Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is the major cause of clinical trial failure and postmarketing withdrawals of approved drugs. It is very expensive and time-consuming to evaluate hepatotoxicity using animal or cell-based experiments in the early stage of drug development. In this study, an in silico model based on the joint decision-making strategy was developed for DILI assessment using a relatively large dataset of 2608 compounds. Five consensus models were developed with PaDEL descriptors and PubChem, Substructure, Estate, and Klekota–Roth fingerprints, respectively. Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. The application domain (AD) based on the frequency-weighted and distance (FWD)-based method and Tanimoto similarity index showed the wide AD of the qualified consensus models. A joint decision-making model was integrated by the qualified consensus models, and the overwhelming majority principle was used to improve the performance of consensus models. The application scope narrowing caused by the overwhelming majority principle was successfully solved by joint decision-making. The proposed model successfully predicted 99.2% of the compounds in the test set, with an accuracy of 80.0%, a sensitivity of 83.9, and a specificity of 73.3%. For an external validation set containing 390 compounds collected from DILIrank, 98.2% of the compounds were successfully predicted with an accuracy of 79.9%, a sensitivity of 97.1%, and a specificity of 66.0%. Furthermore, 25 privileged substructures responsible for DILI were identified from Substructure, PubChem, and Klekota–Roth fingerprints. These privileged substructures can be regarded as structural alerts in hepatotoxicity evaluation. Compared with the main published studies, our method exhibits certain advantage in data size, transparency, and standardization of the modeling process and accuracy and credibility of prediction results. It is a promising tool for virtual screening in the early stage of drug development.

Systematic Literature Review of Challenges in Blockchain Scalability

Blockchain technology is fast becoming the most transformative technology of recent times and has created hype and optimism, gaining much attention from the public and private sectors. It has been widely deployed in decentralized crypto currencies such as Bitcoin and Ethereum. Bitcoin is the success story of a public blockchain application that propelled intense research and development into blockchain technology. However, scalability remains a crucial challenge. Both Bitcoin and Ethereum are encountering low-efficiency issues with low throughput, high transaction latency, and huge energy consumption. The scalability issue in public Blockchains is hindering the provision of optimal solutions to businesses and industries. This paper presents a systematic literature review (SLR) on the public blockchain scalability issue and challenges. The scope of this SLR includes an in-depth investigation into the scalability problem of public blockchain, associated fundamental factors, and state-of-art solutions. This project managed to extract 121 primary papers from major scientific databases such as Scopus, IEEE explores, Science Direct, and Web of Science. The synthesis of these 121 articles revealed that scalability in public blockchain is not a singular term. A variety of factors are allied to it, with transaction throughput being the most discussed factor. In addition, other interdependent vita factors include storages, block size, number of nodes, energy consumption, latency, and cost. Generally, each term is somehow directly or indirectly reliant on the consensus model embraced by the blockchain nodes. It is also noticed that the contemporary available consensus models are not efficient in scalability and thus often fail to provide good QoS (throughput and latency) for practical industrial applications. Our findings exemplify that the Internet of Things (IoT) would be the leading application of blockchain in industries such as energy, finance, resource management, healthcare, education, and agriculture. These applications are, however, yet to achieve much-desired outcomes due to scalability issues. Moreover, Onchain and offchain are the two major categories of scalability solutions. Sagwit, block size expansion, sharding, and consensus mechanisms are examples of onchain solutions. Offchain, on the other hand, is a lighting network.

Party systems in the UK

The aim of this chapter is to provide an overview of contemporary party systems in the UK by way of context for the detailed account of party politics in the chapters which follow. It starts by defining the term ‘party system’ before highlighting the difference between party systems under majoritarian and consensus models of democracy and considering various ways of classifying party systems. It then surveys the varieties of party system found at Westminster, devolved, and local levels. It argues that the classic two-party system label now obscures as much as it reveals. If it does still apply, then it is mainly at the level of Westminster politics; even there, however, the minor parties have become more relevant in both the electoral and legislative arenas—and even, on occasion, in the executive arena.

Robust minimum cost consensus models with aggregation operators under individual opinion uncertainty

Individual opinion is one of the vital factors influencing the consensus in group decision-making, and is often uncertain. The previous studies mostly used probability distribution, interval distribution or uncertainty distribution function to describe the uncertainty of individual opinions. However, this requires an accurate understanding of the individual opinions distribution, which is often difficult to satisfy in real life. In order to overcome this shortcoming, this paper uses a robust optimization method to construct three uncertain sets to better characterize the uncertainty of individual initial opinions. In addition, we used three different aggregation operators to obtain collective opinions instead of using fixed values. Furthermore, we applied the numerical simulations on flood disaster assessment in south China so as to evaluate the robustness of the solutions obtained by the robust consensus models that we proposed. The results showed that the proposed models are more robust than the previous models. Finally, the sensitivity analysis of uncertain parameters was discussed and compared, and the characteristics of the proposed models were revealed.

Improving Euclidean’s Consensus Degrees in Group Decision Making Problems Through a Uniform Extension

In a Group Decision Making problem, several people try to reach a single common decision by selecting one of the possible alternatives according to their respective preferences. So, a consensus process is performed in order to increase the level of accord amongst people, called experts, before obtaining the final solution. Improving the consensus degree as much as possible is a very interesting task in the process. In the evaluation of the consensus degree, the measurement of the distance representing disagreement among the experts’ preferences should be considered. Different distance functions have been proposed to implement in consensus models. The Euclidean distance function is one of the most commonly used. This paper analyzes how to improve the consensus degrees, obtained through the Euclidean distance function, when the preferences of the experts are slightly modified by using one of the properties of the Uniform distribution. We fulfil an experimental study that shows the betterment in the consensus degrees when the Uniform extension is applied, taking into account different number of experts and alternatives.

Elucidating the genome of Emydura subglobosa through manual annotation and computational biology tools

Rapid advancements in automated genomic technologies have uncovered many unique findings about the turtle genome and its associated features including olfactory gene expansions and duplications of toll-like receptors. However, automated technologies often result in a high frequency of errors through the process of assembly and annotation and highlight the need for manual annotation. In this study, we have manually annotated four genes of the red-bellied short-neck turtle (Emydura subglobosa), an understudied outgroup of turtles representing a diverse lineage. We improved upon initial ab initio gene predictions through homology-based evidence and generated refined consensus models. Through functional, localization, and structural analyses of the predicted proteins, we have discovered conserved genes encoding proteins that play a role in C21-steroid hormone biosynthetic processes, Vitamin A uptake, collagen/elastin integrity, tumor suppression, and fatty acid catabolism. Overall, these findings further our knowledge about the genetic features underlying turtle physiology, morphology, and longevity, which could have important implications for the treatment of human diseases and evolutionary studies.