Filters








247 Hits in 3.2 sec

Multiclass Microarray Gene Expression Analysis Based on Mutual Dependency Models [chapter]

Girija Chetty, Madhu Chetty
2009 Lecture Notes in Computer Science  
In this paper a novel feature selection technique based on mutual dependency modelling between genes is proposed for multiclass microarray gene expression classification.  ...  This can be done by modeling gene profiles for multiclass microarray gene data sets based on mutual dependency models, which model complex gene interactions.  ...  Conclusions and Further Plan In this paper a novel feature selection technique based on mutual dependency modelling between genes is proposed for multiclass microarray gene expression classification.  ... 
doi:10.1007/978-3-642-04031-3_5 fatcat:y72cgmjacbeirlvruanjxpi63m

Multiclass microarray gene expression classification based on fusion of correlation features

G Chetty, M Chetty
2010 2010 13th International Conference on Information Fusion  
In this paper, we propose novel algorithmic models based on fusion of independent and correlated gene features for multiclass microarray gene expression classification.  ...  data sets corresponding to multiclass microarray gene expression data.  ...  Conclusions and Further Work A novel feature selection technique based on correlation modelling between genes is proposed in this paper for multiclass microarray gene expression classification.  ... 
doi:10.1109/icif.2010.5711915 fatcat:oefkgocqxffdrbddo3svoyg46e

Iterative ensemble feature selection for multiclass classification of imbalanced microarray data

Junshan Yang, Jiarui Zhou, Zexuan Zhu, Xiaoliang Ma, Zhen Ji
2016 Journal of Biological Research - Thessaloniki  
Microarray technology allows biologists to monitor expression levels of thousands of genes among various tumor tissues.  ...  One of the most widely used classification strategies for multiclass classification data is the One-Versus-All (OVA) schema that divides the original problem into multiple binary classification of one  ...  The paper indicated that multiclass classification problem is much more difficult than the binary one for gene expression data.  ... 
doi:10.1186/s40709-016-0045-8 pmid:27437198 pmcid:PMC4943507 fatcat:aeoli7if7vcy5la6q37g677q64

Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis

Davies Segera, Mwangi Mbuthia, Abraham Nyete
2019 BioMed Research International  
To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis  ...  Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel,  ...  Furey proposed an SVM based on a simple kernel to carry out gene expression data analysis, which turned out to perform remarkably [27] .  ... 
doi:10.1155/2019/4085725 pmid:31998772 pmcid:PMC6973196 fatcat:uc3wyjif6zhwzj2zhircgypemy

Multiclass classification of microarray data samples with a reduced number of genes

Elizabeth Tapia, Leonardo Ornella, Pilar Bulacio, Laura Angelone
2011 BMC Bioinformatics  
Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size.  ...  Results: A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented.  ...  multiclass classification of microarray data samples based on a reduced number of genes.  ... 
doi:10.1186/1471-2105-12-59 pmid:21342522 pmcid:PMC3056725 fatcat:7i24ujejh5bt5ffks5i3mhlk7a

Gene selection via the BAHSIC family of algorithms

Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alex Smola
2007 Computer applications in the biosciences : CABIOS  
Motivation Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics.  ...  As a further advantage, feature selection via BAHSIC works directly on multiclass problems.  ...  INTRODUCTION Gene selection from microarray data is clearly one of the most popular topics in bioinformatics. To illustrate this, the database for "Bibliography on Microarray Data Analysis" (?)  ... 
doi:10.1093/bioinformatics/btm216 pmid:17646335 fatcat:2bwwqp3gvvfbvm527psar5qx2a

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

Hala Alshamlan, Ghada Badr, Yousef Alohali
2015 BioMed Research International  
We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets.  ...  The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes.  ...  dependency on the target class or have the minimal redundancy on the selected gene subset .  ... 
doi:10.1155/2015/604910 pmid:25961028 pmcid:PMC4414228 fatcat:ulpmlux7rzeclefyhb5aofhtym

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data

Hui Wen Nies, Mohd Saberi Mohamad, Zalmiyah Zakaria, Weng Howe Chan, Muhammad Akmal Remli, Yong Hui Nies
2021 Entropy  
Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions.  ...  An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+.  ...  8] , only use gene expression data for microarray analysis.  ... 
doi:10.3390/e23091232 pmid:34573857 pmcid:PMC8472068 fatcat:yck4wfs4crbdfg3k74g26mxw34

Multiclass Prediction with Partial Least Square Regression for Gene Expression Data: Applications in Breast Cancer Intrinsic Taxonomy

Chi-Cheng Huang, Shih-Hsin Tu, Ching-Shui Huang, Heng-Hui Lien, Liang-Chuan Lai, Eric Y. Chuang
2013 BioMed Research International  
Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles.  ...  The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype.  ...  predicted values based on that fitted model and computed the model again (expectation-maximization algorithm).  ... 
doi:10.1155/2013/248648 pmid:24490149 pmcid:PMC3893734 fatcat:ovwvz5pgovbk5k643a5rjktaw4

Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level

Daniel Castillo, Juan Manuel Galvez, Luis J. Herrera, Fernando Rojas, Olga Valenzuela, Octavio Caba, Jose Prados, Ignacio Rojas, Enrique Hernandez-Lemus
2019 PLoS ONE  
, quantified at gene expression.  ...  For this purpose, this work presents an integration of multiple Microarray and RNA-seq platforms, which has led to the design of a multiclass study by collecting samples from the main four types of leukemia  ...  in which the most relevant Leukemia multiclass assessment from Microarray and RNA-seq at gene expression level genes would be placed on the first positions within of this ranking.  ... 
doi:10.1371/journal.pone.0212127 pmid:30753220 pmcid:PMC6372182 fatcat:z5qfelrs6zgr7imzs7lqdseix4

Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series

Juan Manuel Gálvez, Daniel Castillo, Luis Javier Herrera, Belén San Román, Olga Valenzuela, Francisco Manuel Ortuño, Ignacio Rojas, Paolo Provero
2018 PLoS ONE  
These genes were obtained from the assessment of a number of potential batch effects on the gene expression data.  ...  Moreover, the influence of potential deviations on the gene expression quantification is usually disregarded.  ...  Up to our best knowledge, the integration of different datasets based on gene expression analysis still remains unprecedented.  ... 
doi:10.1371/journal.pone.0196836 pmid:29750795 pmcid:PMC5947894 fatcat:gh52lt65q5hdfnyoibqyacvcde

Robust and Stable Gene Selection via Maximum-Minimum Correntropy Criterion [article]

Majid Mohammadi, Hossein Sharifi Noghabi, Ghosheh Abed Hodtani, Habib Rajabi Mashhadi
2015 bioRxiv   pre-print
One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray.  ...  Through broad experimental evaluation, MMCC is proved to be significantly better compared to other well-known gene selection algorithms for 25 commonly used microarray data sets.  ...  [20] applied feature selection for tissue classification based on gene expression. Zhang et al.  ... 
doi:10.1101/029538 fatcat:fe3qmoubprh5vpjmjeplxytpb4

Robust and stable gene selection via Maximum–Minimum Correntropy Criterion

Majid Mohammadi, Hossein Sharifi Noghabi, Ghosheh Abed Hodtani, Habib Rajabi Mashhadi
2016 Genomics  
One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray.  ...  Through broad experimental evaluation, MMCC is proved to be significantly better compared to other well-known gene selection algorithms for 25 commonly used microarray data sets.  ...  [20] applied feature selection for tissue classification based on gene expression. Zhang et al.  ... 
doi:10.1016/j.ygeno.2015.12.006 pmid:26762945 fatcat:cr2x6bunkjhfvaen4blzbiagna

Gene Set-Based Functionome Analysis of Pathogenesis in Epithelial Ovarian Serous Carcinoma and the Molecular Features in Different FIGO Stages

Chia-Ming Chang, Chi-Mu Chuang, Mong-Lien Wang, Ming-Jie Yang, Cheng-Chang Chang, Ming-Shyen Yen, Shih-Hwa Chiou
2016 International Journal of Molecular Sciences  
The function, as defined by the GO term or canonical pathway gene set, was quantified by measuring the changes in the gene expressional order between cancerous and normal control states.  ...  Here, we conducted a whole-genome integrative analysis to investigate the functions of SC at different stages.  ...  This conversion makes it feasible for the GSR model to integrate the microarray gene expression datasets from different microarray platforms. This model had limitations.  ... 
doi:10.3390/ijms17060886 pmid:27275818 pmcid:PMC4926420 fatcat:w77a6rl4yjfvrbyokspadj5djq

Gene Expression Data Classification Using Consensus Independent Component Analysis

Chun-Hou Zheng, De-Shuang Huang, Xiang-Zhen Kong, Xing-Ming Zhao
2008 Genomics, Proteomics & Bioinformatics  
Firstly, the original DNA microarray gene expression data are modeled by independent component analysis (ICA).  ...  We propose a new method for tumor classif ication from gene expression data, which mainly contains three steps.  ...  Conclusion In this paper, we presented ICA methods for the classification of tumors based on microarray gene expression data.  ... 
doi:10.1016/s1672-0229(08)60022-4 pmid:18973863 pmcid:PMC5054104 fatcat:hd6dm23kjnaydlr357ocashs6e
« Previous Showing results 1 — 15 out of 247 results