AIMC Topic: Neural Networks, Computer

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DeLUCS: Deep learning for unsupervised clustering of DNA sequences.

PloS one
We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of prim...

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals.

PloS one
Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrat...

Application of classic and soft computing for modeling yield and environmental final impact in vegetable production (a case study: transplanting onion in Isfahan province, Iran).

Environmental science and pollution research international
This study aimed to develop a precision model between inputs and yield, and also between inputs (indirect emissions) and environmental final index (EFI) in onion farms through regression models (classic computing) and artificial intelligence models (...

COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization.

Computers in biology and medicine
The coronavirus outbreak 2019, called COVID-19, which originated in Wuhan, negatively affected the lives of millions of people and many people died from this infection. To prevent the spread of the disease, which is still in effect, various restricti...

Memristive Behaviors Dominated by Reversible Nucleation Dynamics of Phase-Change Nanoclusters.

Small (Weinheim an der Bergstrasse, Germany)
One of the important steps for realizing artificial intelligence is identifying elementary units that are beneficial for neural network construction. A type of memristive behavior in which phase-change nanoclusters nucleate adaptively in two adjacent...

Deep learning identified glioblastoma subtypes based on internal genomic expression ranks.

BMC cancer
BACKGROUND: Glioblastoma (GBM) can be divided into subtypes according to their genomic features, including Proneural (PN), Neural (NE), Classical (CL) and Mesenchymal (ME). However, it is a difficult task to unify various genomic expression profiles ...

Automated annotation of birdsong with a neural network that segments spectrograms.

eLife
Songbirds provide a powerful model system for studying sensory-motor learning. However, many analyses of birdsong require time-consuming, manual annotation of its elements, called syllables. Automated methods for annotation have been proposed, but th...

Predicting Ca and Mg ligand binding sites by deep neural network algorithm.

BMC bioinformatics
BACKGROUND: Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues.

Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches.

Computational and mathematical methods in medicine
Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health. It occurs abruptly without any symptoms and thus increases the mortality rate of humans. Almost 1% of world's population suffers from epileptic seizures....

Variational quantum classifiers through the lens of the Hessian.

PloS one
In quantum computing, the variational quantum algorithms (VQAs) are well suited for finding optimal combinations of things in specific applications ranging from chemistry all the way to finance. The training of VQAs with gradient descent optimization...