Motivation: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form the so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best-performing method. Hence, there is a need for combining these methods to generate robust disease modules.
Results: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.
Availability And Implementation: MODifieR is available under the GNU GPL license and can be freely downloaded from https://gitlab.com/Gustafsson-lab/MODifieR and as a Docker image from https://hub.docker.com/r/ddeweerd/modifier.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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Environ Res
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Faculty of Exact Sciences and Technology, Federal University of Grande Dourados, Dourados, MS, 79804-970, Brazil. Electronic address:
Transforming lignocellulosic biomass waste into value-added materials like porous carbons offers a sustainable and increasingly important solution for its efficient management within a circular economy framework. Although the heteroatom-doping process enhances oxygen- or nitrogen-containing functionalities on porous carbons, it often leads to losses in structural integrity and other key functionalities. This study presents a novel protocol to produce N-doped porous carbons that efficiently introduces nitrogen groups while improving surface area, microporosity definition and the concentration of oxygen-containing functionalities.
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IADI, U1254, Inserm, Université de Lorraine, Nancy, France.
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January 2025
Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata 700106, India.
Estimating rare event kinetics from molecular dynamics simulations is a non-trivial task despite the great advances in enhanced sampling methods. Weighted Ensemble (WE) simulation, a special class of enhanced sampling techniques, offers a way to directly calculate kinetic rate constants from biased trajectories without the need to modify the underlying energy landscape using bias potentials. Conventional WE algorithms use different binning schemes to partition the collective variable (CV) space separating the two metastable states of interest.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Chemistry and Biochemistry, University of California, Los Angeles, 607 Charles E. Young Drive, Los Angeles, California 90095-1569, United States.
Dimension-engineered synthesis of atomically thin II-VI nanoplatelets (NPLs) remains an open challenge. While CdSe NPLs have been made with confinement ranging from 2 to 11 monolayers (ML), CdTe NPLs have been significantly more challenging to synthesize and separate. Here we provide detailed mechanistic insight into the layer-by-layer growth kinetics of the CdTe NPLs.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Automation, Tsinghua University, Beijing, China. Electronic address:
Background: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators at admission. Electrocardiogram (ECG) provides easily accessible information, with heart rate variability (HRV) derived from ECG showing prognostic value. However, few studies have conducted a comprehensive analysis of HRV-based prognostic model against established standards, which limits the application of HRV's prognostic value in clinical settings.
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