Statistical and Machine Learning Approaches for Network Analysis

4.1 Introduction

4.1.1 Biological Networks and Computational Challenges

The huge interest in complex networks across many research areas has also found application in biological studies, where associations between genes, proteins, and metabolites deserve further investigation particularly due to the underlying regulative or interactive dynamics. Here the proposed work addresses protein interactome networks (PIN) [1] from an integrative dynamic perspective, and aims to establish a better definition of their modular configurations.

There are currently reasons of concern in relation to the computational analysis of PIN, and they mainly refer to three problems. First, there is a limited interactome coverage [2] that depends on the organism under study [3] and on the available data-generating methodologies (yeast two-hybrid, co-IP, text mining and literature mining, DB curation, orthology, etc.). Consequently, data integration is often needed to ensure a better data uncertainty control and validation quality.

Second, there is also limited measurement accuracy as a limiting factor, and refers to the uncertainty inherent to both experimentally measured and predicted interactions (due to various sources of errors, biases, etc.). For example, evidence was recently provided [4] with regard to literature-curated interactome data about the necessity of careful quality control for reliable inference. Notably, scoring systems have been proposed to assign reliability to the interactions, thus ...