AIMC Topic: Neural Networks, Computer

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BLSAM-TIP: Improved and robust identification of tyrosinase inhibitory peptides by integrating bidirectional LSTM with self-attention mechanism.

PloS one
Tyrosinase plays a central role in melanin biosynthesis, and its dysregulation has been implicated in the pathogenesis of various pigmentation disorders. The precise identification of tyrosinase inhibitory peptides (TIPs) is critical, as these bioact...

AutoTFCNNY: A multi-instance neural network for enhanced early cancer detection using TCR data.

PloS one
For most cancers, early diagnosis and intervention can significantly improve cure rates and patient survival. Consequently, achieving early and accurate cancer detection has always been a central focus in both medical practice and scientific research...

Robust detection of femtogram-level Alzheimer's biomarkers using machine learning-enhanced graphene biosensors.

Biosensors & bioelectronics
Early diagnosis of Alzheimer's disease (AD) requires blood biomarker tests sensitive to femtogram/mL concentrations. Graphene field-effect transistors (GFETs) are promising for this application, but suffer from device-to-device variability and requir...

Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.

Proceedings of the National Academy of Sciences of the United States of America
Understanding the computational principles of the brain and translating them into neuromorphic hardware and modern deep learning architectures is critical for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally constrained, biop...

Deep intelligence: a four-stage deep network for accurate brain tumor segmentation.

Scientific reports
Image segmentation is an essential research field in image processing that has developed from traditional processing techniques to modern deep learning methods. In medical image processing, the primary goal of the segmentation process is to segment o...

An integrated algorithm for single lead electrocardiogram signal analysis using deep learning with 12-lead data.

Scientific reports
Artificial intelligence (AI) algorithms have demonstrated remarkable efficiency in analyzing 12-lead clinical electrocardiogram (ECG) signals. This has sparked interest in leveraging cost-effective and user-friendly smart devices based on single-lead...

Multi-modal deep learning framework for early detection of Parkinson's disease using neurological and physiological data for high-fidelity diagnosis.

Scientific reports
Parkinson's disease (PD) is a progressive neurodegenerative disorder that remained challenging for proper diagnosis in its early stages due to its heterogeneous symptom presentation and overlapping clinical features. Consequently, there is no consens...

Anston attentional network for structured data based stroke risk prediction in smart aging.

Scientific reports
To reduce the pressure on public health services caused by the aging population, nursing homes need to predict disease risks for the elderly periodically. To improve the disease risks predicting ability of nursing homes, we designed Anston (An Attent...

Use of a Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework for Low-Resource Settings: Development and Validation Study.

JMIR formative research
BACKGROUND: Early-stage diagnosis of laryngeal cancer significantly improves patient survival and quality of life. However, the scarcity of specialists in low-resource settings hinders the timely review of flexible nasopharyngoscopy (FNS) videos, whi...

Predicting drug-target affinity through triple pre-activated random residual planet convolution coupled attention network and contact maps.

Journal of computer-aided molecular design
Drug discovery relies on the ability to predict drug-target affinity (DTA), which allows for the efficient identification of drug candidates for certain protein targets. Scalability, accuracy, and interpretability are issues that traditional methods ...