Abstract. This paper present a new approach for the analysis of gene expression, by extracting a Markov Chain from trained Recurrent Neural Networks (RNNs). A lot of microarray dat...
Igor Lorenzato Almeida, Denise Regina Pechmann Sim...
The analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. In this paper we explore a ...
Allan Tucker, Peter A. C. 't Hoen, Veronica Vincio...
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...
Background: In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is co...
Background: Microarray technologies have become common tools in biological research. As a result, a need for effective computational methods for data analysis has emerged. Numerou...
Background: Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGEML...
Tim F. Rayner, Philippe Rocca-Serra, Paul T. Spell...
Background: In class prediction problems using microarray data, gene selection is essential to improve the prediction accuracy and to identify potential marker genes for a disease...
Background: Evaluating the importance of the different sources of variations is essential in microarray data experiments. Complex experimental designs generally include various fa...
Background: Microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise co...
Background: DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously. With the advance of microarray technology, the challeng...
Guoqing Lu, The V. Nguyen, Yuannan Xia, Michael Fr...