AIMC Topic: Deep Learning

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An attention-based mRNA transformer network for accurate prediction of melanoma response to immune checkpoint inhibitors.

Scientific reports
Melanoma immunotherapy urgently requires approaches that can accurately predict drug responses to minimize unnecessary treatments. Deep learning models have emerged as powerful tools in this domain due to their robust predictive capabilities. Integra...

Evaluation of deep learning models using explainable AI with qualitative and quantitative analysis for rice leaf disease detection.

Scientific reports
Deep learning models have shown remarkable success in disease detection and classification tasks, but lack transparency in their decision-making process, creating reliability and trust issues. Although traditional evaluation methods focus entirely on...

Using deep learning to predict internalizing problems from brain structure in youth.

Translational psychiatry
Internalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the bi...

Deep Learning Radiomics Model Based on Computed Tomography Image for Predicting the Classification of Osteoporotic Vertebral Fractures: Algorithm Development and Validation.

JMIR medical informatics
BACKGROUND: Osteoporotic vertebral fractures (OVFs) are common in older adults and often lead to disability if not properly diagnosed and classified. With the increased use of computed tomography (CT) imaging and the development of radiomics and deep...

Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.

Cellular and molecular life sciences : CMLS
The 3D organization of the genome plays a critical role in regulating gene expression, maintaining cellular identity, and mediating responses to environmental cues. Advances in super-resolution microscopy and genomic technologies have enabled unprece...

Multiple model visual feature embedding and selection method for an efficient pest classification supporting precision agriculture.

Scientific reports
Agriculture 5.0 is a principal economic activity in the world with major workforce dependent crops cultivation. An automated system for crops field insect pest identification can help decrease labour, while also improving the speed and precision in c...

Ocotillo optimization-driven deep learning for bone marrow cytology classification.

PloS one
Manual diagnosis of hematological cancers like leukemia through bone marrow smear analysis is labor-intensive, prone to errors, and highly dependent on expert knowledge. To overcome these limitations, this study introduces a comprehensive deep learni...

Personalized MRI-based characterization of subcortical anomalies in Ataxia-Telangiectasia using deep-learning.

PloS one
BACKGROUND: Cerebellar atrophy is a known feature of ataxia-telangiectasia (A-T). However, basal ganglia dysfunction contributing to extrapyramidal movement disorders in A-T remains understudied.

Label-free classification of nanoscale drug delivery systems using hyperspectral imaging and convolutional neural networks.

International journal of pharmaceutics
Label-free characterization of nanoscale drug delivery systems remains a critical challenge in pharmaceutical research. Traditional analytical methods, such as cryo-electron microscopy, are labor-intensive, low-throughput, and often require labeling,...

MultiSAAl: Sequence-Informed Antibody-Antigen Interaction Prediction Using Multiscale Deep Learning.

Journal of chemical information and modeling
Antibody-antigen interaction prediction is essential for therapeutic development but remains experimentally costly. The dynamic conformational changes essential to antibody-antigen binding are often missed by structure-based methods relying on static...