AIMC Topic: Neural Networks, Computer

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Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features.

Sensors (Basel, Switzerland)
Artificial intelligence (AI) and machine learning (ML) are employed to make systems smarter. Today, the speech emotion recognition (SER) system evaluates the emotional state of the speaker by investigating his/her speech signal. Emotion recognition i...

Intelligent Imaging in Nuclear Medicine: the Principles of Artificial Intelligence, Machine Learning and Deep Learning.

Seminars in nuclear medicine
The emergence of artificial intelligence (AI) in nuclear medicine has occurred over the last 50 years but more recent developments in machine learning (ML) and deep learning (DL) have driven new capabilities of AI in nuclear medicine. In nuclear medi...

Comparison of performances of conventional and deep learning-based methods in segmentation of lung vessels and registration of chest radiographs.

Radiological physics and technology
Conventional machine learning-based methods have been effective in assisting physicians in making accurate decisions and utilized in computer-aided diagnosis for more than 30 years. Recently, deep learning-based methods, and convolutional neural netw...

Cardiac magnetic resonance image segmentation based on convolutional neural network.

Computer methods and programs in biomedicine
OBJECTIVE: In cardiac medical imaging, the extraction and segmentation of the part of interest is the key to the diagnosis of heart disease. Due to irregular diastole and contraction, magnetic resonance imaging (MRI) images have poorly defined bounda...

Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence.

Neuron
A potentially organizing goal of the brain and cognitive sciences is to accurately explain domains of human intelligence as executable, neurally mechanistic models. Years of research have led to models that capture experimental results in individual ...

iDRBP_MMC: Identifying DNA-Binding Proteins and RNA-Binding Proteins Based on Multi-Label Learning Model and Motif-Based Convolutional Neural Network.

Journal of molecular biology
DNA-binding protein (DBP) and RNA-binding protein (RBP) are playing crucial roles in gene expression. Accurate identification of them is of great significance, and accurately computational predictors are highly required. In previous studies, DBP reco...

A Deep-Learning View of Chemical Space Designed to Facilitate Drug Discovery.

Journal of chemical information and modeling
Drug discovery projects entail cycles of design, synthesis, and testing that yield a series of chemically related small molecules whose properties, such as binding affinity to a given target protein, are progressively tailored to a particular drug di...

Estimating the Binding of Sars-CoV-2 Peptides to HLA Class I in Human Subpopulations Using Artificial Neural Networks.

Cell systems
Epidemiological studies show that SARS-CoV-2 infection leads to severe symptoms only in a fraction of patients, but the determinants of individual susceptibility to the virus are still unknown. The major histocompatibility complex (MHC) class I expos...

Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.

Journal of chemical information and modeling
One of the main challenges in drug discovery is predicting protein-ligand binding affinity. Recently, machine learning approaches have made substantial progress on this task. However, current methods of model evaluation are overly optimistic in measu...

A gene prioritization method based on a swine multi-omics knowledgebase and a deep learning model.

Communications biology
The analyses of multi-omics data have revealed candidate genes for objective traits. However, they are integrated poorly, especially in non-model organisms, and they pose a great challenge for prioritizing candidate genes for follow-up experimental v...