Contrastive Graph Pooling for Explainable Classification of Brain Networks.

Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying neurodegenerative conditions such as Parkinson's, Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a graph and extracts features by graph neural networks (GNNs). However, the unique characteristics of fMRI data require a special design of GNN. Tailoring GNN to generate effective and domain-explainable features remains challenging. In this paper, we propose a contrastive dual-attention block and a differentiable graph pooling method called ContrastPool to better utilize GNN for brain networks, meeting fMRI-specific requirements. We apply our method to 5 resting-state fMRI brain network datasets of 3 diseases and demonstrate its superiority over state-of-the-art baselines. Our case study confirms that the patterns extracted by our method match the domain knowledge in neuroscience literature, and disclose direct and interesting insights. Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions. The source code is available at https://github.com/AngusMonroe/ContrastPool.

Adaptive Transfer Kernel Learning for Transfer Gaussian Process Regression.

Transfer regression is a practical and challenging problem with important applications in various domains, such as engineering design and localization. Capturing the relatedness of different domains is the key of adaptive knowledge transfer. In this paper, we investigate an effective way of explicitly modelling domain relatedness through transfer kernel, a transfer-specified kernel that considers domain information in the covariance calculation. Specifically, we first give the formal definition of transfer kernel, and introduce three basic general forms that well cover existing related works. To cope with the limitations of the basic forms in handling complex real-world data, we further propose two advanced forms. Corresponding instantiations of the two forms are developed, namely Trkαß and Trkω based on multiple kernel learning and neural networks, respectively. For each instantiation, we present a condition with which the positive semi-definiteness is guaranteed and a semantic meaning is interpreted to the learned domain relatedness. Moreover, the condition can be easily used in the learning of TrGP αß and TrGP ω that are the Gaussian process models with the transfer kernels Trkαß and Trkω respectively. Extensive empirical studies show the effectiveness of TrGP αß and TrGP ω on domain relatedness modelling and transfer adaptiveness.

m6A demethylase FTO regulates the apoptosis and inflammation of cardiomyocytes via YAP1 in ischemia-reperfusion injury.

Reperfusion therapy after acute myocardial infarction can induce myocardial ischemia-reperfusion injury (IRI). Novel evidence has illustrated that N6-methyladenosine (m6A) modification modulates the myocardial IRI progression. Here, our study focuses on the role of m6A methyltransferase fat mass and obesity-associated protein (FTO) in myocardial ischemia/reoxygenation injury and explores potential regulatory mechanisms. Results discovered that FTO down-expressed in myocardial IRI mice and hypoxia/reoxygenation (H/R)-induced cardiomyocytes. Functionally, FTO overexpression attenuated the H/R-induced apoptosis and inflammation of cardiomyocytes. Mechanistically, methylated RNA immunoprecipitation quantitative polymerase chain reaction (MeRIP-qPCR) assay and RIP assay revealed that Yap1 mRNA acted as the target of FTO in cardiomyocytes. Moreover, FTO uninstalled the methylation of Yap1 mRNA, and enforced the stability of Yap1 mRNA. Taken together, our study reveals the role of FTO in H/R-induced myocardial cell injury via m6A-dependent manner, which may provide a new approach to improve myocardial IRI.

Subdomain Adaptation With Manifolds Discrepancy Alignment.

Reducing domain divergence is a key step in transfer learning. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this article, we take the local divergence of subdomains into account in transfer. Specifically, we propose to use the low-dimensional manifold to represent the subdomain, and align the local data distribution discrepancy in each manifold across domains. A manifold maximum mean discrepancy (M3D) is developed to measure the local distribution discrepancy in each manifold. We then propose a general framework, called transfer with manifolds discrepancy alignment (TMDA), to couple the discovery of data manifolds with the minimization of M3D. We instantiate TMDA in the subspace learning case considering both the linear and nonlinear mappings. We also instantiate TMDA in the deep learning framework. Experimental studies show that TMDA is a promising method for various transfer learning tasks.

Practical Multisource Transfer Regression With Source-Target Similarity Captures.

A key challenge in many applications of multisource transfer learning is to explicitly capture the diverse source-target similarities. In this article, we are concerned with stretching the set of practical approaches based on Gaussian process (GP) models to solve multisource transfer regression problems. Precisely, we first investigate the feasibility and performance of a family of transfer covariance functions that represent the pairwise similarity of each source and the target domain. We theoretically show that using such a transfer covariance function for general GP modeling can only capture the same similarity coefficient for all the sources, and thus may result in unsatisfactory transfer performance. This outcome, together with the scalability issues of a single GP based approach, leads us to propose TCMSStack , an integrated framework incorporating a separate transfer covariance function for each source and stacking. Contrary to typical stacking approaches, TCMSStack learns the source-target similarity in each base GP model by considering the dependencies of the other sources along the process. We introduce two instances of the proposed TCMSStack . Extensive experiments on one synthetic and two real-world data sets, with learning settings up to 11 sources for the latter, demonstrate the effectiveness of our approach.

Feature Analysis of Marginalized Stacked Denoising Autoenconder for Unsupervised Domain Adaptation.

Marginalized stacked denoising autoencoder (mSDA), has recently emerged with demonstrated effectiveness in domain adaptation. In this paper, we investigate the rationale for why mSDA benefits domain adaptation tasks from the perspective of adaptive regularization. Our investigations focus on two types of feature corruption noise: Gaussian noise (mSDA g ) and Bernoulli dropout noise (mSDA bd ). Both theoretical and empirical results demonstrate that mSDA bd successfully boosts the adaptation performance but mSDA g fails to do so. We then propose a new mSDA with data-dependent multinomial dropout noise (mSDA md ) that overcomes the limitations of mSDA bd and further improves the adaptation performance. Our mSDA md is based on a more realistic assumption: different features are correlated and, thus, should be corrupted with different probabilities. Experimental results demonstrate the superiority of mSDA md to mSDA bd on the adaptation performance and the convergence speed. Finally, we propose a deep transferable feature coding (DTFC) framework for unsupervised domain adaptation. The motivation of DTFC is that mSDA fails to consider the distribution discrepancy across different domains in the feature learning process. We introduce a new element to mSDA: domain divergence minimization by maximum mean discrepancy. This element is essential for domain adaptation as it ensures the extracted deep features to have a small distribution discrepancy. The effectiveness of DTFC is verified by extensive experiments on three benchmark data sets for both Bernoulli dropout noise and multinomial dropout noise.