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                    "term": "Molecular modelling"
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                    "term": "Drug discovery"
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                    "doi": "10.1021/ACS.JCIM.0C01358",
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                        "title": "MSLDOCK: Multi-Swarm Optimization for Flexible Ligand Docking and Virtual Screening",
                        "abstract": "© Autodock and its various variants are widely utilized docking approaches, which adopt optimization methods as search algorithms for flexible ligand docking and virtual screening. However, many of them have their limitations, such as poor accuracy for dockings with highly flexible ligands and low docking efficiency. In this paper, a multi-swarm optimization algorithm integrated with Autodock environment is proposed to design a high-performance and high-efficiency docking program, namely, MSLDOCK. The search algorithm is a combination of the random drift particle swarm optimization with a novel multi-swarm strategy and the Solis and Wets local search method with a modified implementation. Due to the algorithm's structure, MSLDOCK also has a multithread mode. The experimental results reveal that MSLDOCK outperforms other two Autodock-based approaches in many aspects, such as self-docking, cross-docking, and virtual screening accuracies as well as docking efficiency. Moreover, compared with three non-Autodock-based docking programs, MSLDOCK can be a reliable choice for self-docking and virtual screening, especially for dealing with highly flexible ligand docking problems. The source code of MSLDOCK can be downloaded for free from https://github.com/lcmeteor/MSLDOCK.",
                        "date": "2021-03-22T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Li C."
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                            {
                                "name": "Sun J."
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                                "name": "Palade V."
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                        "journal": "Journal of Chemical Information and Modeling"
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                    "term": "Machine learning"
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                    "term": "Infectious disease"
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                    "term": "Urology and nephrology"
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                    "metadata": {
                        "title": "Individualized prediction of COVID-19 adverse outcomes with MLHO",
                        "abstract": "© 2021, The Author(s).The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients’ past medical records (before their COVID-19 infection). MLHO’s architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients’ pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.",
                        "date": "2021-12-01T00:00:00Z",
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                                "name": "Estiri H."
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                            {
                                "name": "Strasser Z.H."
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                                "name": "Murphy S.N."
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                        "journal": "Scientific Reports"
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                    "uri": "http://edamontology.org/topic_0659",
                    "term": "Functional, regulatory and non-coding RNA"
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                    "uri": "http://edamontology.org/topic_3512",
                    "term": "Gene transcripts"
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                    "term": "Microarray experiment"
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                    "term": "Gene expression"
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                    "term": "Statistics and probability"
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                        "title": "miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data",
                        "abstract": "© 2021 Informa UK Limited, trading as Taylor & Francis Group.In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, miRSM, for facilitating the procedure of inferring and analysing miRNA sponge modules. miRSM provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. miRSM will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.",
                        "date": "2021-01-01T00:00:00Z",
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                        "authors": [
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                                "name": "Zhang J."
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                            {
                                "name": "Liu L."
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                            {
                                "name": "Xu T."
                            },
                            {
                                "name": "Zhang W."
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                            {
                                "name": "Zhao C."
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                            {
                                "name": "Li S."
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                            {
                                "name": "Li J."
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                            {
                                "name": "Rao N."
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