad T.

PLoS One. 2019 Jan 31;14(1):e0204186. doi: 10.1371/journal.pone.0204186. eCollection 2019.

2.

Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

Liu C, Wang X, Genchev GZ, Lu H.

Methods. 2017 Jul 15;124:100-107. doi: 10.1016/j.ymeth.2017.06.010. Epub 2017 Jun 13.

PMID:
28627406
3.

Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data.

El-Manzalawy Y, Hsieh TY, Shivakumar M, Kim D, Honavar V.

BMC Med Genomics. 2018 Sep 14;11(Suppl 3):71. doi: 10.1186/s12920-018-0388-0.

4.

Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.

Kim D, Li R, Dudek SM, Ritchie MD.

J Biomed Inform. 2015 Aug;56:220-8. doi: 10.1016/j.jbi.2015.05.019. Epub 2015 Jun 3.

5.

An integrative imputation method based on multi-omics datasets.

Lin D, Zhang J, Li J, Xu C, Deng HW, Wang YP.

BMC Bioinformatics. 2016 Jun 21;17:247. doi: 10.1186/s12859-016-1122-6.

6.

Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification.

Wolahan SM, Hirt D, Glenn TC.

In: Kobeissy FH, editor. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (FL): CRC Press/Taylor & Francis; 2015. Chapter 25.

7.

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer.

Huang Z, Zhan X, Xiang S, Johnson TS, Helm B, Yu CY, Zhang J, Salama P, Rizkalla M, Han Z, Huang K.

Front Genet. 2019 Mar 8;10:166. doi: 10.3389/fgene.2019.00166. eCollection 2019.

8.

Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction.

Kim D, Shin H, Sohn KA, Verma A, Ritchie MD, Kim JH.

Methods. 2014 Jun 1;67(3):344-53. doi: 10.1016/j.ymeth.2014.02.003. Epub 2014 Feb 18.

9.

A universal deep learning approach for modeling the flow of patients under different severities.

Jiang S, Chin KS, Tsui KL.

Comput Methods Programs Biomed. 2018 Feb;154:191-203. doi: 10.1016/j.cmpb.2017.11.003. Epub 2017 Nov 7.

PMID:
29249343
10.

Sparse overlapping group lasso for integrative multi-omics analysis.

Park H, Niida A, Miyano S, Imoto S.

J Comput Biol. 2015 Feb;22(2):73-84. doi: 10.1089/cmb.2014.0197. Epub 2015 Jan 28.

PMID:
25629319
12.

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

Deist TM, Dankers FJWM, Valdes G, Wijsman R, Hsu IC, Oberije C, Lustberg T, van Soest J, Hoebers F, Jochems A, El Naqa I, Wee L, Morin O, Raleigh DR, Bots W, Kaanders JH, Belderbos J, Kwint M, Solberg T, Monshouwer R, Bussink J, Dekker A, Lambin P.

Med Phys. 2018 Jul;45(7):3449-3459. doi: 10.1002/mp.12967. Epub 2018 Jun 13. Erratum in: Med Phys. 2019 Feb;46(2):1080-1087.

PMID:
29763967
13.

biosigner: A New Method for the Discovery of Significant Molecular Signatures from Omics Data.

Rinaudo P, Boudah S, Junot C, Thévenot EA.

Front Mol Biosci. 2016 Jun 21;3:26. doi: 10.3389/fmolb.2016.00026. eCollection 2016.

14.

A computational framework for complex disease stratification from multiple large-scale datasets.

De Meulder B, Lefaudeux D, Bansal AT, Mazein A, Chaiboonchoe A, Ahmed H, Balaur I, Saqi M, Pellet J, Ballereau S, Lemonnier N, Sun K, Pandis I, Yang X, Batuwitage M, Kretsos K, van Eyll J, Bedding A, Davison T, Dodson P, Larminie C, Postle A, Corfield J, Djukanovic R, Chung KF, Adcock IM, Guo YK, Sterk PJ, Manta A, Rowe A, Baribaud F, Auffray C; U-BIOPRED Study Group and the eTRIKS Consortium.

BMC Syst Biol. 2018 May 29;12(1):60. doi: 10.1186/s12918-018-0556-z.

15.

Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice.

Iuliano A, Occhipinti A, Angelini C, De Feis I, Lió P.

Front Physiol. 2016 Jun 17;7:208. doi: 10.3389/fphys.2016.00208. eCollection 2016.

16.

NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.

Zou M, Liu Z, Zhang XS, Wang Y.

Bioinformatics. 2015 Oct 15;31(20):3330-8. doi: 10.1093/bioinformatics/btv374. Epub 2015 Jun 18.

PMID:
26092859
17.

Biomarker signature identification in "omics" data with multi-class outcome.

Lagani V, Kortas G, Tsamardinos I.

Comput Struct Biotechnol J. 2013 Jun 8;6:e201303004. doi: 10.5936/csbj.201303004. eCollection 2013.

18.

Inferring microRNA and transcription factor regulatory networks in heterogeneous data.

Le TD, Liu L, Liu B, Tsykin A, Goodall GJ, Satou K, Li J.

BMC Bioinformatics. 2013 Mar 11;14:92. doi: 10.1186/1471-2105-14-92.

19.

A Composite Gene Expression Signature Optimizes Prediction of Colorectal Cancer Metastasis and Outcome.

Schell MJ, Yang M, Missiaglia E, Delorenzi M, Soneson C, Yue B, Nebozhyn MV, Loboda A, Bloom G, Yeatman TJ.

Clin Cancer Res. 2016 Feb 1;22(3):734-45. doi: 10.1158/1078-0432.CCR-15-0143. Epub 2015 Oct 7.

20.

An Improved Method for Prediction of Cancer Prognosis by Network Learning.

Kim M, Oh I, Ahn J.

Genes (Basel). 2018 Oct 2;9(10). pii: E478. doi: 10.3390/genes9100478.

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