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  • Articles  (81,203)
  • Oxford University Press  (79,222)
  • Berkeley Electronic Press (now: De Gruyter)
  • Medicine  (59,190)
  • Economics  (22,071)
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  • 1
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    Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: Motivation Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference. Results Based on the Gaussian graphical model with adaptive gene importance regularization, we develop a novel importance-penalized joint graphical Lasso method, IPJGL, for differential network inference. The presented method is validated by the simulation experiments as well as the real datasets. Furthermore, to precisely evaluate the results of differential network inference, we propose a new metric named APC2 for the differential levels of gene pairs. We apply IPJGL to analyze the TCGA colorectal and breast cancer datasets and find some candidate cancer genes with significant survival analysis results, including SOST for colorectal cancer and RBBP8 for breast cancer. We also conduct further analysis based on the interactions in the Reactome database and confirm the utility of our method. Availability R source code of importance-penalized joint graphical lasso is freely available at https://github.com/Wu-Lab/IPJGL. Supplementary information Supplementary materials are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 2
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    AOP-helpFinder webserver: a tool for comprehensive analysis of the literature to support adverse outcome pathways development (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: Motivation Adverse Outcome Pathways (AOPs) are a conceptual framework developed to support the use of alternative toxicology approaches in the risk assessment. AOPs are structured linear organizations of existing knowledge illustrating causal pathways from the initial molecular perturbation triggered by various stressors, through key events (KEs) at different levels of biology, to the ultimate health or ecotoxicological adverse outcome. Results Artificial intelligence can be used to systematically explore available toxicological data that can be parsed in the scientific literature. Recently a tool called AOP-helpFinder was developed to identify associations between stressors and KEs supporting thus documentation of AOPs. To facilitate the utilization of this advanced bioinformatics tool by the scientific and the regulatory community, a webserver was created. The proposed AOP-helpFinder webserver uses better performing version of the tool which reduces the need for manual curation of the obtained results. As an example, the server was successfully applied to explore relationships of a set of endocrine disruptors with metabolic-related events. The AOP-helpFinder webserver assists in a rapid evaluation of existing knowledge stored in the PubMed database, a global resource of scientific information, to build AOPs and Adverse Outcome Networks (AONs) supporting the chemical risk assessment. Availability and implementation AOP-helpFinder is available at http://aop-helpfinder.u-paris-sciences.fr/index.php
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 3
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    Predictors of Pain Reduction in Trials of Interventions for Aromatase Inhibitor-Associated Musculoskeletal Symptoms (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: Background Almost half of aromatase inhibitor (AI)-treated breast cancer patients experience AI-associated musculoskeletal symptoms (AIMSS); 20-30% discontinue treatment because of severe symptoms. We hypothesized that we could identify predictors of pain reduction in AIMSS intervention trials by combining data from previously conducted trials. Methods We pooled patient-level data from 3 randomized trials testing interventions (omega-3 fatty acids, acupuncture, and duloxetine) for AIMSS that had similar eligibility criteria and the same patient-reported outcome measures. Only patients with baseline Brief Pain Inventory (BPI) average pain score of ≥ 4 of 10 were included. The primary outcome examined was 2-point reduction in average pain from baseline to week 12. Variable cut-point selection and logistic regression were used. Risk models were built by summing the number of factors statistically significantly associated with pain reduction. Analyses were stratified by study and adjusted for treatment arm. Results For the 583 analyzed patients, the four factors statistically significantly associated with pain reduction were FACT Functional Well-Being 〉24 and Physical Well-Being 〉14 (higher scores reflect better function), and WOMAC
    Electronic ISSN: 2515-5091
    Topics: Chemistry and Pharmacology , Medicine
    Published by Oxford University Press on behalf of National Cancer Institute.
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  • 4
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    The Power of Word-Frequency Based Alignment-Free Functions: a Comprehensive Large-Scale Experimental Analysis (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: Motivation Alignment-free (AF) distance/similarity functions are a key tool for sequence analysis. Experimental studies on real datasets abound and, to some extent, there are also studies regarding their control of false positive rate (Type I error). However, assessment of their power, i.e., their ability to identify true similarity, has been limited to some members of the D2 family. The corresponding experimental studies have concentrated on short sequences, a scenario no longer adequate for current applications, where sequence lengths may vary considerably. Such a State of the Art is methodologically problematic, since information regarding a key feature such as power is either missing or limited. Results By concentrating on a representative set of word-frequency based AF functions, we perform the first coherent and uniform evaluation of the power, involving also Type I error for completeness. Two Alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer), a wide range of sequence lengths from a few thousand to millions, and different values of k have been used. As a result, we provide a characterization of those AF functions that is novel and informative. Indeed, we identify weak and strong points of each function considered, which may be used as a guide to choose one for analysis tasks. Remarkably, of the fifteen functions that we have considered, only four stand out, with small differences between small and short sequence length scenarios. Finally, in order to encourage the use of our methodology for validation of future AF functions, the Big Data platform supporting it is public. Availability The software is available at: https://github.com/pipp8/power_statistics Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 5
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    CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Description: Motivation Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural features in coiled-coil: coiled-coil domain (CCD), oligomeric state, and register. However, most of the existing computational tools only focus on one of them. Results Here, we describe a new deep learning model, CoCoPRED, which is based on convolutional layers, bidirectional long short-term memory, and attention mechanism. It has three networks, i.e., CCD network, oligomeric state network, and register network, corresponding to the three types of structural features in coiled-coil. This means CoCoPRED has the ability of fulfilling comprehensive prediction for coiled-coil proteins. Through the 5-fold cross-validation experiment, we demonstrate that CoCoPRED can achieve better performance than the state-of-the-art models on both CCD prediction and oligomeric state prediction. Further analysis suggests the CCD prediction may be a performance indicator of the oligomeric state prediction in CoCoPRED. The attention heads in CoCoPRED indicate that registers a, b, and e are more crucial for the oligomeric state prediction. Availability CoCoPRED is available at http://www.csbio.sjtu.edu.cn/bioinf/CoCoPRED. Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 6
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    Eureka! A Petroleum Transactions Guide that Delivers (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-30
    Print ISSN: 1754-9957
    Electronic ISSN: 1754-9965
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Law , Economics
    Published by Oxford University Press
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  • 7
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    Local border reforms and economic activity (2021)
    Oxford University Press
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    Publication Date: 2021-10-30
    Description: In this article, we make use of large-scale municipal border changes in Germany to provide the first evidence on the effect of local border changes on the distribution of activity in space. To allow for a comparison of economic activity within unique geographical units over time, we use geo-coded light data as well as local land-use data. Applying a difference-in-differences approach, we find evidence that municipalities absorbing their merger partners and hosting the new administrative center experience a significant increase in local activity, while the municipalities that are being absorbed and are losing the administrative center experience a decrease in such activity. The difference between the gains in activity from absorbing municipalities and the losses from absorbed ones is positive. These previously undocumented results point to the importance of distance to the administrative center as a determinant of the spatial distribution of economic activity.
    Print ISSN: 1468-2702
    Electronic ISSN: 1468-2710
    Topics: Geography , Economics
    Published by Oxford University Press
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  • 8
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    The Jun-dependent axon regeneration gene program: Jun promotes regeneration over plasticity (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-28
    Description: The regeneration-associated gene (RAG) expression program is activated in injured peripheral neurons after axotomy and enables long-distance axon re-growth. Over 1000 genes are regulated, and many transcription factors are upregulated or activated as part of this response. However, a detailed picture of how RAG expression is regulated is lacking. In particular the transcriptional targets and specific functions of the various transcription factors are unclear. Jun was the first regeneration-associated transcription factor identified and the first shown to be functionally important. Here we fully define the role of Jun in the RAG expression program in regenerating facial motor neurons. At 1, 4, and 14 days after axotomy, Jun upregulates 11%, 23% and 44% of the RAG program, respectively. Jun functions relevant to regeneration include cytoskeleton production, metabolic functions and cell activation, and the down-regulation of neurotransmission machinery. In silico analysis of promoter regions of Jun targets identifies stronger over-representation of AP1-like sites than CRE-like sites, although CRE sites were also over-represented in regions flanking AP1 sites. Strikingly, in motor neurons lacking Jun, an alternative SRF-dependent gene expression program is initiated after axotomy. The promoters of these newly expressed genes exhibit over-representation of CRE sites in regions near to SRF target sites. This alternative gene expression program includes plasticity-associated transcription factors, and leads to an aberrant early increase in synapse density on motor neurons. Jun thus has the important function in the early phase after axotomy of pushing the injured neuron away from a plasticity response and towards a regenerative phenotype.
    Print ISSN: 0964-6906
    Electronic ISSN: 1460-2083
    Topics: Biology , Medicine
    Published by Oxford University Press
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  • 9
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    DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-28
    Description: Motivation DNA Methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods employ either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination. Results Here we present DeepMP, a convolutional neural network (CNN)-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on E. coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets. Availability DeepMP is implemented and freely available under MIT license at https://github.com/pepebonet/DeepMP Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 10
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    CpG Transformer for imputation of single-cell methylomes (2021)
    Oxford University Press
    Details
    Publication Date: 2021-10-28
    Description: Motivation The adoption of current single-cell DNA methylation sequencing protocols is hindered by incomplete coverage, outlining the need for effective imputation techniques. The task of imputing single-cell (methylation) data requires models to build an understanding of underlying biological processes. Results We adapt the transformer neural network architecture to operate on methylation matrices through combining axial attention with sliding window self-attention. The obtained CpG Transformer displays state-of-the-art performances on a wide range of scBS-seq and scRRBS-seq datasets. Furthermore, we demonstrate the interpretability of CpG Transformer and illustrate its rapid transfer learning properties, allowing practitioners to train models on new datasets with a limited computational and time budget. Availability and Implementation CpG Transformer is freely available at https://github.com/gdewael/cpg-transformer. Supplementary information Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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