Search
Search Results
-
Exploring an online method of measuring implicit sequence-learning consciousness
Existing methods for measuring implicit sequence-learning consciousness are conducted offline. Based on the traditional measurement of...
-
Suppression of Motor Sequence Learning and Execution Through Anodal Cerebellar Transcranial Electrical Stimulation
Cerebellum (CB) and primary motor cortex (M1) have been associated with motor learning, with different putative roles. Modulation of task performance...
-
Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease
Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico...
-
Fundamentals for predicting transcriptional regulations from DNA sequence patterns
Cell-type-specific regulatory elements, cataloged through extensive experiments and bioinformatics in large-scale consortiums, have enabled...
-
Consolidation of motor sequence learning eliminates susceptibility of SMAproper to TMS: a combined rTMS and cTBS study
Earlier research suggested that after 210 practice trials, the supplementary motor area (SMA) is involved in executing all responses of familiar...
-
Clinical feature-related single-base substitution sequence signatures identified with an unsupervised machine learning approach
BackgroundMutation processes leave different signatures in genes. For single-base substitutions, previous studies have suggested that mutation...
-
Motor learning and performance in schizophrenia and aging: two different patterns of decline
Psychomotor slowing has consistently been observed in schizophrenia, however research on motor learning in schizophrenia is limited. Additionally,...
-
A delayed matching task-based study on action sequence of motor imagery
The way people imagine greatly affects performance of brain-computer interface (BCI) based on motion imagery (MI). Action sequence is a basic unit of...
-
White matter microstructural changes in short-term learning of a continuous visuomotor sequence
Efficient neural transmission is crucial for optimal brain function, yet the plastic potential of white matter (WM) has long been overlooked. Growing...
-
Attention mechanism based multi-sequence MRI fusion improves prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer
BackgroundAccurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) is very important for treatment plan decision in locally advanced...
-
Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5
Sepsis is a disease with a very high mortality rate, mainly involving an immune-dysregulated response due to bacterial infection. Most studies are...
-
Deep-learning-enabled antibiotic discovery through molecular de-extinction
Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here...
-
A novel paradigm for observational learning in rats
The ability to learn by observing the behavior of others is energy efficient and brings high survival value, making it an important learning tool...
-
Explicit motor sequence learning after stroke: a neuropsychological study
Motor learning interacts with and shapes experience-dependent cerebral plasticity. In stroke patients with paresis of the upper limb, motor recovery...
-
Sequence Alignment
Sequence alignment is an essential computational step before performing evolutionary analysis of genomic sequences and structural analysis of...
-
Transformer-based temporal sequence learners for arrhythmia classification
An electrocardiogram (ECG) plays a crucial role in identifying and classifying cardiac arrhythmia. Traditional methods employ handcrafted features,...
-
SCAN: sequence-based context-aware association network for hepatic vessel segmentation
Accurate segmentation of hepatic vessel is significant for the surgeons to design the preoperative planning of liver surgery. In this paper, a...
-
Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings
Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks
1 –6 , including the prediction of gene...
-
SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models
ObjectiveTo address the challenge of computational identification of cell type-specific regulatory elements on a genome-wide scale.
Results ...
-
The dengue-specific immune response and antibody identification with machine learning
Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all...
