GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks

Recently, Graph Neural Networks (GNNs) have significantly advanced the performance of machine learning tasks on graphs. However, this technological breakthrough makes people wonder: how does a GNN make such decisions, and can we trust its prediction with high confidence? When it comes to some critical fields, such as biomedicine, where making wrong decisions can have severe consequences, it is crucial to interpret the inner working mechanisms of GNNs before applying them. In this paper, we propose a model-agnostic model-level explanation method for different GNNs that follow the message passing scheme, GNNInterpreter, to explain the high-level decision-making process of the GNN model. More specifically, GNNInterpreter learns a probabilistic generative graph distribution that produces the most discriminative graph pattern the GNN tries to detect when making a certain prediction by optimizing a novel objective function specifically designed for the model-level explanation for GNNs. Compared with the existing work, GNNInterpreter is more computationally efficient and more flexible in generating explanation graphs with different types of node features and edge features, without introducing another blackbox to explain the GNN and without requiring manually specified domain-specific knowledge. Additionally, the experimental studies conducted on four different datasets demonstrate that the explanation graph generated by GNNInterpreter can match the desired graph pattern when the model is ideal and reveal potential model pitfalls if there exist any

Mutual funds, tunneling and firm performance:evidence from China

© 2019, The Author(s). In contrast to US companies, Chinese firms have concentrated ownership with the effect that the central agency problem emanates from controlling shareholders expropriating minority shareholders, a phenomenon referred to as ‘tunneling’. This study examines the monitoring effect of mutual funds on the tunneling behavior of controlling shareholders. Due to the distinctive institutional settings in China, including a high level of ownership concentration, underdeveloped legal system in the stock markets and weak governance mechanisms in the mutual fund industry, we find that an increase in mutual fund ownership effectively mitigates the tunneling behavior of controlling shareholders thus improving firm performance. Nonetheless, after the mutual fund ownership reaches a certain threshold, an increase in concentrated mutual fund ownership is associated with heavier tunneling and lower firm performance. This may suggest that concentrated mutual funds collude with controlling shareholders in order to preserve their private interests. Moreover, the above effects are found to be more pronounced for firms with heavier tunneling activities. Our finding of the non-monotonic monitoring role of mutual funds brings attention to the private interest theory for mutual funds, an aspect that has been largely ignored in previous studies on mutual funds

Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme

Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes

Advances in the Relationships Between Cow’s Milk Protein Allergy and Gut Microbiota in Infants

Cow’s milk protein allergy (CMPA) is an immune response to cow’s milk proteins, which is one of the most common food allergies in infants and young children. It is estimated that 2–3% of infants and young children have CMPA. The diet, gut microbiota, and their interactions are believed to be involved in the alterations of mucosal immune tolerance, which might lead to the development of CMPA and other food allergies. In this review, the potential molecular mechanisms of CMPA, including omics technologies used for analyzing microbiota, impacts of early microbial exposures on CMPA development, and microbiota–host interactions, are summarized. The probiotics, prebiotics, synbiotics, fecal microbiota transplantation, and other modulation strategies for gut microbiota and the potential application of microbiota-based design of diets for the CMPA treatment are also discussed. This review not only summarizes the current studies about the interactions of CMPA with gut microbiota but also gives insights into the possible CMPA treatment strategies by modulating gut microbiota, which might help in improving the life quality of CMPA patients in the future

TF-Cluster: A pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM)

<p>Abstract</p> <p>Background</p> <p>Identifying the key transcription factors (TFs) controlling a biological process is the first step toward a better understanding of underpinning regulatory mechanisms. However, due to the involvement of a large number of genes and complex interactions in gene regulatory networks, identifying TFs involved in a biological process remains particularly difficult. The challenges include: (1) Most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation, making it difficult to recognize TFs for a biological process; (2) Transcription usually involves several hundred genes that generate a combination of intrinsic noise from upstream signaling networks and lead to fluctuations in transcription; (3) A TF can function in different cell types or developmental stages. Currently, the methods available for identifying TFs involved in biological processes are still very scarce, and the development of novel, more powerful methods is desperately needed.</p> <p>Results</p> <p>We developed a computational pipeline called TF-Cluster for identifying functionally coordinated TFs in two steps: (1) Construction of a shared coexpression connectivity matrix (SCCM), in which each entry represents the number of shared coexpressed genes between two TFs. This sparse and symmetric matrix embodies a new concept of coexpression networks in which genes are associated in the context of other shared coexpressed genes; (2) Decomposition of the SCCM using a novel heuristic algorithm termed "Triple-Link", which searches the highest connectivity in the SCCM, and then uses two connected TF as a primer for growing a TF cluster with a number of linking criteria. We applied TF-Cluster to microarray data from human stem cells and <it>Arabidopsis </it>roots, and then demonstrated that many of the resulting TF clusters contain functionally coordinated TFs that, based on existing literature, accurately represent a biological process of interest.</p> <p>Conclusions</p> <p>TF-Cluster can be used to identify a set of TFs controlling a biological process of interest from gene expression data. Its high accuracy in recognizing true positive TFs involved in a biological process makes it extremely valuable in building core GRNs controlling a biological process. The pipeline implemented in Perl can be installed in various platforms.</p

Ameliorative Effect and Underlying Mechanisms of Total Triterpenoids from Psidium guajava Linn (Myrtaceae) Leaf on High-Fat Streptozotocin-induced Diabetic Peripheral Neuropathy in Rats

Purpose: To investigate whether the total triterpenoids extracted from Psidium Guajava leaves (TTPGL) attenuate the development of diabetic peripheral neuropathy in rats by regulating the NF-κB pathway of the inflammatory process and its signaling mediators.Methods: All the Sprague Dawley rats used were maintained in a clean environment on a 12 h light/12h dark cycle. High-fat feeding and intraperitoneal injection of 40 mg/kg streptozotocin (STZ) were used to induce diabetes in the rats. The rats were randomly divided into 5 groups: diabetic mellitus (DM) group; TTPGL - 30 group, TTPGL - 60 group and TTPGL - 120 group treated by intragastric administration (i.g) with 30, 100 and 120 mg/kg/day TTPGL, respectively. The well-established drug, rosiglitazone (RSG, 3 mg/k/d, i.g.), was used as positive control. Normal rats served as control group. Nerve conduction velocity and sensitive tests were measured on weeks 1, 4 and 8. After 8 weeks administration, expression of pro-inflammatory molecules (TNF - α, IL - 6 and iNOS) and tissue proteins (Akt, IKKα, and NF – κB - p65) were evaluated to assess biochemical changes.Results: Compared to DM group, TTPGL (especially 120 mg / kg dose) treatment improved (p &lt; 0.05) physical functions and provided neuronal protection in high - fat/streptozotocin - induced peripheral neuropathy rats. We found that the expressions of several pro - inflammatory factors such as tumor necrosis factor - α (TNF - α), IL - 6 and inducible nitric oxide synthase (iNOS) were highly suppressed (p &lt; 0.05 or p &lt; 0.01) by TTPGL in sciatic nerve. Mechanism analysis indicated that the ameliorative effect of TTPGL, in part, is through suppression of the expression of pro - inflammatory cytokines by NF - κB pathway mediation.Conclusion: TTPGL offers a potential therapeutic approach for the treatment of diabetic peripheral neuropathy.Keywords: Triterpenoids, Psidium Guajava, Diabetic peripheral neuropathy, Pro inflammatory cytokines, NF-κB pathwa

Effect of Cholesterol on C99 Dimerization: Revealed by Molecular Dynamics Simulations

C99 is the immediate precursor for amyloid beta (Aβ) and therefore is a central intermediate in the pathway that is believed to result in Alzheimer’s disease (AD). It has been suggested that cholesterol is associated with C99, but the dynamic details of how cholesterol affects C99 assembly and the Aβ formation remain unclear. To investigate this question, we employed coarse-grained and all-atom molecular dynamics simulations to study the effect of cholesterol and membrane composition on C99 dimerization. We found that although the existence of cholesterol delays C99 dimerization, there is no direct competition between C99 dimerization and cholesterol association. In contrast, the existence of cholesterol makes the C99 dimer more stable, which presents a cholesterol binding C99 dimer model. Cholesterol and membrane composition change the dimerization rate and conformation distribution of C99, which will subsequently influence the production of Aβ. Our results provide insights into the potential influence of the physiological environment on the C99 dimerization, which will help us understand Aβ formation and AD’s etiology