Common Module Research Articles

Identification of hub genes significantly linked to subarachnoid hemorrhage and epilepsy via bioinformatics analysis.

Although epilepsy has been linked to subarachnoid hemorrhage (SAH), the underlying mechanism has not been fully elucidated. This study aimed to further explore the potential mechanisms in epilepsy and SAH through genes. Gene expression profiles for subarachnoid hemorrhage (GSE36791) and epilepsy (GSE143272) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to identify the common differentially expressed genes (DEGs) to epilepsy and SAH, which were further analyzed by functional enrichment analysis. Single-sample gene set enrichment analysis (ssGSEA) and weighted correlation network analysis (WGCNA) were used to identify common module genes related to the infiltration of immune cells in epilepsy and SAH. Hub module genes were identified using a protein-protein interaction (PPI) network. Finally, the most relevant genes were obtained by taking the intersection points between the DEGs and hub module genes. We performed validation by retrospectively analyzing the RT-PCR levels of the most relevant genes in patients with pure SAH and patients with SAH complicated with epilepsy. Our experiments verified that the SAH and SAH+epilepsy groups were significantly different from the normal control group. In addition, significant differences were observed between the SAH and SAH+epilepsy groups. In total, 159 common DEGs-85 downregulated genes and 74 upregulated genes-were identified. Functional analysis emphasized that the immune response was a common feature to epilepsy and SAH. The results of ssGSEA and WGCNA revealed changes in immunocyte recruitment and the related module genes. Finally, MMP9 and C3aR1 were identified as hub genes, and RT-PCR confirmed that the expression levels of the hub genes were higher in epilepsy and SAH samples than in normal samples. Our study revealed the pathogenesis of SAH complicated with epilepsy and identified hub genes that might provide new ideas for further mechanistic studies.

Crystal Structure of the SH3 Domain of ASAP1 in Complex with the Proline Rich Motif (PRM) of MICAL1 Reveals a Unique SH3/PRM Interaction Mode

SH3 domains are common protein binding modules. The target sequence of SH3 domains is usually a proline-rich motif (PRM) containing a minimal "PxxP" sequence. The mechanism of how different SH3 domains specifically choose their targets from vast PxxP-containing sequences is still not very clear, as many reported SH3/PRM interactions are weak and promiscuous. Here, we identified the binding of the SH3 domain of ASAP1 to the PRM of MICAL1 with a sub-μM binding affinity, and determined the crystal structure of ASAP1-SH3 and MICAL1-PRM complex. Our structural and biochemical analyses revealed that the target-binding pocket of ASAP1-SH3 contains two negatively charged patches to recognize the "xPx + Px+" sequence in MICAL1-PRM and consequently strengthen the interaction, differing from the typical SH3/PRM interaction. This unique PRM-binding pocket is also found in the SH3 domains of GTPase Regulator associated with focal adhesion kinase (GRAF) and Src kinase associated phosphoprotein 1 (SKAP1), which we named SH3AGS. In addition, we searched the Swiss-Prot database and found ~130 proteins with the SH3AGS-binding PRM in silico. Finally, gene ontology analysis suggests that the strong interaction between the SH3AGS-containing proteins and their targets may play roles in actin cytoskeleton regulation and vesicle trafficking.

Identifying miRNA-Gene Common and Specific Regulatory Modules for Cancer Subtyping by a High-Order Graph Matching Model.

Identifying regulatory modules between miRNAs and genes is crucial in cancer research. It promotes a comprehensive understanding of the molecular mechanisms of cancer. The genomic data collected from subjects usually relate to different cancer statuses, such as different TNM Classifications of Malignant Tumors (TNM) or histological subtypes. Simple integrated analyses generally identify the core of the tumorigenesis (common modules) but miss the subtype-specific regulatory mechanisms (specific modules). In contrast, separate analyses can only report the differences and ignore important common modules. Therefore, there is an urgent need to develop a novel method to jointly analyze miRNA and gene data of different cancer statuses to identify common and specific modules. To that end, we developed a High-Order Graph Matching model to identify Common and Specific modules (HOGMCS) between miRNA and gene data of different cancer statuses. We first demonstrate the superiority of HOGMCS through a comparison with four state-of-the-art techniques using a set of simulated data. Then, we apply HOGMCS on stomach adenocarcinoma data with four TNM stages and two histological types, and breast invasive carcinoma data with four PAM50 subtypes. The experimental results demonstrate that HOGMCS can accurately extract common and subtype-specific miRNA-gene regulatory modules, where many identified miRNA-gene interactions have been confirmed in several public databases.

Sparse Independence Component Analysis for Competitive Endogenous RNA Co-Module Identification in Liver Hepatocellular Carcinoma.

Long non-coding RNAs (lncRNAs) have been shown to be associated with the pathogenesis of different kinds of diseases and play important roles in various biological processes. Although numerous lncRNAs have been found, the functions of most lncRNAs and physiological/pathological significance are still in its infancy. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood. In order to reveal functional lncRNAs and identify the key lncRNAs, we develop a new sparse independence component analysis (ICA) method to identify lncRNA-mRNA-miRNA expression co-modules based on the competitive endogenous RNA (ceRNA) theory using the sample-matched lncRNA, mRNA and miRNA expression profiles. The expression data of the three RNA combined together is approximated sparsely to obtain the corresponding sparsity coefficient, and then it is decomposed by using ICA constraint optimization to obtain the common basis and modules. Subsequently, affine propagation clustering is used to perform cluster analysis on the common basis under multiple running conditions to obtain the co-modules for the selection of different RNA elements. We applied sparse ICA to Liver Hepatocellular Carcinoma (LIHC) dataset and the experiment results demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules. It may provide insights into the function of lncRNAs and molecular mechanism of LIHC. Clinical and Translational Impact Statement-The results on LIHC dataset demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules, which may provide insights into the function of IncRNAs and molecular mechanism of LIHC.

Learners’ Differences in Blended Learner-Centric Approach for a Common Programming Subject

As the number of students entering higher education increases with a growing diversity of background, educators of programming courses face increasing challenges. Different teaching pedagogies need to be explored for students with different background knowledge. Some students find programming courses difficult to understand and practice. It may lead to de-motivation and disengagement in learning process with consequential impact on their grades. Addressing these issues demands approaches for effective teaching programming courses to multidisciplinary cohorts. This article investigates how computing science (CS) and engineering cohorts respond differently to teaching approaches in a common module, Fundamentals of Programming. Both traditional teacher-centric teaching and a blended learner-centric approach have been explored in a diverse group of students. The blended learner-centric approach combines classroom teaching and self-paced blended learning using work examples videos method. These two teaching approaches have been evaluated in Academic Year 2019/2020. It can be seen from the evaluation results of 92 CS and 150 Engineering students who participate in this research that the performance is improved by about 5% through blended learner-centric approach. It is further observed that quantitatively the performance gap between CS and Engineering students has been reduced. Questionnaire survey has also been conducted with 54 CS and 89 Engineering students being responded. The learners’ perceptions of the blended learner-centric approach have also been compared between these two cohorts.

Communication-Efficient Federated Multitask Learning Over Wireless Networks

This article investigates the scheduling framework of the federated multitask learning (FMTL) problem with a hard-cooperation structure over wireless networks, in which the scheduling becomes more challenging due to the different convergence behaviors of different tasks. Based on the special model structure, we propose a dynamic user and task scheduling scheme with a block-wise incremental gradient aggregation algorithm, in which the neural network model is decomposed into a common feature-extraction module and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> task-specific modules. Different block gradients with respect to different modules can be scheduled separately. We further propose a Lyapunov-drift-based scheduling scheme that minimizes the overall communication latency by utilizing both the instantaneous data importance and the channel state information. We prove that the proposed scheme can converge almost surely to a KKT solution of the training problem such that the data-distortion issue is resolved. Simulation results illustrate that the proposed scheme significantly reduces the communication latency compared to the state-of-the-art baseline schemes.

Impact of acetic acid exposure on metal contact degradation of different crystalline silicon solar cell technologies

Degradation due to acetic acid in photovoltaic (PV) modules has been a commonly observed phenomenon for both damp-heat exposure and outdoor operations. Acetic acid is a degradation byproduct of ethylene-vinyl acetate (EVA), a common module encapsulant. To address this issue, robust metallization pastes and cell technologies are being developed. However, it is important to assess how these technologies perform in an acetic acid environment and withstand degradation before they are implemented in the solar market. In this work, we investigate the impact of acetic acid exposure on four different cell groups: monofacial passivated emitter and rear contact (PERC) cells with advanced telluride-based front contact pastes, bifacial PERC cells with novel aluminum rear contact pastes, bifacial tunnel oxide passivated contacts (TOPCon) cells, and silicon heterojunction (SHJ) cells. These cells were exposed to acetic acid for different time increments. The recombination losses were characterized by Suns- V OC , and multi-variate regression analysis of intensity-dependent photoluminescence (PL) images with Griddler AI. Resistive losses were tracked with the transmission line method (TLM). Samples showing severe performance degradation were selected for further materials characterization to understand the root cause. Top-down and cross-sectional scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS) were performed to investigate the change in materials properties. Our study shows that the front contacts of the bifacial TOPCon cells and monofacial PERC cells were significantly affected by acetic acid exposure. The SHJ cells were found to be the most stable. • Effect of acetic acid on metallization pastes and cell technologies was investigated. • Front Ag-Al contacts for Bifacial TOPCon cells degrade the most due to acid exposure. • Monofacial PERC cells with Pb-free Te-O paste is the second group that is affected. • Bifacial PERC cells were stable with Al back contacts showing no signs degradation. • SHJ cells were the most stable group in this experiment.

Regulations of myo-inositol homeostasis: Mechanisms, implications, and perspectives.

Phosphorylation is the most common module of cellular signalling pathways. The dynamic nature of phosphorylation, which is conferred by the balancing acts of kinases and phosphatases, allows this modification to finely control crucial cellular events such as growth, differentiation, and cell cycle progression. Although most research to date has focussed on protein phosphorylation, non-protein phosphorylation substrates also play vital roles in signal transduction. The most well-established substrate of non-protein phosphorylation is inositol, whose phosphorylation generates many important signalling molecules such as the second messenger IP3, a key factor in calcium signalling. A fundamental question to our understanding of inositol phosphorylation is how the levels of cellular inositol are controlled. While the availability of protein phosphorylation substrates is known to be readily controlled at the levels of transcription, translation, and/or protein degradation, the regulatory mechanisms that control the uptake, synthesis, and removal of inositol are underexplored. Potentially, such mechanisms serve as an important layer of regulation of cellular signal transduction pathways. There are two ways in which mammalian cells acquire inositol. The historic use of radioactive 3H-myo-inositol revealed that inositol is promptly imported from the extracellular environment by three specific symporters SMIT1/2, and HMIT, coupling sodium or proton entry, respectively. Inositol can also be synthesized de novo from glucose-6P, thanks to the enzymatic activity of ISYNA1. Intriguingly, emerging evidence suggests that in mammalian cells, de novo myo-inositol synthesis occurs irrespective of inositol availability in the environment, prompting the question of whether the two sources of inositol go through independent metabolic pathways, thus serving distinct functions. Furthermore, the metabolic stability of myo-inositol, coupled with the uptake and endogenous synthesis, determines that there must be exit pathways to remove this extraordinary sugar from the cells to maintain its homeostasis. This essay aims to review our current knowledge of myo-inositol homeostatic metabolism, since they are critical to the signalling events played by its phosphorylated forms.

ReAdapt: A Reconfigurable Datapath for Runtime Energy-Quality Scalable Adaptive Filters

This paper proposes ReAdapt–a reconfigurable datapath architecture for scaling the energy-quality trade-off of adaptive filtering at runtime. The ReAdapt can dynamically select four adaptive filtering algorithms for gradating complexity levels during runtime by reconfiguring the processing flow in its datapath and by blocking the switching activity (e.g., reducing the CMOS dynamic power) of unused modules with data-gating. The ReAdapt proposal can scale the energy-quality trade-off by choosing the following four different levels of filter algorithms complexity: 1) least mean square (LMS); 2) partial update normalized LMS (PU-NLMS); 3) set-membership normalized LMS (SM-NLMS); 4) normalized LMS (NLMS). The ReAdapt architecture reuses common modules of each adaptive filter, resulting in a compact VLSI hardware implementation. The ReAdapt architecture operation is implemented in a case-study for interference mitigation for electroencephalogram (EEG) signal processing. The hardware synthesis results show an increase of 6.80 times in throughput and at least a reduction of 2.84 times in energy per operation compared with the state-of-the-art adaptive filters. This paper also investigates the benefits of dynamically reconfiguring the four ReAdapt operating modes at runtime for different levels of signal-to-noise ratio (SNR) for the processed signals. We also demonstrate that dynamically reconfiguring the ReAdapt operating modes during runtime results in an optimal energy-quality trade-off which is advantageous over the conventional single static mode.

Rice circadian clock regulator Nhd1 controls the expression of the sucrose transporter gene OsSUT1 and impacts carbon-nitrogen balance.

Interdependent metabolic and transport processes of carbon (C) and nitrogen (N) regulate plant growth and development, while the regulatory pathways remain poorly defined. We previously reported that rice circadian clock N-mediated heading date-1 (Nhd1) regulates growth duration-dependent N use efficiency. Here, we report that knockout of Nhd1 in rice reduced the rate of photosynthesis and the sucrose ratio of sheaths to blades, but increased the total C to N ratio and free amino acids. Leaf RNA-seq analysis indicated that mutation of Nhd1 dramatically altered expression of the genes linked to starch and sucrose metabolism, circadian rhythm, and amino acid metabolic pathways. We identified that Nhd1 can directly activate the transcriptional expression of sucrose transporter-1 (OsSUT1). Knockout of Nhd1 suppressed OsSUT1 expression, and both nhd1 and ossut1 mutants showed similar shorter height, and lower shoot biomass and sucrose concentration in comparison with the wild type, while overexpression of OsSUT1 can restore the defective sucrose transport and partially ameliorate the reduced growth of nhd1 mutants. The Nhd1-binding site of the OsSUT1 promoter is conserved in all known rice genomes. The positively related variation of Nhd1 and OsSUT1 expression among randomly selected indica and japonica varieties suggests a common regulatory module of Nhd1-OsSUT1-mediated C and N balance in rice.

Multi-spectral color vision fusion jointly with two-stream feature interaction and color transformation network

To utilize the complementary information and color vision fidelity in multispectral fusion task, a robust color vision fusion framework is proposed to provide a promising informative fusion result. Specifically, the proposed fusion framework is separated into two stages: the first image fusion stage and the second color transformation stage. The first stage is a two-stream feature interactional network via common and inherent features extraction modules, which obtains the suboptimal gray fused image retaining rich textural details and highlighting the thermal targets. After generating the suboptimal results, the second color transformation stage is implemented to transfer the color cues from the visible modal to the fused result, which ensures the fused results are consistent to natural visible light image in color appearance and keep a steady brightness and high fidelity. Moreover, the proposed colorization sub-network boosts the gray-domain fusion methods on preserving color information. The experimental results illustrate the superiority of the proposed method over state-of-the-art methods in both subjective and objective evaluation.

Attention mechanism combined with residual recurrent neural network for sound event detection and localization

In the task of sound event detection and localization (SEDL) in a complex environment, the acoustic signals of different events usually have nonlinear superposition, so the detection and localization effect is not good. Given this, this paper is based on the Residual-spatially and channel Squeeze-Excitation (Res-scSE) model. Combined with Multiple-scale Convolutional Recurrent Neural Network (M-CRNN), the Res-scSE-CRNN model is proposed. Firstly, to solve the problem of insufficient extraction of time-frequency feature in single-size convolution kernel, multi-scale feature fusion is carried out by using the feature hierarchy of the convolutional neural network to improve the accuracy of detection. Secondly, aiming at the problem of overlapping audio event localization accuracy is not high, with Res-scSE to replace common convolution module and add residual structure to strengthen the feature extraction, and combining with an attention mechanism to enhance neural network channels and spatial relationships, to improve the network to extract the characteristics of directivity, achieve the goal of the overlapped audio localization. In this paper, experiments are carried out in the open dataset DCASE2019, and evaluation indicators are used to analyze the effectiveness of the proposed model and baseline model in the detection and localization of audio events. The results show that compared with the M-CRNN model, the detection error rate of Res-scSE-CRNN model is reduced 4%, the F1-Score is increased 3.4%, the localization error is reduced by 22.8°, and the frame recall rate is increased 3%.

A Multimodal Fusion Model for Estimating Human Hand Force: Comparing surface electromyography and ultrasound signals

Biomimetic robots have received significant attention in recent years. Among them, the wearable exoskeleton, which imitates the functions of the musculoskeletal system to assist humans, is a typical biomimetic robot. Given that safe human–robot interaction plays a critical role in the successful application of wearable exoskeletons, this work studies the clinical readiness of a multimodal fusion model that estimates hand force based on the surface electromyography (sEMG) and A-mode ultrasound signals of the forearm muscles. The proposed multimodal fusion model affords the biomimetic hand exoskeleton assisting the elderly in completing daily tasks or quantitatively assessing the recovery level of poststroke patients. The suggested fusion model is called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Optimization of Latent Representation for the Self-Attention Convolutional Neural Network</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLR-SACNN</i> ), which utilizes a common component extraction module (CCEM) and a complementary component retention module (CCRM) to optimize latent representation of the multiple modalities. Then the optimized latent representations are fused with the self-attention mechanism. The experiments conducted on a self-collected multimodal data set verify performance of the proposed OLR-SACNN model. Specifically, compared to solely employing sEMG or A-mode ultrasound signals, the force estimation’s normalized mean-square error (NMSE) based on the multiple modalities decreases by 97.7 and 38.92%, respectively. Furthermore, the OLR-SACNN model has been used to estimate the hand force of some poststroke patients and attained the desired performance.

Multi‐tissue profiling of RNA and DNA‐methylation signatures of progressive tau neuropathology in tau transgenic mice

AbstractBackgroundIntracellular abnormal aggregation of hyperphosphorylated tau protein is a neuropathological hallmark associated with Alzheimer’s Disease (AD). The insoluble tau aggregates called neurofibrillary tangles lead to microtubule dysfunction and neurodegeneration with AD progression. The TG4510 transgenic mice overexpressing the P301L mutant human tau protein exhibit region‐specific tau inclusions, brain atrophy, and behavioral deficits, thereby serving as a widely used animal model of tauopathy in AD. This study aims to characterize the tissue‐specific transcriptomic and epigenomic signatures of progressive tau pathology in TG4510 mice as well as investigate the correlation of brain and blood expression and epigenomic changes.MethodUsing UMI‐based bulk RNA‐Sequencing and Reduced‐representation Bisulfite Sequencing (RRBS), we profiled the gene expression and DNA‐methylation changes respectively, in the cortex, hippocampus, cerebellum and pre‐sacrifice and terminal whole blood between three age groups (2‐3 months, 6‐7 months, 8‐9 months old; N=20/tissue/group) of male and female TG4510 mice.ResultThe cortex and hippocampus show the largest transcriptional differences between mice, recapitulating phenotype differences with loss of neurotransmission and increased inflammation in these tissues. Systems‐level co‐expression analysis reveals common gene modules in immune system, behaviour and cognition and ECM/synaptic pathways between cortex/hippocampus and blood. Furthermore, the genes correlated between brain regions and blood in these mice were prioritized as potential biomarkers of tau pathology, which can be further validated in preclinical animal models and ultimately human studies.ConclusionThis study serves as one of the first evidence of multiomic molecular underpinnings in a tauopathy mouse model providing potential novel targets and biomarkers of progressive tau pathology in AD.DisclosuresAll authors are employees of AbbVie. The design, study conduct, and financial support for this research were provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the publication.

BHLH heterodimer complex variations regulate cell proliferation activity in the meristems of Arabidopsis thaliana

Root, shoot, and lateral meristems are the main regions of cell proliferation in plants. It has been proposed that meristems might have evolved dedicated transcriptional networks to balance cell proliferation. Here, we show that basic helix-loop-helix (bHLH) transcription factor heterodimers formed by members of the TARGET OF MONOPTEROS5 (TMO5) and LONESOME HIGHWAY (LHW) subclades are general regulators of cell proliferation in all meristems. Yet, genetics and expression analyses suggest specific functions of these transcription factors in distinct meristems, possibly due to their expression domains determining heterodimer complex variations within meristems, and to a certain extent to the absence of some of them in a given meristem. Target gene specificity analysis for heterodimer complexes focusing on the LONELY GUY gene targets further suggests differences in transcriptional responses through heterodimer diversification that could allow a common bHLH heterodimer complex module to contribute to cell proliferation control in multiple meristems.

Bacteria-Derived Peptidoglycan Triggers a Noncanonical Nuclear Factor-κB-Dependent Response in Drosophila Gustatory Neurons.

Probing the external world is essential for eukaryotes to distinguish beneficial from pathogenic micro-organisms. If it is clear that the main part of this task falls to the immune cells, recent work shows that neurons can also detect microbes, although the molecules and mechanisms involved are less characterized. In Drosophila, detection of bacteria-derived peptidoglycan by pattern recognition receptors of the peptidoglycan recognition protein (PGRP) family expressed in immune cells triggers nuclear factor-κB (NF-κB)/immune deficiency (IMD)-dependent signaling. We show here that one PGRP protein, called PGRP-LB, is expressed in bitter gustatory neurons of proboscises. In vivo calcium imaging in female flies reveals that the PGRP/IMD pathway is cell-autonomously required in these neurons to transduce the peptidoglycan signal. We finally show that NF-κB/IMD pathway activation in bitter-sensing gustatory neurons influences fly behavior. This demonstrates that a major immune response elicitor and signaling module are required in the peripheral nervous system to sense the presence of bacteria in the environment.SIGNIFICANCE STATEMENT In addition to the classical immune response, eukaryotes rely on neuronally controlled mechanisms to detect microbes and engage in adapted behaviors. However, the mechanisms of microbe detection by the nervous system are poorly understood. Using genetic analysis and calcium imaging, we demonstrate here that bacteria-derived peptidoglycan can activate bitter gustatory neurons. We further show that this response is mediated by the PGRP-LC membrane receptor and downstream components of a noncanonical NF-κB signaling cascade. Activation of this signaling cascade triggers behavior changes. These data demonstrate that bitter-sensing neurons and immune cells share a common detection and signaling module to either trigger the production of antibacterial effectors or to modulate the behavior of flies that are in contact with bacteria. Because peptidoglycan detection doesn't mobilize the known gustatory receptors, it also demonstrates that taste perception is much more complex than anticipated.

Configuring products using NSGA-II: warranty profits, performance–price, and environmental emissions from the contractor perspective

In the context of mass customization business activities, product configuration (PC) can assist companies in quickly occupying the market and improving their competitive advantage. In this paper, contractors are defined as outsourcing companies for producers and manufacturers that do not possess self-developed product module technology and who are more knowledgeable and discerning consumer representatives. This study focused on a PC approach that includes optional, redundant, and common modules. In this approach, three objective functions were considered and solved using NSGA-II: warranty profit, performance-price, and environmental emissions. Warranty profit is calculated by forcing the product failure probability to satisfy a power-law distribution and the user selection behavior to satisfy a multinomial logit (MNL) model. Kano’s model-based user satisfaction and the relative importance of the product module instance are used to calculate performance-price. The environmental emissions were determined from both production and assembly emissions of the module instance. Additionally, a chromosome encoding method and a chromosome crossover operation were designed for the PC problem. Finally, the feasibility and effectiveness of the proposed method were verified by taking an electric vehicle DC charging pile configuration project as an example, and discussing and explaining the practical significance of each parameter in the example.

Identify Multiple Gene-Drug Common Modules via Constrained Graph Matching.

Identifying gene-drug interactions is vital to understanding biological mechanisms and achieving precise drug repurposing. High-throughput technologies produce a large amount of pharmacological and genomic data, providing an opportunity to explore the associations between oncogenic genes and therapeutic drugs. However, most studies only focus on "one-to-one" or "one-to-many" interactions, ignoring the multivariate patterns between genes and drugs. In this article, a high-order graph matching model with hypergraph constraints is proposed to discover the gene-drug common regulatory modules. Moreover, the prior knowledge is formulated into hypergraph constraints to reveal their multiple correspondences, penalizing the tensor matching process. The experimental results on the synthetic data demonstrate the proposed model is robust to noise contamination and outlier corruption, achieving a better performance than four state-of-the-art methods. We then evaluate the statistical power of our proposed method on the pharmacogenomics data. Our identified gene-drug common modules not only show significantly enriched pathways associated with cancer but also manifest the highly close gene-drug interactions.

234.10: Using a Unique Repository of Pancreas Transplant Biopsies To Validate the Tissue Common Response Module (tCRM) Score as a Tool to Assess the Severity of Pancreas Transplant Rejection

234.10: Using a Unique Repository of Pancreas Transplant Biopsies To Validate the Tissue Common Response Module (tCRM) Score as a Tool to Assess the Severity of Pancreas Transplant Rejection

Clustering of Cancer Attributed Networks by Dynamically and Jointly Factorizing Multi-Layer Graphs.

The accumulated omic data provides an opportunity to exploit the mechanisms of cancers and poses a challenge for their integrative analysis. Although extensive efforts have been devoted to address this issue, the current algorithms result in undesirable performance because of the complexity of patterns and heterogeneity of data. In this study, the ultimate goal is to propose an effective and efficient algorithm (called NMF-DEC) to identify clusters by integrating the interactome and transcriptome data. By treating the expression profiles of genes as attributes of vertices in the gene interaction networks, we transform the integrative analysis of omic data into clustering of attributed networks. To circumvent the heterogeneity, we construct a similarity network for the attributes of genes and cast it into the common module detection problem in multi-layer networks. The NMF-DEC explores the relation between attributes and topological structure of networks by jointly factorizing the similarity and interaction networks with the same basis. In this optimization, the interaction network is dynamically updated and the information of attributes is dynamically incorporated, providing a better strategy to characterize the structure of modules in attributed networks. Extensive experiments indicate that compared with state-of-the-art baselines, NMF-DEC is more accurate on social network, and show better performance on cancer attributed networks, implying the superiority of the proposed methods for the integrative analysis of omic data.