Weighted General Group Lasso for Gene Selection in Cancer Classification

[EN] Relevant gene selection is crucial for analyzing cancer gene expression datasets including two types of tumors in cancer classification. Intrinsic interactions among selected genes cannot be fully identified by most existing gene selection methods. In this paper, we propose a weighted general group lasso (WGGL) model to select cancer genes in groups. A gene grouping heuristic method is presented based on weighted gene co-expression network analysis. To determine the importance of genes and groups, a method for calculating gene and group weights is presented in terms of joint mutual information. To implement the complex calculation process of WGGL, a gene selection algorithm is developed. Experimental results on both random and three cancer gene expression datasets demonstrate that the proposed model achieves better classification performance than two existing state-of-the-art gene selection methods.This work was supported in part by the National Natural Science Foundation of China under Grant 61572127, in part by the National Key Research and Development Program of China under Grant 2017YFB1400801, in part by the Key Research and Development Program in Jiangsu Province under Grant BE2015728, and in part by the Collaborative Innovation Center of Wireless Communications Technology. The work of R. Ruiz was supported by the Spanish Ministry of Economy and Competitiveness through the Project "SCHEYARD-Optimization of Scheduling Problems in Container Yards" partly financed with FEDER funds under Grant DPI2015-65895-R. This paper was recommended by Associate Editor S. Yang.Wang, Y.; Li, X.; Ruiz García, R. (2019). Weighted General Group Lasso for Gene Selection in Cancer Classification. IEEE Transactions on Cybernetics. 49(8):2860-2873. https://doi.org/10.1109/TCYB.2018.2829811S2860287349

Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN

Learning the physical simulation on large-scale meshes with flat Graph Neural Networks (GNNs) and stacking Message Passings (MPs) is challenging due to the scaling complexity w.r.t. the number of nodes and over-smoothing. There has been growing interest in the community to introduce \textit{multi-scale} structures to GNNs for physical simulation. However, current state-of-the-art methods are limited by their reliance on the labor-intensive drawing of coarser meshes or building coarser levels based on spatial proximity, which can introduce wrong edges across geometry boundaries. Inspired by the bipartite graph determination, we propose a novel pooling strategy, \textit{bi-stride} to tackle the aforementioned limitations. Bi-stride pools nodes on every other frontier of the breadth-first search (BFS), without the need for the manual drawing of coarser meshes and avoiding the wrong edges by spatial proximity. Additionally, it enables a one-MP scheme per level and non-parametrized pooling and unpooling by interpolations, resembling U-Nets, which significantly reduces computational costs. Experiments show that the proposed framework, \textit{BSMS-GNN}, significantly outperforms existing methods in terms of both accuracy and computational efficiency in representative physical simulations.Comment: Updates summary: * update to the accepted version ICM

The relationship between ostracism and negative risk-taking behavior: the role of ego depletion and physical exercise

BackgroundAs a major public health problem globally, negative risk-taking behavior of college students may be related to their ostracism experience, but the reason for this association is unclear. Based on the limited resource theory, combined with the integrative model of athletic performance, we tested a moderated mediation model in which ego depletion mediated the association between ostracism and risk-taking, and physical exercise moderated the mediation process to examine the mechanisms underlying the association between ostracism and negative risk-taking behavior.MethodsOne thousand three hundred seven students (43% female) from four universities in China were recruited using cluster random sampling. The experience of being ostracized, ego depletion, physical exercise level, and negative risk-taking behavior were measured through an anonymous online questionnaire in “www.sojump.com.”ResultsAfter controlling for gender and grade in college, ostracism was positively related to negative risk-taking behavior; ego depletion mediated this relationship; and physical exercise level attenuated these direct and indirect relationships.ConclusionThe results highlight individual risk and protective factors associated with negative risk-taking behavior, and provide new perspectives on ways to prevent and reduce college students’ negative risk-taking behavior

PP2A Inhibitors Arrest G2/M Transition Through JNK/Sp1-Dependent Down-Regulation of CDK1 and Autophagy-Dependent Up-Regulation of p21

Protein phosphatase 2A (PP2A) plays an important role in the control of the cell cycle. We previously reported that the PP2A inhibitors, cantharidin and okadaic acid (OA), efficiently repressed the growth of cancer cells. In the present study, we found that PP2A inhibitors arrested the cell cycle at the G2 phase through a mechanism that was dependent on the JNK pathway. Microarrays further showed that PP2A inhibitors induced expression changes in multiple genes that participate in cell cycle transition. To verify whether these expression changes were executed in a PP2A-dependent manner, we targeted the PP2A catalytic subunit (PP2Ac) using siRNA and evaluated gene expression with a microarray. After the cross comparison of these microarray data, we identified that CDK1 was potentially the same target when treated with either PP2A inhibitors or PP2Ac siRNA. In addition, we found that the down-regulation of CDK1 occurred in a JNK-dependent manner. Luciferase reporter gene assays demonstrated that repression of the transcription of CDK1 was executed through the JNK-dependent activation of the Sp1 transcription factor. By constructing deletion mutants of the CDK1 promoter and by using ChIP assays, we identified an element in the CDK1 promoter that responded to the JNK/Sp1 pathway after stimulation with PP2A inhibitors. Cantharidin and OA also up-regulated the expression of p21, an inhibitor of CDK1, via autophagy rather than PP2A/JNK pathway. Thus, this present study found that the PP2A/JNK/Sp1/CDK1 pathway and the autophagy/p21 pathway participated in G2/M cell cycle arrest triggered by PP2A inhibitors

Artificial Intelligence Framework Identifies Candidate Targets for Drug Repurposing in Alzheimer’s Disease

Background: Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer’s disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. Methods: To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein–protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein–protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells. Results: Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861–0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862–0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. Conclusions: In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD

My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype

Massive genome sequencing data have inspired new challenges in personalized treatments and facilitated oncological drug discovery. We present a comprehensive database, My Personal Mutanome (MPM), for accelerating the development of precision cancer medicine protocols. MPM contains 490,245 mutations from over 10,800 tumor exomes across 33 cancer types in The Cancer Genome Atlas mapped to 94,563 structure-resolved/predicted protein-protein interaction interfaces (“edgetic”) and 311,022 functional sites (“nodetic”), including ligand-protein binding sites and 8 types of protein posttranslational modifications. In total, 8884 survival results and 1,271,132 drug responses are obtained for these mapped interactions. MPM is available at https://mutanome.lerner.ccf.org

Dub3 Inhibition Suppresses Breast Cancer Invasion and Metastasis by Promoting Snail1 Degradation

Snail1, a key transcription factor of epithelial–mesenchymal transition (EMT), is subjected to ubiquitination and degradation, but the mechanism by which Snail1 is stabilized in tumours remains unclear. We identify Dub3 as a bona fide Snail1 deubiquitinase, which interacts with and stabilizes Snail1. Dub3 is overexpressed in breast cancer; knockdown of Dub3 resulted in Snail1 destabilization, suppressed EMT and decreased tumour cell migration, invasion, and metastasis. These effects are rescued by ectopic Snail1 expression. IL-6 also stabilizes Snail1 by inducing Dub3 expression, the specific inhibitor WP1130 binds to Dub3 and inhibits the Dub3-mediating Snail1 stabilization in vitroand in vivo. Our study reveals a critical Dub3–Snail1 signalling axis in EMT and metastasis, and provides an effective therapeutic approach against breast cancer

Bacterial and fungal inhibitor interacted impacting growth of invasive Triadica sebifera and soil N2O emissions

Plant invasions affect biodiversity and seriously endanger the stability of ecosystems. Invasive plants show strong adaptability and growth advantages but are influenced by various factors. Soil bacteria and fungi are critical to plant growth and are important factors affecting plant invasions. Plant invasions also affect soil N2O emissions, but the effects of invasive plants from different population origins on N2O emissions and their microbial mechanisms are not clear. In this experiment, we grew Triadica sebifera from native (China) and invasive (USA) populations with or without bacterial (streptomycin) and/or fungal (iprodione) inhibitors in a factorial experiment in which we measured plant growth and soil N2O emissions of T. sebifera. Plants from invasive populations had higher leaf masses than those from native populations when soil bacteria were not inhibited (with or without fungal inhibition) which might reflect that they are more dependent on soil bacteria. Cumulative N2O emissions were higher for soils with invasive T. sebifera than those with a plant from a native population. Bacterial inhibitor application reduced cumulative N2O emissions but reductions were larger with application of the fungal inhibitor either alone or in combination with the bacterial inhibitor. This suggests that fungi play a strong role in plant performance and soil N2O emissions. Therefore, it is important to further understand the effects of soil microorganisms on the growth of T. sebifera and soil N2O emissions to provide a more comprehensive scientific basis for understanding the causes and consequences of plant invasions