✦ LIBER ✦

Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features

✍ Scribed by Sung-Bae Cho; Jungwon Ryu

Book ID: 118694545
Publisher: IEEE
Year: 2002
Tongue: English
Weight: 825 KB
Volume: 90
Category: Article
ISSN: 0018-9219
DOI: 10.1109/jproc.2002.804682

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Tumor tissue identification based on gen

Tumor tissue identification based on gene expression data using DWT feature extraction and PNN classifier

✍ Guangmin Sun; Xiaoying Dong; Guandong Xu 📂 Article 📅 2006 🏛 Elsevier Science 🌐 English ⚖ 320 KB

Classifying Lung Cancer Recurrence Time

Classifying Lung Cancer Recurrence Time Using Novel Ensemble Method with Gene Network based Input Models

✍ Ford, William; Park, Jin Woo; Campbell, Aaron S.; Deng, Youping; Li, Yan; Land, 📂 Article 📅 2012 🏛 Elsevier 🌐 English ⚖ 403 KB

Robust assignment of cancer subtypes fro

Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier

✍ Martin Lauss; Attila Frigyesi; Tobias Ryden; Mattias Höglund 📂 Article 📅 2010 🏛 BioMed Central 🌐 English ⚖ 887 KB

MiRenSVM: towards better prediction of m

MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features

✍ Jiandong Ding; Shuigeng Zhou; Jihong Guan 📂 Article 📅 2010 🏛 BioMed Central 🌐 English ⚖ 814 KB

Accurate Molecular Classification of Hum

Accurate Molecular Classification of Human Cancers Based on Gene Expression Using a Simple Classifier with a Pathological Tree-Based Framework

✍ Kerby A. Shedden; Jeremy M.G. Taylor; Thomas J. Giordano; Rork Kuick; David E. M 📂 Article 📅 2003 🏛 American Society for Investigative Pathology 🌐 English ⚖ 162 KB

Risk classification of cancer survival u

Risk classification of cancer survival using ANN with gene expression data from multiple laboratories

✍ Chen, Yen Chen (author);Ke, Wan Chi (author);Chiu, Hung Wen (author) 📂 Article 📅 2014 🏛 Elsevier Ltd 🌐 English ⚖ 642 KB