AIMC Topic: Neural Networks, Computer

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A deep learning based two-layer predictor to identify enhancers and their strength.

Methods (San Diego, Calif.)
The enhancer is a DNA sequence that can increase the activity of promoters and thus speed up the frequency of gene transcription. The enhancer plays an essential role in activating gene expression. Currently, gene sequencing technology has been devel...

General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation.

Journal of chemical information and modeling
Cocrystal engineering as an effective way to modify solid-state properties has inspired great interest from diverse material fields while cocrystal density is an important property closely correlated with the material function. In order to accurately...

Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI.

Sensors (Basel, Switzerland)
Alzheimer's disease (AD) is the most common form of dementia. Computer-aided diagnosis (CAD) can help in the early detection of associated cognitive impairment. The aim of this work is to improve the automatic detection of dementia in MRI brain data....

A 2.5D Deep Learning-Based Method for Drowning Diagnosis Using Post-Mortem Computed Tomography.

IEEE journal of biomedical and health informatics
It is challenging to diagnose drowning in autopsy even with the help of post-mortem multi-slice computed tomography (MSCT) due to the complex pathophysiology and the shortage of forensic specialists equipped with radiology knowledge. Therefore, a com...

DisP+V: A Unified Framework for Disentangling Prototype and Variation From Single Sample per Person.

IEEE transactions on neural networks and learning systems
Single sample per person face recognition (SSPP FR) is one of the most challenging problems in FR due to the extreme lack of enrolment data. To date, the most popular SSPP FR methods are the generic learning methods, which recognize query face images...

SpaRCe: Improved Learning of Reservoir Computing Systems Through Sparse Representations.

IEEE transactions on neural networks and learning systems
"Sparse" neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. While, in machine learning, "sparsity" is related to a penalty term that leads to some connecting weights becom...

Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI.

IEEE transactions on neural networks and learning systems
In recent years, deep learning-based feature representation methods have shown a promising impact on electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on...

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data.

IEEE transactions on neural networks and learning systems
Multi-view classification with limited sample size and data augmentation is a very common machine learning (ML) problem in medicine. With limited data, a triplet network approach for two-stage representation learning has been proposed. However, effec...

Adversarial Knowledge Distillation Based Biomedical Factoid Question Answering.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical factoid question answering is an essential application for biomedical information sharing. Recently, neural network based approaches have shown remarkable performance for this task. However, due to the scarcity of annotated data which requ...

A Method for Predicting DNA Motif Length Based On Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
A DNA motif is a sequence pattern shared by the DNA sequence segments that bind to a specific protein. Discovering motifs in a given DNA sequence dataset plays a vital role in studying gene expression regulation. As an important attribute of the DNA ...