AIMC Topic: Neural Networks, Computer

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μOR-ligand: target-aware view-based hybrid feature selection for μ-opioid receptor ligand functional classification.

Journal of computer-aided molecular design
Understanding active functional class (agonist vs antagonist) at the human μ-opioid receptor (μOR) is critical for drug discovery and safety assessment. While recent machine learning models such as ExtraTrees (ET) and message-passing neural networks ...

Decoding spatiotemporal dynamics of suspended sediment and vegetation in shallow reservoirs with Sentinel-2 and ANNs: A case study of Lake Tisza, Hungary.

Environmental monitoring and assessment
Shallow reservoirs on large rivers are highly dynamic systems vulnerable to sediment accumulation, eutrophication, and water quality deterioration, posing significant threats to their storage capacity, hydropower generation, and ecological balance. R...

Cervical cancer prediction using deformable kernel darknet-53 and depth wise separable convolutional neural networks.

Scientific reports
The prediction of Cervical Cancer (CC) remains a tough task due to diverse clinical variations and unbalanced data distribution, while good-quality data remains limited. Early CC signs tend to lack distinct characteristics, which makes their precise ...

PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.

Scientific reports
Adverse drug events represent a key challenge in public health, especially concerning drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to...

Multi-institutional validation of AI models for classifying urothelial neoplasms in digital pathology.

Scientific reports
This study proposes a deep learning approach for classifying normal, noninvasive, and invasive urothelial neoplasms via digitized histopathologicalimages. Despite many artificial intelligence (AI) models for cancer diagnosis, few focus on bladder les...

Real-time self-supervised denoising for high-speed fluorescence neural imaging.

Nature communications
Self-supervised denoising methods significantly enhance the signal-to-noise ratio in fluorescence neural imaging, yet real-time solutions remain scarce in high-speed applications. Here, we present the FrAme-multiplexed SpatioTemporal learning strateg...

Explore brain-inspired machine intelligence for connecting dots on graphs through holographic blueprint of oscillatory synchronization.

Nature communications
Neural coupling in both neuroscience and AI emerges dynamic oscillatory patterns that encode abstract concepts. To that end, we hypothesize that a deeper understanding of the neural mechanisms that determine brain rhythms could inspire next-generatio...

Evaluation of radiosensitivity for high grade gliomas patients using a multi-temporal graph convolutional networks.

Physics in medicine and biology
Assessing the efficacy of radiotherapy in patients with high-grade gliomas (HGGs) is challenging due to the occurrence of pseudo-progression and radionecrosis. This study introduces a directed graph network leveraging MR image features at multiple ti...

IV3TM: Inception V3 enabled bidirectional long short-term memory network for brain tumor classification.

PloS one
A brain tumor is one of the life-threatening neurological conditions affecting millions of people worldwide. Early diagnosis and classification of brain tumor types facilitate prompt treatment, thereby increasing the patient's chances of survival. Th...

MultiFAR: Multidimensional information fusion with attention-driven representation learning for student performance prediction.

PloS one
The advancement in computing technology, online learning platforms, and pedagogical tools enable educators and learners to connect without temporal and geographical boundaries. The existing deep learning models to predict student performance are eith...