The emergence of vehicular applications such as collision warning enhances traffic efficiency and the driving experience of users. Due to the features of decentralization, complexity, and heterogeneity in intelligent transportation systems, the key issue is ensuring the legality and trustworthiness of all participating parties. Traditional methodologies such as public key infrastructure provide services for verifying the legitimacy of entities with certificates. However, even if a vehicle is legally registered, trust in its behavior is not always assured. Recently decentralized trust management systems (DTMS) are proposed to solve the trust issue. However, achieving both dependability and efficiency remains a challenge. In this paper, we propose a novel DTMS based on the consortium blockchain. We in particular focus on the dependability of trust evaluation, where trust computation is performed on both vehicles and RSUs. To achieve a sustainable trust environment, we propose an incentive model whereby the raters can earn rewards for providing honest ratings. Instead of utilizing the consensus algorithm based on the Proof of Work (PoW) or Proof of Stake (PoS), we implement a verifiable delay function (VDF)-based consensus model within the trusted execution environment (TEE) to ensure efficiency and security for the consortium blockchain. In addition, we design a smart contract on top of the blockchain to assist the system in detecting a specific attack against the trust system, known as the on-off attack. Through extensive experiments, the results of trust assessment and blockchain performance demonstrate the dependability and efficiency of our proposed DTMS.

MOTIVATION: Gene regulatory networks (GRNs) are a way of describing the interaction between genes, which contribute to revealing the different biological mechanisms in the cell. Reconstructing GRNs based on gene expression data has been a central computational problem in systems biology. However, due to the high dimensionality and non-linearity of large-scale GRNs, accurately and efficiently inferring GRNs is still a challenging task. RESULTS: In this article, we propose a new approach, iLSGRN, to reconstruct large-scale GRNs from steady-state and time-series gene expression data based on non-linear ordinary differential equations. Firstly, the regulatory gene recognition algorithm calculates the Maximal Information Coefficient between genes and excludes redundant regulatory relationships to achieve dimensionality reduction. Then, the feature fusion algorithm constructs a model leveraging the feature importance derived from XGBoost (eXtreme Gradient Boosting) and RF (Random Forest) models, which can effectively train the non-linear ordinary differential equations model of GRNs and improve the accuracy and stability of the inference algorithm. The extensive experiments on different scale datasets show that our method makes sensible improvement compared with the state-of-the-art methods. Furthermore, we perform cross-validation experiments on the real gene datasets to validate the robustness and effectiveness of the proposed method. AVAILABILITY AND IMPLEMENTATION: The proposed method is written in the Python language, and is available at: https://github.com/lab319/iLSGRN.

MOTIVATION: Survival analysis using gene expression profiles plays a crucial role in the interpretation of clinical research and assessment of disease therapy programs. Several prediction models have been developed to explore the relationship between patients' covariates and survival. However, the high-dimensional genomic features limit the prediction performance of the survival model. Thus, an accurate and reliable prediction model is necessary for survival analysis using high-dimensional genomic data. RESULTS: In this study, we proposed an improved survival prediction model based on XGBoost framework called XGBLC, which used Lasso-Cox to enhance the ability to analyze high-dimensional genomic data. The novel first- and second-order gradient statistics of Lasso-Cox were defined to construct the loss function of XGBLC. We extensively tested our XGBLC algorithm on both simulated and real-world datasets, and estimated the performance of models with 5-fold cross-validation. Based on 20 cancer datasets from The Cancer Genome Atlas (TCGA), XGBLC outperforms five state-of-the-art survival methods in terms of C-index, Brier score and AUC. The results show that XGBLC still keeps good accuracy and robustness by comparing the performance on the simulated datasets with different scales. The developed prediction model would be beneficial for physicians to understand the effects of patient's genomic characteristics on survival and make personalized treatment decisions. AVAILABILITY AND IMPLEMENTATION: The implementation of XGBLC algorithm based on R language is available at: https://github.com/lab319/XGBLC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Gene regulatory networks (GRNs) capture the regulatory interactions between genes, resulting from the fundamental biological process of transcription and translation. In some cases, the topology of GRNs is not known, and has to be inferred from gene expression data. Most of the existing GRNs reconstruction algorithms are either applied to time-series data or steady-state data. Although time-series data include more information about the system dynamics, steady-state data imply stability of the underlying regulatory networks. RESULTS: In this article, we propose a method for inferring GRNs from time-series and steady-state data jointly. We make use of a non-linear ordinary differential equations framework to model dynamic gene regulation and an importance measurement strategy to infer all putative regulatory links efficiently. The proposed method is evaluated extensively on the artificial DREAM4 dataset and two real gene expression datasets of yeast and Escherichia coli. Based on public benchmark datasets, the proposed method outperforms other popular inference algorithms in terms of overall score. By comparing the performance on the datasets with different scales, the results show that our method still keeps good robustness and accuracy at a low computational complexity. AVAILABILITY AND IMPLEMENTATION: The proposed method is written in the Python language, and is available at: https://github.com/lab319/GRNs_nonlinear_ODEs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.