The predicted age difference (PAD) between an individual's predicted brain age and chronological age has been commonly viewed as a meaningful phenotype relating to aging and brain diseases. However, the systematic bias appears in the PAD achieved using machine learning methods. Recent studies have designed diverse bias correction methods to eliminate it for further downstream studies. Strikingly, here we demonstrate that bias still exists in the PAD of samples with the same age even after kind of correction. Therefore, current PAD may not be taken as a reliable phenotype and more investigations are needed to solve this fundamental defect. To this end, we propose an age-level bias correction method and demonstrate its efficacy in numerical experiments.

Confounding factors exist widely in various biological data owing to technical variations, population structures and experimental conditions. Such factors may mask the true signals and lead to spurious associations in the respective biological data, making it necessary to adjust confounding factors accordingly. However, existing confounder correction methods were mainly developed based on the original data or the pairwise Euclidean distance, either one of which is inadequate for analyzing different types of data, such as sequencing data. In this work, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, which reduces data dimension and extracts the information from different distance measures using principal coordinate analysis, and adjusts confounding factors across multiple datasets by minimizing the associations between lower-dimensional representations and confounding variables. Application of the proposed method was further extended to classification and prediction. We demonstrated the efficacy of AC-PCoA on three simulated datasets and five real datasets. Compared to the existing methods, AC-PCoA shows better results in visualization, statistical testing, clustering, and classification.

Community (module) structure is a common and important property of many types of networks, such as social networks and biological networks. Several classes of algorithms have been proposed for community structure detection and identification, including clustering techniques, modularity optimization, and other methods. Among these methods, the modularity optimization method has attracted a great deal of attention and much related research has been published. However, the existing modularity optimization method does not perform well in the presence of unbalanced community structures. In this paper, we introduce a metric to characterize the community structure better than other metrics in this situation, and we propose a method to infer the number of communities, which may solve the resolution limit problem. We then develop an algorithm for community structure identification based on eigendecompositions, and we give both simulated and real data examples to illustrate the better performance of our approach.

Re-construction of a genetic regulatory network from a given time-series gene expression data is an important research topic in systems biology. One of the main difficulties in building a genetic regulatory network lies in the fact that practical data set has a huge number of genes vs. a small number of sampling time points. In this paper, we propose a new linear regression model that may overcome this difficulty for uncovering the regulatory relationship in a genetic network.

Primary thickening determines bamboo yield and wood property. However, little is known about the regulatory networks involved in this process. This study identified a total of 58,652 genes and 150 miRNAs via transcriptome and small RNA sequencing using the underground thickening shoot samples of wild-type (WT) Moso bamboo (Phyllostachys edulis) and a thick wall (TW) variant (P. edulis “Pachyloen”) at five developmental stages (WTS1/TWS1-WTS5/TWS5). A total of 14,029 (65.17%) differentially expressed genes and 68 (45.33%) differentially expressed miRNAs were identified from the WT, TW, and WTTW groups. The first two groups were composed of four pairwise combinations, each between two successive stages (WTS2/TWS2_versus_WTS1/TWS1, WTS3/TWS3_versus_WTS2/TWS2, WTS4/TWS4_versus_WTS3/TWS3, and WTS5/TWS5_versus_WTS4/TWS4), and the WTTW group was composed of five combinations, each between two relative stages (TWS1–5_versus_WTS1–5). Additionally, among the phytohormones, zeatin showed more remarkable changes in concentrations than indole-3-acetic acid, gibberellic acid, and abscisic acid throughout the five stages in the WT and the TW groups. Moreover, 125 cleavage sites were identified for 387 miRNA–mRNA pairs via degradome sequencing (P < 0.05). The dual-luciferase reporter assay confirmed that 13 miRNAs bound to 12 targets. Fluorescence in situ hybridization localized miR166 and miR160 in the shoot apical meristem and the procambium of Moso bamboo shoots at the S1 stage. Thus, primary thickening is a complex process regulated by miRNA–gene–phytohormone networks, and the miRNAome and transcriptome dynamics regulate phenotypic plasticity. These findings provide insights into the molecular mechanisms underlying wood formation and properties and propose targets for bamboo breeding.

Network module (community) structure has been a hot research topic in recent years. Many methods have been proposed for module detection and identification. Hierarchical structure of modules is shown to exist in many networks such as biological networks and social networks. Compared to the partitional module identification methods, less research is done on the inference of hierarchical modular structure. In this paper, we propose a method for constructing the hierarchical modular structure based on the stochastic block model. Statistical tests are applied to test the hierarchical relations between different modules. We give both artificial networks and real data examples to illustrate the performance of our approach. Application of the proposed method to yeast gene coexpression network shows that it does have a hierarchical modular structure with the modules on different levels corresponding to different gene functions.