✦ LIBER ✦

Prediction of regulatory interactions in Arabidopsis using gene-expression data and support vector machines

✍ Scribed by Xiaoqing Yu; Taigang Liu; Xiaoqi Zheng; Zhongnan Yang; Jun Wang

Publisher: Elsevier Science
Year: 2011
Tongue: English
Weight: 153 KB
Volume: 49
Category: Article
ISSN: 0981-9428
DOI: 10.1016/j.plaphy.2011.01.002

No coin nor oath required. For personal study only.

✦ Synopsis

Identification of regulatory relationships between transcription factors (TFs) and their targets is a central problem in post-genomic biology. In this paper, we apply an approach based on the support vector machine (SVM) and gene-expression data to predict the regulatory interactions in Arabidopsis. A set of 125 experimentally validated TF-target interactions and 750 negative regulatory gene pairs are collected as the training data. Their expression profiles data at 79 experimental conditions are fed to the SVM to perform the prediction. Through the jackknife cross-validation test, we find that the overall prediction accuracy of our approach achieves 88.68%. Our approach could help to widen the understanding of Arabidopsis gene regulatory scheme and may offer a cost-effective alternative to construct the gene regulatory network.

📜 SIMILAR VOLUMES

Prediction of Key State Variables using

Prediction of Key State Variables using Support Vector Machines in Bioprocesses

✍ Y. Li; J. Yuan 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 196 KB 👁 2 views

The use of hybrid manifold learning and

The use of hybrid manifold learning and support vector machines in the prediction of business failure

✍ Fengyi Lin; Ching-Chiang Yeh; Meng-Yuan Lee 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 348 KB

The prediction of business failure is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. This paper proposes a hybrid manifold learning approach model which combines both isometric feature mapping (ISOMAP) algorithm and support ve

Variable selection and oversampling in t

Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies

✍ Wolfgang Härdle; Yuh-Jye Lee; Dorothea Schäfer; Yi-Ren Yeh 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 291 KB 👁 1 views

## Abstract In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objectives regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of

Quantitative Prediction of logk of Pepti

Quantitative Prediction of logk of Peptides in High-Performance Liquid Chromatography Based on Molecular Descriptors by Using the Heuristic Method and Support Vector Machine.

✍ H. X. Liu; C. X. Xue; R. S. Zhang; X. J. Yao; M. C. Liu; Z. D. Hu; B. T. Fan 📂 Article 📅 2005 🏛 John Wiley and Sons ⚖ 8 KB 👁 3 views