An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions

Protein-protein interactions (PPI) play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from high-throughput technologies to infer PPI has been observed. However, results obtained from such experiments show high rates of false positives and false negatives predictions as well as systematic predictive bias. Recent research has revealed that several machine and statistical learning methods applied to integrate relatively weak, diverse sources of large-scale functional data may provide improved predictive accuracy and coverage of PPI. In this paper we describe the effects of applying different computational, integrative methods to predict PPI in Saccharomyces cerevisiae. We investigated the predictive ability of combining different sets of relatively strong and weak predictive datasets. We analysed several genomic datasets ranging from mRNA co-expression to marginal essentiality. Moreover, we expanded an ...

• Type: Journal
• Year: 2006
• Where: JIB
• Authors: Fiona Browne, Haiying Wang, Huiru Zheng, Francisco Azuaje
• Keywords: JIB 2006, Predictive, Systematic Predictive Bias, Weak Predictive Datasets