Recently, the attention of the research community has been focused on long non-coding RNAs (lncRNAs) and their physiological/pathological implications. As the number of experiments increase in a rapid rate and transcriptional units are better annotated, databases indexing lncRNA properties and function gradually become essential tools to this process. Aim of DIANA-LncBase (www.microrna.gr/LncBase) is to reinforce researchers' attempts and unravel microRNA (miRNA)-lncRNA putative functional interactions. This study provides, for the first time, a comprehensive annotation of miRNA targets on lncRNAs. DIANA-LncBase hosts transcriptome-wide experimentally verified and computationally predicted miRNA recognition elements (MREs) on human and mouse lncRNAs. The analysis performed includes an integration of most of the available lncRNA resources, relevant high-throughput HITS-CLIP and PAR-CLIP experimental data as well as state-of-the-art in silico target predictions. The experimentally supported entries available in DIANA-LncBase correspond to >5000 interactions, while the computationally predicted interactions exceed 10 million. DIANA-LncBase hosts detailed information for each miRNA-lncRNA pair, such as external links, graphic plots of transcripts' genomic location, representation of the binding sites, lncRNA tissue expression as well as MREs conservation and prediction scores.
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http://dx.doi.org/10.1093/nar/gks1246 | DOI Listing |
J Chem Inf Model
January 2025
Max-Planck-Institut für Immunbiologie und Epigenetik (MPI-IE), Stübeweg 51, 79108 Freiburg im Breisgau, Germany.
Intrinsically disordered regions are found in most eukaryotic proteins and are enriched with positively and negatively charged residues. While it is often convenient to assume that these residues follow their model-compound p values, recent work has shown that local charge effects (charge regulation) can upshift or downshift side chain p values with major consequences for molecular function. Despite this, charge regulation is rarely considered when investigating disordered regions.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Department of Physics, Wesleyan University, Middletown, Connecticut 06459, USA.
Phase change materials such as Ge2Sb2Te5 (GST) are ideal candidates for next-generation, non-volatile, solid-state memory due to the ability to retain binary data in the amorphous and crystal phases and rapidly transition between these phases to write/erase information. Thus, there is wide interest in using molecular modeling to study GST. Recently, a Gaussian Approximation Potential (GAP) was trained for GST to reproduce Density Functional Theory (DFT) energies and forces at a fraction of the computational cost [Zhou et al.
View Article and Find Full Text PDFJ Phys Chem C Nanomater Interfaces
January 2025
Materials Chemistry and Catalysis, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht 3584 CG, The Netherlands.
Machine learning potentials (MLPs) offer efficient and accurate material simulations, but constructing the reference ab initio database remains a significant challenge, particularly for catalyst-adsorbate systems. Training an MLP with a small data set can lead to overfitting, thus limiting its practical applications. This study explores the feasibility of developing computationally cost-effective and accurate MLPs for catalyst-adsorbate systems with a limited number of ab initio references by leveraging a transfer learning strategy from subsets of a comprehensive public database.
View Article and Find Full Text PDFACS Nano
January 2025
Graduate School for Integrative Sciences and Engineering Programme, National University of Singapore, Singapore119077, Singapore.
Machine-learned potentials (MLPs) have transformed the field of molecular simulations by scaling "quantum-accurate" potentials to linear time complexity. While they provide more accurate reproduction of physical properties as compared to empirical force fields, it is still computationally costly to generate their training data sets from ab initio calculations. Despite the emergence of foundational or general MLPs for organic molecules and dense materials, it is unexplored if one general MLP can be effectively developed for a wide variety of nanoporous metal-organic frameworks (MOFs) with different chemical moieties and geometric properties.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, 29209, USA.
Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data.
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