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                        "title": "MTR3D: Identifying regions within protein tertiary structures under purifying selection",
                        "abstract": "© 2021 The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.The identification of disease-causal variants is non-trivial. By mapping population variation from over 448,000 exome and genome sequences to over 81,000 experimental structures and homology models of the human proteome, we have calculated both regional intolerance to missense variation (Missense Tolerance Ratio, MTR), using a sliding window of 21-41 codons, and introduce a new 3D spatial intolerance to missense variation score (3D Missense Tolerance Ratio, MTR3D), using spheres of 5-8 Å. We show that the MTR3D is less biased by regions with limited data and more accurately identifies regions under purifying selection than estimates relying on the sequence alone. Intolerant regions were highly enriched for both ClinVar pathogenic and COSMIC somatic missense variants (Mann-Whitney U test P < 2.2 × 10-16). Further, we combine sequence- and spatial-based scores to generate a consensus score, MTRX, which distinguishes pathogenic from benign variants more accurately than either score separately (AUC = 0.85). The MTR3D server enables easy visualisation of population variation, MTR, MTR3D and MTRX scores across the entire gene and protein structure for >17,000 human genes and >42,000 alternative alternate transcripts, including both Ensembl and RefSeq transcripts. MTR3D is freely available by user-friendly web-interface and API at http://biosig.unimelb.edu.au/mtr3d/.",
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                        "title": "ModularBoost: an efficient network inference algorithm based on module decomposition",
                        "abstract": "© 2021, The Author(s).Background: Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results: ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions: As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.",
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                            {
                                "name": "Li X."
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                        "abstract": "© 2021 Elizarova A et al.Emerging studies demonstrate the ability of microRNAs (miRNAs) to activate genes via different mechanisms. Specifically, miRNAs may trigger an enhancer promoting chromatin remodelling in the enhancer region, thus activating the enhancer and its target genes. Here we present MIREyA, a pipeline developed to predict such miRNA-gene-enhancer trios based on an expression dataset which obviates the need to write custom scripts. We applied our pipeline to primary murine macrophages infected by Mycobacterium tuberculosis (HN878 strain) and detected Mir22, Mir221, Mir222, Mir155 and Mir1956, which could up-regulate genes related to immune responses. We believe that MIREyA is a useful tool for detecting putative miRNA-directed gene activation cases. MIREyA is available from: https://github.com/veania/MIREyA",
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                    "term": "Pathology"
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                        "title": "De novo Prediction of Moonlighting Proteins Using Multimodal Deep Ensemble Learning",
                        "abstract": "© Copyright © 2021 Li, Zhao, Liu, Wang, Wei, Han and Du.Moonlighting proteins (MPs) are a special type of protein with multiple independent functions. MPs play vital roles in cellular regulation, diseases, and biological pathways. At present, very few MPs have been discovered by biological experiments. Due to the lack of data sample, computation-based methods to identify MPs are limited. Currently, there is no de-novo prediction method for MPs. Therefore, systematic research and identification of MPs are urgently required. In this paper, we propose a multimodal deep ensemble learning architecture, named MEL-MP, which is the first de novo computation model for predicting MPs. First, we extract four sequence-based features: primary protein sequence information, evolutionary information, physical and chemical properties, and secondary protein structure information. Second, we select specific classifiers for each kind of feature. Finally, we apply the stacked ensemble to integrate the output of each classifier. Through comprehensive model selection and cross-validation experiments, it is shown that specific classifiers for specific feature types can achieve superior performance. For validating the effectiveness of the fusion-based stacked ensemble, different feature fusion strategies including direct combination and a multimodal deep auto-encoder are used for comparative purposes. MEL-MP is shown to exhibit superior prediction performance (F-score = 0.891), surpassing the existing machine learning model, MPFit (F-score = 0.784). In addition, MEL-MP is leveraged to predict the potential MPs among all human proteins. Furthermore, the distribution of predicted MPs on different chromosomes, the evolution of MPs, the association of MPs with diseases, and the functional enrichment of MPs are also explored. Finally, for maximum convenience, a user-friendly web server is available at: http://ml.csbg-jlu.site/mel-mp/.",
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                        "authors": [
                            {
                                "name": "Li Y."
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                            {
                                "name": "Zhao J."
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                            {
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                                "name": "Wei L."
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                            {
                                "name": "Du W."
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                    "metadata": {
                        "title": "Update of the particle irradiation data ensemble (PIDE) for cell survival",
                        "abstract": "© 2021 The Author(s). Published by Oxford University Press on behalf of The Japanese Radiation Research Society and Japanese Society for Radiation Oncology.The particle irradiation data ensemble (PIDE) is the largest database of cell survival data measured after exposure to ion beams and photon reference radiation. We report here on the updated version of the PIDE database and demonstrate how to investigate generic properties of radiation dose response using these sets of raw data. The database now contains information of over 1100 pairs of photon and ion dose response curves. It provides the originally published raw data of cell survival in addition to given linear quadratic (LQ) model parameters. If available, the raw data were used to derive LQ model parameters in the same way for all experiments. To demonstrate the extent of the database and the variability among experiments we focus on the dose response curves after ion and photon radiation separately in a first step. Furthermore, we discuss the capability and the limitations of the database for analyzing properties of low and high linear energy transfer (LET) radiation response based on multiple experiments. PIDE is freely available to the research community under www.gsi.de/bio-pide.",
                        "date": "2021-07-01T00:00:00Z",
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                            {
                                "name": "Friedrich T."
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