AIMC Topic: Neural Networks, Computer

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Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation.

BMC bioinformatics
Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but dropout events, where gene expression is undetected in individual cells, present a significant challenge. We propose scMASKGAN, which transforms ma...

Intelligent health model for medical imaging to guide laymen using neural cellular automata.

Scientific reports
A layman in health systems is a person who doesn't have any knowledge about health data i.e., X-ray, MRI, CT scan, and health examination reports, etc. The motivation behind the proposed invention is to help laymen to make medical images understandab...

An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.

Scientific reports
Breast cancer represents a significant global health challenge, which makes it essential to detect breast cancer early and accurately to improve patient prognosis and reduce mortality rates. However, traditional diagnostic processes relying on manual...

Deep neural network valve detection for accelerometer based cardiac monitoring.

Scientific reports
Miniaturized accelerometers incorporated in pacing leads attached directly onto the heart provide a means for continuous monitoring of cardiac function. Several functional accelerometer indices first require detection of valve events. We previously d...

Modeling rapid language learning by distilling Bayesian priors into artificial neural networks.

Nature communications
Humans can learn languages from remarkably little experience. Developing computational models that explain this ability has been a major challenge in cognitive science. Existing approaches have been successful at explaining how humans generalize rapi...

MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images.

Cell reports. Medicine
Combining molecular classification with clinicopathologic methods improves risk assessment and chooses therapies for endometrial cancer (EC). Detecting mismatch repair (MMR) deficiencies in EC is crucial for screening Lynch syndrome and identifying i...

Data Fusion of Electronic Nose and Multispectral Imaging for Meat Spoilage Detection Using Machine Learning Techniques.

Sensors (Basel, Switzerland)
Meat quality plays a significant role in the consumers' health condition; hence, the constant pursuit for techniques capable of objective and accurate quality assessment by the meat industry. Multispectral imaging and electronic noses are valuable te...

Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on the Chemical Structure.

International journal of molecular sciences
Antibody-drug conjugates (ADCs) are promising cancer therapeutics, but optimizing their cytotoxic payloads remains challenging. We present DumplingGNN, a novel hybrid Graph Neural Network architecture for predicting ADC payload activity and toxicity....

Predicting Placebo Responses Using EEG and Deep Convolutional Neural Networks: Correlations with Clinical Data Across Three Independent Datasets.

Neuroinformatics
Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, and enhancing the detection of true drug effects. In response to this need, we developed a deep convo...

IoT driven smart health monitoring for heart disease prediction using quantum kernel enhanced sardine diffusion and CNN.

Scientific reports
Heart disease is one of the major causes of death worldwide, and the traditional diagnostic procedures typically cause delays in treatment, particularly in low-resource regions. In this article, we propose a novel IoT-based Quantum Kernel-Enhanced Sa...