Heterogeneous network embedding enabling accurate disease association predictions.

BackgroundIt is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation.ResultsWe incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset.ConclusionsWe propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation

RDGSL: Dynamic Graph Representation Learning with Structure Learning

Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can significantly degrade the quality of representation generation, impeding the effectiveness of TGNs in downstream tasks. Though structure learning is widely applied to mitigate noise in static graphs, its adaptation to dynamic graph settings poses two significant challenges. i) Noise dynamics. Existing structure learning methods are ill-equipped to address the temporal aspect of noise, hampering their effectiveness in such dynamic and ever-changing noise patterns. ii) More severe noise. Noise may be introduced along with multiple interactions between two nodes, leading to the re-pollution of these nodes and consequently causing more severe noise compared to static graphs. In this paper, we present RDGSL, a representation learning method in continuous-time dynamic graphs. Meanwhile, we propose dynamic graph structure learning, a novel supervisory signal that empowers RDGSL with the ability to effectively combat noise in dynamic graphs. To address the noise dynamics issue, we introduce the Dynamic Graph Filter, where we innovatively propose a dynamic noise function that dynamically captures both current and historical noise, enabling us to assess the temporal aspect of noise and generate a denoised graph. We further propose the Temporal Embedding Learner to tackle the challenge of more severe noise, which utilizes an attention mechanism to selectively turn a blind eye to noisy edges and hence focus on normal edges, enhancing the expressiveness for representation generation that remains resilient to noise. Our method demonstrates robustness towards downstream tasks, resulting in up to 5.1% absolute AUC improvement in evolving classification versus the second-best baseline

TIGER: Temporal Interaction Graph Embedding with Restarts

Temporal interaction graphs (TIGs), consisting of sequences of timestamped interaction events, are prevalent in fields like e-commerce and social networks. To better learn dynamic node embeddings that vary over time, researchers have proposed a series of temporal graph neural networks for TIGs. However, due to the entangled temporal and structural dependencies, existing methods have to process the sequence of events chronologically and consecutively to ensure node representations are up-to-date. This prevents existing models from parallelization and reduces their flexibility in industrial applications. To tackle the above challenge, in this paper, we propose TIGER, a TIG embedding model that can restart at any timestamp. We introduce a restarter module that generates surrogate representations acting as the warm initialization of node representations. By restarting from multiple timestamps simultaneously, we divide the sequence into multiple chunks and naturally enable the parallelization of the model. Moreover, in contrast to previous models that utilize a single memory unit, we introduce a dual memory module to better exploit neighborhood information and alleviate the staleness problem. Extensive experiments on four public datasets and one industrial dataset are conducted, and the results verify both the effectiveness and the efficiency of our work.Comment: WWW 2023. Codes: https://github.com/yzhang1918/www2023tige

Risk factors for in-hospital mortality after total arch procedure in patients with acute type A aortic dissection

ObjectKnowledge about the risk factors of in-hospital mortality for acute type A aortic dissection (ATAAD) patients who received total arch procedure is limited. This study aims to investigate preoperative and intraoperative risk factors of in-hospital mortality of these patients.MethodsFrom May 2014 to June 2018, 372 ATAAD patients received the total arch procedure in our institution. These patients were divided into survival and death groups, and patients` in-hospital data were retrospectively collected. Receiver operating characteristic curve analysis was adopted to determine the optimal cut-off value of continuous variables. Univariate and multivariable logistic regression analyses were used to detect independent risk factors for in-hospital mortality.ResultsA total of 321 patients were included in the survival group and 51 in the death group. Preoperative details showed that patients in the death group were older (55.4 ± 11.7 vs. 49.3 ± 12.6, P = 0.001), had more renal dysfunction (29.4% vs. 10.9%, P = 0.001) and coronary ostia dissection (29.4% vs. 12.2%, P = 0.001), and decreased left ventricular ejection fraction (LVEF) (57.5 ± 7.9% vs. 59.8 ± 7.3%, P = 0.032). Intraoperative results showed that more patients in the death group experienced concomitant coronary artery bypass grafting (35.3% vs. 15.3%, P = 0.001) with increased cardiopulmonary bypass (CPB) time (165.7 ± 39.0 vs. 149.4 ± 35.8 min, P = 0.003), cross-clamp time (98.4 ± 24.5 vs. 90.2 ± 26.9 min, P = 0.044), and red blood cell transfusion (913.7 ± 629.0 vs. 709.7 ± 686.6 ml, P = 0.047). Logistic regression analysis showed that age >55 years, renal dysfunction, CPB time >144 min, and RBC transfusion >1,300 ml were independent risk factors for in-hospital mortality in patients with ATAAD.ConclusionIn the present study, we identified that older age, preoperative renal dysfunction, long CPB time, and intraoperative massive transfusion were risk factors for in-hospital mortality in ATAAD patients with the total arch procedure