AIMC Topic: Neural Networks, Computer

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[Application of convolutional neural networks for the classification of metaphase chromosomes].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics
OBJECTIVE: To train a deep convolutional neural networks (CNN) using a labeled data set to classify the metaphase chromosomes and test its accuracy for chromosomal identification.

Deep CNNs for glioma grading on conventional MRIs: Performance analysis, challenges, and future directions.

Mathematical biosciences and engineering : MBE
The increasing global incidence of glioma tumors has raised significant healthcare concerns due to their high mortality rates. Traditionally, tumor diagnosis relies on visual analysis of medical imaging and invasive biopsies for precise grading. As a...

A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection.

Mathematical biosciences and engineering : MBE
Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area o...

AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides.

Bioinformatics (Oxford, England)
MOTIVATION: Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect.

iNGNN-DTI: prediction of drug-target interaction with interpretable nested graph neural network and pretrained molecule models.

Bioinformatics (Oxford, England)
MOTIVATION: Drug-target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but c...

RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification.

Bioinformatics (Oxford, England)
MOTIVATION: Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention ...

EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.

Bioinformatics (Oxford, England)
SUMMARY: Deep neural networks (DNNs) have been widely applied to predict the molecular functions of the non-coding genome. DNNs are data hungry and thus require many training examples to fit data well. However, functional genomics experiments typical...

Insights into the inner workings of transformer models for protein function prediction.

Bioinformatics (Oxford, England)
MOTIVATION: We explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such that latent ...

Micro-expression recognition based on multi-scale 3D residual convolutional neural network.

Mathematical biosciences and engineering : MBE
In demanding application scenarios such as clinical psychotherapy and criminal interrogation, the accurate recognition of micro-expressions is of utmost importance but poses significant challenges. One of the main difficulties lies in effectively cap...

Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females.

Experimental dermatology
Predicting a person's chronological age (CA) from visible skin features using artificial intelligence (AI) is now commonplace. Often, convolutional neural network (CNN) models are built using images of the face as biometric data. However, hands hold ...