As a rapid increase in Internet of Things (IoT) devices, vehicle fog/edge computing (VFC/VEC) has seen swift development. Consequently, these devices often opt to purchase computing resources from the edge–cloud service providers (ECSPs) to expand their capabilities. However, due to limited edge server resources, they may face risks of overload or breakdowns. Meanwhile, there are often large amounts of underutilized idle resources near roads (such as parked vehicles), which can provide additional computing and communication capabilities to the system. Inspired by this, we propose a scheme that utilizes idle vehicles to assist in computation. To coordinate the interests of various participating entities and incentivize resource sharing, we construct a multistage multileader multifollower (MSMLMF) Stackelberg game model that encompasses the collaboration and competition among users, ECSPs, vehicle operators (VOPs), and idle vehicles. Participants aim to maximize their utility while considering the potential actions of others. Additionally, considering the information asymmetry between VOP and vehicles, we introduce individual rationality (IR) and incentive compatibility (IC) constraints from the contract theory to analyze and ensure the effectiveness of contracts. Next, we employ backward induction to gradually simplify the game model into convex optimization problems and theoretically prove the existence and uniqueness of Nash equilibrium (NE) points. Finally, through simulation experiments verify that our proposed model and scheme outperform other baselines in overall social welfare.

Chronic obstructive pulmonary disease (COPD) is a complex disease influenced by well-established environmental exposures (most notably, cigarette smoking) and incompletely defined genetic factors. The chromosome 4q region harbors multiple genetic risk loci for COPD, including signals near HHIP, FAM13A, GSTCD, TET2, and BTC. Leveraging RNA-Seq data from lung tissue in COPD cases and controls, we estimated the co-expression network for genes in the 4q region bounded by HHIP and BTC (~70MB), through partial correlations informed by protein-protein interactions. We identified several co-expressed gene pairs based on partial correlations, including NPNT-HHIP, BTC-NPNT and FAM13A-TET2, which were replicated in independent lung tissue cohorts. Upon clustering the co-expression network, we observed that four genes previously associated to COPD: BTC, HHIP, NPNT and PPM1K appeared in the same network community. Finally, we discovered a sub-network of genes differentially co-expressed between COPD vs controls (including FAM13A, PPA2, PPM1K and TET2). Many of these genes were previously implicated in cell-based knock-out experiments, including the knocking out of SPP1 which belongs to the same genomic region and could be a potential local key regulatory gene. These analyses identify chromosome 4q as a region enriched for COPD genetic susceptibility and differential co-expression.

Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundamental feature of eukaryotic gene expression. Cigarette smoking is an important environmental risk factor for many diseases, and it has profound effects on gene expression. Using smoking status as a prediction target, we developed deep neural network predictive models using gene, exon, and isoform level quantifications from RNA sequencing data in 2,557 subjects in the COPDGene Study. We observed that models using exon and isoform quantifications clearly outperformed gene-level models when using data from 5 genes from a previously published prediction model. Whereas the test set performance of the previously published model was 0.82 in the original publication, our exon-based models including an exon-to-isoform mapping layer achieved a test set AUC (area under the receiver operating characteristic) of 0.88, which improved to an AUC of 0.94 using exon quantifications from a larger set of genes. Isoform variability is an important source of latent information in RNA-seq data that can be used to improve clinical prediction models.

Ensemble systems improve the generalization of single classifiers by aggregating the prediction of a set of base classifiers. Assessing classification reliability (posterior probability) is crucial in a number of applications, such as biomedical and diagnosis applications, where the cost of a misclassified input vector can be unacceptable high. Available methods are limited to either calibrate the posterior probability on an aggregated decision value or obtain a posterior probability for each base classifier and aggregate the result. We propose a method that takes advantage of the distribution of the decision values from the base classifiers to summarize a statistic which is subsequently used to generate the posterior probability. Three approaches are considered to fit the probabilistic output to the statistic: the standard Gaussian CDF, isotonic regression, and linear logistic. Even though this study focuses on a bagged support vector machine ensemble (Z‐bag), our approach is not limited by the aggregation method selected, the choice of base classifiers, nor the statistic used. Performance is assessed on one artificial and 12 real‐world data sets from the UCI Machine Learning Repository. Our approach achieves comparable or better generalization on accuracy and posterior estimation to existing ensemble calibration methods although lowering computational cost.

Many analysis techniques are currently available to identify the signaling pathways significantly impacted in a given condition. All these approaches calculate a p-value that aims to quantify the significance of the involvement of a given pathway in the condition under study. These p-values were thought to be related to the likelihood of their respective pathways being involved in the given condition, and to be independent. Here we show that this is not true, and that many pathways are not independent and that can considerably affect each other's p-values through a phenomenon we refer to as “cross-talk.” Thus, the significance of a given pathway in a given experiment has to be interpreted in the context of the other pathways that appear to be significant. Using real data, we show that in same cases pathways with significant classical p-values are not biologically meaningful, and that some biologically meaningful pathways with insignificant p-values become significant when the cross-talk effects of other pathways are removed. We show that this phenomenon is related to the amount of common genes between different pathways, affecting the most widely used methods for pathway analysis, and we propose an analysis technique that is able to correct the over-enrichment significance of a pathway when the cross-talk effects of other pathways are removed.