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Neuronavigated Magnetic Stimulation combined with cognitive training for Alzheimer’s patients: an EEG graph study
Alzheimer’s disease (AD) is the most common neurodegenerative disorder in elderly subjects. Recent studies verified the effects of cognitive training...
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Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage
Key messageCalibrating a genomic selection model on a sparse factorial design rather than on tester designs is advantageous for some traits, and...
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A three-dimensional virtual mouse generates synthetic training data for behavioral analysis
We developed a three-dimensional (3D) synthetic animated mouse based on computed tomography scans that is actuated using animation and semirandom,...
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Identification of geographical origin and adulteration of Northeast China soybeans by mid-infrared spectroscopy and spectra augmentation
Mathematical models based on infrared spectroscopy and machine learning have been successfully used to trace the origin of soybeans. However, as...
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Multimodal hybrid convolutional neural network based brain tumor grade classification
An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the...
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DPI_CDF: druggable protein identifier using cascade deep forest
BackgroundDrug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug...
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PredictEFC: a fast and efficient multi-label classifier for predicting enzyme family classes
BackgroundEnzymes play an irreplaceable and important role in maintaining the lives of living organisms. The Enzyme Commission (EC) number of an...
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COVID-19 Government Response Event Dataset (CoronaNet v.1.0)
Governments worldwide have implemented countless policies in response to the COVID-19 pandemic. We present an initial public release of a large...
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Prediction of vancomycin initial dosage using artificial intelligence models applying ensemble strategy
BackgroundAntibiotic resistance has become a global concern. Vancomycin is known as the last line of antibiotics, but its treatment index is narrow....
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StackDPP: a stacking ensemble based DNA-binding protein prediction model
BackgroundDNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes,...
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Comparing models of learning and relearning in large-scale cognitive training data sets
Practice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research....
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CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training
BackgroundThe state-of-the-art deep learning based cancer type prediction can only predict cancer types whose samples are available during the...
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Species-specific basecallers improve actual accuracy of nanopore sequencing in plants
BackgroundLong-read sequencing platforms offered by Oxford Nanopore Technologies (ONT) allow native DNA containing epigenetic modifications to be...
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Genomic prediction using training population design in interspecific soybean populations
Agronomically important traits generally have complex genetic architecture, where many genes have a small and largely additive effect. Genomic...
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Real-Time Detection of Shot-Hole Disease in Cherry Fruit Using Deep Learning Techniques via Smartphone
Nowadays, pesticides are generally used to control diseases and pests. However, many farmers often do not fully understand what diseases and pests...
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Insights into ageing rates comparison across tissues from recalibrating cerebellum DNA methylation clock
DNA methylation (DNAm)-based age clocks have been studied extensively as a biomarker of human ageing and a risk factor for age-related diseases....
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Growth charts for small sample sizes using unsupervised clustering: Application to canine early growth
Breed-specific growth curves (GCs) are needed for neonatal puppies, but breed-specific data may be insufficient. We investigated an unsupervised...
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Virtual reality-empowered deep-learning analysis of brain cells
Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present...
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Learning self-supervised molecular representations for drug–drug interaction prediction
Drug–drug interactions (DDI) are a critical concern in healthcare due to their potential to cause adverse effects and compromise patient safety....
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Improving Deep Learning-based Plant Disease Classification with Attention Mechanism
In recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient...
