✦ LIBER ✦

Clustering short time series gene expression data

✍ Scribed by Ernst, J.; Nau, G. J.; Bar-Joseph, Z.

Book ID: 125886836
Publisher: Oxford University Press
Year: 2005
Tongue: English
Weight: 680 KB
Volume: 21
Category: Article
ISSN: 1367-4803
DOI: 10.1093/bioinformatics/bti1022

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Clustering of unevenly sampled gene expr

Clustering of unevenly sampled gene expression time-series data

✍ C.S. Möller-Levet; F. Klawonn; K.-H. Cho; H. Yin; O. Wolkenhauer 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 341 KB

Functional clustering of time series gen

Functional clustering of time series gene expression data by Granger causality

✍ André Fujita, Patricia Severino, Kaname Kojima, João Ricardo Sato… 📂 Article 📅 2012 🏛 BioMed Central 🌐 English ⚖ 353 KB

Stochastic Dynamic Modeling of Short Gen

Stochastic Dynamic Modeling of Short Gene Expression Time-Series Data

✍ Zidong Wang, ; Fuwen Yang, ; Ho, D.W.C.; Swift, S.; Tucker, A.; Xiaohui Liu 📂 Article 📅 2008 🏛 IEEE 🌐 English ⚖ 316 KB

Analyzing time series gene expression da

Analyzing time series gene expression data

✍ Bar-Joseph, Z. 📂 Article 📅 2004 🏛 Oxford University Press 🌐 English ⚖ 230 KB

Extracting biologically significant patt

Extracting biologically significant patterns from short time series gene expression data

✍ Alain B Tchagang; Kevin V Bui; Thomas McGinnis; Panayiotis V Benos 📂 Article 📅 2009 🏛 BioMed Central 🌐 English ⚖ 906 KB

Clustering multivariate time-series data

✍ Ashish Singhal; Dale E. Seborg 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 212 KB

## Abstract A new methodology for clustering multivariate time‐series data is proposed. The new methodology is based on calculating the degree of similarity between multivariate time‐series datasets using two similarity factors. One similarity factor is based on principal component analysis and the