Emotion recognition from multi-channel EEG via deep forest
Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks
based on electroencephalography (EEG), and have achieved better performance than …
based on electroencephalography (EEG), and have achieved better performance than …
[HTML][HTML] Three-dimensional dense reconstruction: A review of algorithms and datasets
Y Lee - Sensors, 2024 - mdpi.com
Three-dimensional dense reconstruction involves extracting the full shape and texture
details of three-dimensional objects from two-dimensional images. Although 3D …
details of three-dimensional objects from two-dimensional images. Although 3D …
MLMDA: a machine learning approach to predict and validate MicroRNA–disease associations by integrating of heterogenous information sources
Background Emerging evidences show that microRNA (miRNA) plays an important role in
many human complex diseases. However, considering the inherent time-consuming and …
many human complex diseases. However, considering the inherent time-consuming and …
PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation
Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The
growing research on ACPs as therapeutic agent is increasing due to its minimal side effects …
growing research on ACPs as therapeutic agent is increasing due to its minimal side effects …
An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network
H Jiang, Y Huang - BMC bioinformatics, 2022 - Springer
Abstract Background Drug-disease associations (DDAs) can provide important information
for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs …
for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs …
DeepCPPred: a deep learning framework for the discrimination of cell-penetrating peptides and their uptake efficiencies
Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of
bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles …
bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles …
GNMFLMI: graph regularized nonnegative matrix factorization for predicting LncRNA-MiRNA interactions
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been involved in various
biological processes. Emerging evidence suggests that the interactions between lncRNAs …
biological processes. Emerging evidence suggests that the interactions between lncRNAs …
In silico prediction methods of self-interacting proteins: an empirical and academic survey
In silico prediction of self-interacting proteins (SIPs) has become an important part of
proteomics. There is an urgent need to develop effective and reliable prediction methods to …
proteomics. There is an urgent need to develop effective and reliable prediction methods to …
Improving deep forest by screening
Most studies about deep learning are based on neural network models, where many layers
of parameterized nonlinear differentiable modules are trained by backpropagation …
of parameterized nonlinear differentiable modules are trained by backpropagation …
The heterogeneous ensemble of deep forest and deep neural networks for micro-expressions recognition
Abstract Micro-Expressions (MEs) are a kind of short-lived and uncontrollable facial
expressions. Therefore, the MEs recognition task poses a great challenge to both the …
expressions. Therefore, the MEs recognition task poses a great challenge to both the …