Partial least squares {using|utilizing|making use of|employing|working with

Partial least squares making use of microarray gene expression data and assessment of classification models. Comput Biol Chem , :-. Ma S, Dai Y: Principal component evaluation based techniques in bioinformatics studies. Brief BioinformPaatero P, Tapper U: Optimistic matrix factorization: A non-negative issue model with optimal utilization of errorest mates of information values. Environmetrics , :-. Lee DD, Seung HS: Mastering the components of objects by non-negative matrix factorization. Nature , :-. Hyvarinen A, Karhunen J, Oja E: Independent Component Evaluation. Interscience WBrunet JP, Tamayo P, Golub TR, Mesirov JP: Metagenes and molecular pattern discovery working with matrix factorization. Proc Natl Acad Sci U S A , :-. Li SZ, Hou XW, Zhang HJ, Cheng QS: Studying spatially localized partsbased order Tanshinone IIA representation. Proceedings of IEEE International Conference on Laptop Vision and Pattern Recognition: December , -. Hoyer PO: Non-negative sparse coding. Neural Networks for Signal Processing XII: ; Martigny, Switzerland , -. Hoyer PO, Dayan P: Non-negative Matrix ASP8273 factorization with sparseness constraints. Journal of Machine Understanding Investigation , :-. Wang Y, Jia Y, Hu C, Turk M: Fisher non-negative matrix factorization for mastering regional capabilities. Asian Conference on Pc Vision Jeju, Korea; , -. Gao Y, Church G: Enhancing molecular cancer class discovery via sparse non-negative matrix factorization. Bioinformatics , :-. Pauca P, Shahnaz F, Berry M, Plemmons R: Text Mining using NonNegative Matrix Factorizations. Proceedings with the Fourth SIAM International Conference on Information Mining: April ; Lake Buena Vista, Florida, USASokal RR: Clustering and classification: background and current directions. In Classification and Clustering. Academic Press, London;Van Ryzin J :-. Everitt BS: Cluster Evaluation. Edward Arnold;Sharan R, Maron-Katz A, Shamir R: CLICK and EXPANDER: PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/18055457?dopt=Abstract a technique for clustering and visualizing gene expression data. Bioinformatics , :-. Yeung KY, Haynor DR, Ruzzo WL: Validating clustering for gene expression data. Bioinformatics , :-. Dueck D, Morris QD, Frey BJ: Multi-way clustering of microarray information applying probabilistic sparse matrix factorization. Bioinformatics , (Suppl):i-. Xu Y, Olman V, Xu D: Clustering gene expression data making use of a graphtheoretic approach: an application of minimum spanning trees. Bioinformatics , :-. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, et al: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science , :-. Pomeroy SL, Tamayo P, Gaasenbeek M, Sturla LM, Angelo M, McLaughlin ME, Kim JY, Goumnerova LC, Black PM, Lau C, et al: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature , :-. Iyer VR, Eisen MB, Ross DT, Schuler G, Moore T, Lee JC, Trent JM, Staudt LM, Hudson J JrBoguski MS, et al: The transcriptional system in the response of human fibroblasts to serum. Science , :-. Fisher R: The usage of several measurements in taxonomic challenge. Ann Eugenics , :-. Bezdek J, Pal N: Some new indexes of cluster validity. IEEE Trans Syst Man Cybernet , :-. Halkidi M, Batistakis Y, Vazirgiannis M: On clustering validation methods. Journal of Intelligent Facts Systems , :-. Dunn J: Properly separated clusters and optimal fuzzy partitions. Journal of Cybernetics , – Davies DL, Bouldin DW: Acluster separation measure. IEEE Trans Pattern Anal Machine Intell , :-.Rousseeuw PJ: S.Partial least squares making use of microarray gene expression information and assessment of classification models. Comput Biol Chem , :-. Ma S, Dai Y: Principal element analysis primarily based methods in bioinformatics research. Brief BioinformPaatero P, Tapper U: Constructive matrix factorization: A non-negative aspect model with optimal utilization of errorest mates of data values. Environmetrics , :-. Lee DD, Seung HS: Learning the components of objects by non-negative matrix factorization. Nature , :-. Hyvarinen A, Karhunen J, Oja E: Independent Component Analysis. Interscience WBrunet JP, Tamayo P, Golub TR, Mesirov JP: Metagenes and molecular pattern discovery applying matrix factorization. Proc Natl Acad Sci U S A , :-. Li SZ, Hou XW, Zhang HJ, Cheng QS: Learning spatially localized partsbased representation. Proceedings of IEEE International Conference on Personal computer Vision and Pattern Recognition: December , -. Hoyer PO: Non-negative sparse coding. Neural Networks for Signal Processing XII: ; Martigny, Switzerland , -. Hoyer PO, Dayan P: Non-negative Matrix Factorization with sparseness constraints. Journal of Machine Learning Research , :-. Wang Y, Jia Y, Hu C, Turk M: Fisher non-negative matrix factorization for finding out regional attributes. Asian Conference on Personal computer Vision Jeju, Korea; , -. Gao Y, Church G: Enhancing molecular cancer class discovery via sparse non-negative matrix factorization. Bioinformatics , :-. Pauca P, Shahnaz F, Berry M, Plemmons R: Text Mining employing NonNegative Matrix Factorizations. Proceedings from the Fourth SIAM International Conference on Information Mining: April ; Lake Buena Vista, Florida, USASokal RR: Clustering and classification: background and current directions. In Classification and Clustering. Academic Press, London;Van Ryzin J :-. Everitt BS: Cluster Analysis. Edward Arnold;Sharan R, Maron-Katz A, Shamir R: CLICK and EXPANDER: PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/18055457?dopt=Abstract a system for clustering and visualizing gene expression data. Bioinformatics , :-. Yeung KY, Haynor DR, Ruzzo WL: Validating clustering for gene expression information. Bioinformatics , :-. Dueck D, Morris QD, Frey BJ: Multi-way clustering of microarray data utilizing probabilistic sparse matrix factorization. Bioinformatics , (Suppl):i-. Xu Y, Olman V, Xu D: Clustering gene expression information utilizing a graphtheoretic method: an application of minimum spanning trees. Bioinformatics , :-. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, et al: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science , :-. Pomeroy SL, Tamayo P, Gaasenbeek M, Sturla LM, Angelo M, McLaughlin ME, Kim JY, Goumnerova LC, Black PM, Lau C, et al: Prediction of central nervous program embryonal tumour outcome primarily based on gene expression. Nature , :-. Iyer VR, Eisen MB, Ross DT, Schuler G, Moore T, Lee JC, Trent JM, Staudt LM, Hudson J JrBoguski MS, et al: The transcriptional system in the response of human fibroblasts to serum. Science , :-. Fisher R: The use of a number of measurements in taxonomic dilemma. Ann Eugenics , :-. Bezdek J, Pal N: Some new indexes of cluster validity. IEEE Trans Syst Man Cybernet , :-. Halkidi M, Batistakis Y, Vazirgiannis M: On clustering validation approaches. Journal of Intelligent Info Systems , :-. Dunn J: Properly separated clusters and optimal fuzzy partitions. Journal of Cybernetics , – Davies DL, Bouldin DW: Acluster separation measure. IEEE Trans Pattern Anal Machine Intell , :-.Rousseeuw PJ: S.