Results 161 to 170 of about 11,591,695 (294)

DLBLS_SS: protein secondary structure prediction using deep learning and broad learning system. [PDF]

RSC Adv, 2022
Yuan L, Hu X, Ma Y, Liu Y.
europepmc   +1 more source

White Matter Hyperintensity Burden and Short‐Interval Change Associated With Sleep Apnoea in the UK Biobank

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Purpose White matter hyperintensities (WMH) are a core neuroimaging marker of cerebral small vessel disease (CSVD). Sleep apnoea (SA) is a recognized vascular risk factor, but its associations with regional WMH burden, short‐interval WMH change and cognitive performance in population‐based cohorts remain incompletely defined. We
Peng Cheng, Diego Z. Carvalho, Shahid Karim, Anwar Chahal, Virend K. Somers   +4 more
wiley   +1 more source

Cluster Algorithm for Protein Secondary Structure Prediction

, 2013
Protein structure determination and prediction has been a focal research subject in the area of drug discovery due to the importance of protein structure in understanding the biological and chemical activities of organisms.
Singh, Manpreet
core   +1 more source

Improving RNA secondary structure prediction through expanded training data. [PDF]

RNA
Langeberg CJ, Kim T, Nagle R, Rajinikanth A, Meredith C, Garuadapuri DA, Doudna JA, Cate JHD.   +7 more
europepmc   +1 more source

Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani, Emmanuel Barbeau, Béatrice Lemesle, Emilie Milongo‐Rigal, Sophie Fernandez, Sandrine Charpentier, Jean‐François Albucher, Nicolas Raposo, Fabrice Bonneville, Patrice Péran, Jérémie Pariente   +10 more
wiley   +1 more source

Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction. [PDF]

PLoS Comput Biol, 2023
Qiu X.
europepmc   +1 more source

Early Clinical, Imaging, and Pathological Characteristics of SRPK3/TTN‐Digenic Myopathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective SRPK3/TTN‐digenic myopathy was recently established as a skeletal muscle myopathy caused by digenic inheritance. This study characterizes the early clinical presentation of SRPK3/TTN‐digenic myopathy in one previously reported and seven newly identified pediatric patients.
Rotem Orbach, Sandra Donkervoort, Carola Hedberg‐Oldfors, Giovanni Baranello, Dimah Saade, Precilla D'Souza, Ruchee Patel, Eva Michael, A. Reghan Foley, Diana Bharucha‐Goebel, S. Jin Haugland, Meghan McAnally, Omer Abdul Hamid, Katherine Chao, Ellen F. Macnamara, Alan H. Beggs, Anna Sarkozy, Juliane Mueller, Steven A. Moore, Richard S. Finkel, Cynthia J. Tifft, Francesco Muntoni, Anders Oldfors, Carsten G. Bönnemann   +23 more
wiley   +1 more source

Machine learning for RNA secondary structure prediction: a review of current methods and challenges. [PDF]

RNA
Sacco G, Bussi G, Sanguinetti G.
europepmc   +1 more source

Multistage Combination Classifier Augmented Model for Protein Secondary Structure Prediction. [PDF]

Front Genet, 2022
Zhang X, Liu Y, Wang Y, Zhang L, Feng L, Jin B, Zhang H.   +6 more
europepmc   +1 more source

Algorithm for the prediction of RNA secondary structures

Вычислительные технологии, 2001
Under the hypothesis of loops dependent energy, the author exposes a dynamic programming algorithm for calculating the free energies of stable secondary structures and a traceback algorithm for predicting these structures. The free energies of the stable secondary structures are calculated by using a new method called \(m\)-multiloop approach (\(m\)-MA)
openaire   +2 more sources