Statistical Identification of Important Nodes in Biological Systems

Abstract

Biological systems can be modeled and described by biological networks. Biological networks are typical complex networks with widely real-world applications. Many problems arising in biological systems can be boiled down to the identification of important nodes. For example, biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants. To facilitate the identification of important nodes in biological systems, one needs to know network structures or behavioral data of nodes (such as gene expression data). If network topology was known, various centrality measures can be developed to solve the problem; while if only behavioral data of nodes were given, some sophisticated statistical methods can be employed. This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects, that is, 1) in general complex networks based on complex networks theory and epidemic dynamic models; 2) in biological networks based on network motifs; and 3) in plants based on RNA-seq data. The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes, such mapping is not necessarily with explicit form. The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework. This paper also proposed some challenges and future works on the related topics. The associated investigations have potential real-world applications in the control of biological systems, network medicine and new variety cultivation of crops.

Recent Advances on Penalized Regression Models for Biological Data

“…RNA-seq data, as an example, can be explored at different levels, including sample, gene, transcript and exon levels [6,12]. For gene-level analysis, RNA-seq technologies enable us to quantify genome-wide gene expression patterns and perform differential expression analysis.…”

“…In RNA-seq data, based on the observed data X and y, differentially expressed genes (DEGs) can be determined via log 2 (FC) and statistical hypothesis test [12]. The log 2 (FC) quantifies the relative expression difference of a gene under treatments and controls.…”

“…Based on the soft-threshold function (12), researchers have developed a coordinate-wise descent algorithm [70] to iteratively obtain the estimation of each parameter, and the iteration stops when it converges.…”

“…); researchers can also determine a gene's transcriptional structure and quantify the expression of each transcript under given experimental conditions [1]. There are many challenges in exploring RNA-seq data, such as efficient methods to store, retrieve and process massive data, sequence assembly, sequence mapping and alignment, identifying novel splicing events, data-driven network inference, crucial gene identification and so on [1,6,[8][9][10][11][12]. Among these issues, the statistical exploration of sample clustering or classification and crucial gene selection are intriguing yet important.…”

“…Except for sample clustering or classification, gene identification is one of the most important issues in exploring biological data. For functionally crucial gene identification from biological data, there are generally two frequently used approaches [12]. The first approach relies on the topological features of the complex interaction networks or co-expression networks among biomolecules [18].…”