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© 2022 Elsevier B.V. All rights reserved, Background: The performance of traditional risk score systems to predict (post)-operative outcomes is limited. This weakness reduces confidence in its use to support clinical risk mitigation decisions. However, the rapid growth of health data in the last years offers principles to deal with some of these limitations. In this regard, the data allows the extraction of relevant information for both patients stratification and the rigorous identification of associated risk factors. The patients can then be targeted to specific preoperative optimization programs, thus contributing to the reduction of associated morbidity and mortality. Objectives: The main goal of this work is, therefore, to provide a clinical decision support system (CDSS) based on data-driven modeling methods for surgical risk prediction specific for cancer patients in Portugal. Results: The result is IPOscore, a single web-based platform aimed at being an innovative approach to assist clinical decision-making in the surgical oncology domain. This system includes a database to store/manage the clinical data collected in a structured format, data visualization and analysis tools, and predictive machine learning models to predict postoperative outcomes in cancer patients. IPOscore also includes a pattern mining module based on biclustering to assess the discriminative power of a pattern towards postsurgical outcomes. Additionally, a mobile application is provided to this end. Conclusions: The IPOscore platform is a valuable tool for surgical oncologists not only for clinical data management but also as a preventative and predictive healthcare system. Currently, this clinical support tool is being tested at the Portuguese Institute of Oncology (IPO-Porto), and can be accessed online at https://iposcore.org., This work was supported by Fundação para a Ciência e a Tecnologia (FCT), through IDMEC, under LAETA project (UIDB/50022/2020) and IPOscore with reference (DSAIPA/DS/0042/2018). This work was further supported by the Associate Laboratory for Green Chemistry (LAQV), financed by national funds from FCT/MCTES (UIDB/50006/2020 and UIDP/50006/2020), INESC-ID plurianual (UIDB/50021/2020), the FCT individual PhD grant to LA (2021.07759.BD) and the contract CEECIND/01399/2017 to RSC

Propositional satisfiability solving (SAT) has been one of the most successful automated reasoning methods in the last decade in computer science by solving a wide range of both industrial and academic problems. One of the most widely used constraint during the process of translating a practical problem into a SAT instance is the at-most-one (AMO) constraint. Many studies on SAT encodings of the AMO constraint have been reported in the literature, however thorough comparisons of these encodings are largely missing. This paper not only provide such comparisons, but also discusses the similarity of the SAT encodings of the AMO constraint and SAT encodings of more general CSPs. More specifically, the paper empirically evaluates the most well-known AMO encodings on three state-of-the-art SAT solvers of a number of benchmarks and provides some guidelines for efficient SAT encodings of the AMO constraint leading.

The KiMoSys (https://kimosys.org), launched in 2014, is a public repository of published experimental data, which contains concentration data of metabolites, protein abundances and flux data. It offers a web-based interface and upload facility to share data, making it accessible in structured formats, while also integrating associated kinetic models related to the data. In addition, it also supplies tools to simplify the construction process of ODE (Ordinary Differential Equations)-based models of metabolic networks. In this release, we present an update of KiMoSys with new data and several new features, including (i) an improved web interface, (ii) a new multi-filter mechanism, (iii) introduction of data visualization tools, (iv) the addition of downloadable data in machine-readable formats, (v) an improved data submission tool, (vi) the integration of a kinetic model simulation environment and (vii) the introduction of a unique persistent identifier system. We believe that this new version will improve its role as a valuable resource for the systems biology community. Database URL: www.kimosys.org