@inproceedings{27070b69db4d48d88d9a5a02252b4508,
title = "A compressed sensing based feature extraction method for identifying characteristic genes",
abstract = "In current molecular biology, it becomes more and more important to identify characteristic genes closely correlated with a key biological process from gene expression data. In this paper, a novel compressed sensing (CS) based feature extraction method named CSGS is proposed to identify the characteristic genes. Considering the transposed gene expression matrix and class labels as sensing matrix and measurement vector, respectively, CS reconstruction is implemented by basis pursuit algorithm. Top ranking genes with high signal weights are retained as the characteristic genes. Experiments of CSGS are performed on leukemia data set and compared with other sparse methods. Results demonstrate that CSGS is effective in identifying characteristic genes, and is not sensitive to parameters. CSGS could offer a simple way for feature extraction and provide more clues for biologists.",
keywords = "Characteristic genes, Compressed sensing, Feature extraction, Gene expression data",
author = "Li, \{Sheng Jun\} and Junliang Shang and Liu, \{Jin Xing\} and Huiyu Li",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 12th International Conference on Intelligent Computing Theories and Application, ICIC 2016 ; Conference date: 02-08-2016 Through 05-08-2016",
year = "2016",
doi = "10.1007/978-3-319-42294-7\_6",
language = "英语",
isbn = "9783319422930",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "67--77",
editor = "De-Shuang Huang and Kang-Hyun Jo",
booktitle = "Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings",
}