Classification of gene functions using support vector machine for time-course gene expression data

2008 ◽  
Vol 52 (5) ◽  
pp. 2578-2587 ◽  
Author(s):  
Changyi Park ◽  
Ja-Yong Koo ◽  
Sujong Kim ◽  
Insuk Sohn ◽  
Jae Won Lee
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Gene Expression Data ◽  
Time Course ◽  
Support Vector ◽  
Expression Data ◽  
Gene Functions

scholarly journals An Empirical Comparison Study on Kernel Based Support Vector Machine for Classification of Gene Expression Data Set

2012 ◽  
Vol 38 ◽  
pp. 1340-1345 ◽  
Author(s):  
Smruti Rekha Das ◽  
Kaberi Das ◽  
Debahuti Mishra ◽  
Kailash Shaw ◽  
Sashikala Mishra
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Gene Expression Data ◽  
Support Vector ◽  
Expression Data ◽  
Comparison Study ◽  
Empirical Comparison ◽  
Data Set

scholarly journals dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using single cell time-course gene expression data

2021 ◽  
Author(s):  
Yu Xu ◽  
Jiaxing Chen ◽  
Aiping Lyu ◽  
William K Cheung ◽  
Lu Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Time Course ◽  
Target Genes ◽  
Neighborhood Context ◽  
Specific Gene ◽  
Expression Data ◽  
Regulatory Interactions ◽  
Gene Functions

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely applied to reconstruct the cell-type-specific gene regulatory networks by exploring the dynamic changes of gene expression between transcription factors (TFs) and their target genes. The existing algorithms were commonly designed to analyze bulk gene expression data and could not deal with the dropouts and cell heterogeneity in scRNA-seq data. In this paper, we developed dynDeepDRIM that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. dynDeepDRIM integrated the primary image, neighbor images with time-course into a four-dimensional tensor and trained a convolutional neural network to predict the direct regulatory interactions between TFs and genes. We evaluated the performance of dynDeepDRIM on five time-course gene expression datasets. dynDeepDRIM outperformed the state-of-the-art methods for predicting TF-gene direct interactions and gene functions. We also observed gene functions could be better performed if more neighbor images were involved.

scholarly journals Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines

PLoS ONE ◽  
2010 ◽  
Vol 5 (6) ◽  
pp. e11267 ◽  
Author(s):  
Ana Lisa V. Gomes ◽  
Lawrence J. K. Wee ◽  
Asif M. Khan ◽  
Laura H. V. G. Gil ◽  
Ernesto T. A. Marques ◽  
...  
Keyword(s):  
Gene Expression ◽  
Support Vector Machines ◽  
Dengue Fever ◽  
Gene Expression Data ◽  
Support Vector ◽  
Expression Data ◽  
Vector Machines

scholarly journals Sparse Representation for Classification of Tumors Using Gene Expression Data

2009 ◽  
Vol 2009 ◽  
pp. 1-6 ◽  
Author(s):  
Xiyi Hang ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Sparse Representation ◽  
Gene Expression Data ◽  
Least Square Method ◽  
Classification Problem ◽  
Least Square ◽  
Support Vector ◽  
Expression Data ◽  
Classification Of Tumors

Personalized drug design requires the classification of cancer patients as accurate as possible. With advances in genome sequencing and microarray technology, a large amount of gene expression data has been and will continuously be produced from various cancerous patients. Such cancer-alerted gene expression data allows us to classify tumors at the genomewide level. However, cancer-alerted gene expression datasets typically have much more number of genes (features) than that of samples (patients), which imposes a challenge for classification of tumors. In this paper, a new method is proposed for cancer diagnosis using gene expression data by casting the classification problem as finding sparse representations of test samples with respect to training samples. The sparse representation is computed by thel1-regularized least square method. To investigate its performance, the proposed method is applied to six tumor gene expression datasets and compared with various support vector machine (SVM) methods. The experimental results have shown that the performance of the proposed method is comparable with or better than those of SVMs. In addition, the proposed method is more efficient than SVMs as it has no need of model selection.

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