AIMC Topic: Deep Learning

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Accelerating Prediction of Antiviral Peptides Using Genetic Algorithm-Based Weighted Multiperspective Descriptors with Self-Normalized Deep Networks.

Journal of chemical information and modeling
The accurate prediction of antiviral peptides (AVPs) plays a crucial role in accelerating the development of peptide-based therapeutics. Despite extensive production of antiviral medications, viral diseases remain a major human health concern. AVPs h...

Interpretable Artificial Intelligence Analysis of Functional Magnetic Resonance Imaging for Migraine Classification: Quantitative Study.

JMIR medical informatics
BACKGROUND: Deep learning has demonstrated significant potential in advancing computer-aided diagnosis for neuropsychiatric disorders, such as migraine, enabling patient-specific diagnosis at an individual level. However, despite the superior accurac...

Pancancer outcome prediction via a unified weakly supervised deep learning model.

Signal transduction and targeted therapy
Accurate prognosis prediction is essential for guiding cancer treatment and improving patient outcomes. While recent studies have demonstrated the potential of histopathological images in survival analysis, existing models are typically developed in ...

Effective SMOTE boost with deep learning for IDC identification in whole-slide images.

PloS one
Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on...

ICMC: An Interpretable Cross-domain Multi-modal Classification model for grading teaching plan.

PloS one
Multi-modal classification aims to extract pertinent information from various modalities to assign labels to instances. The advent of deep neural networks has significantly advanced this task. However, the majority of current deep neural networks lac...

Uncovering key biomarkers, potential therapeutic targets and development of deep learning model in heart failure.

PloS one
Heart failure (HF) represents a significant public health concern, characterized by elevated rates of mortality and morbidity. Recent advancements in gene sequencing technologies have led to the identification of numerous genes associated with heart ...

Deep learning-driven proteomics analysis for gene annotation in the renin-angiotensin system.

European journal of pharmacology
The renin-angiotensin system (RAS) is central to cardiovascular diseases such as hypertension and cardiomyopathy, yet the functions of many RAS genes remain unclear. This study developed a multi-label deep learning model to systematically annotate RA...

Explainable Deep Learning Framework for SERS Bioquantification.

ACS sensors
Surface-enhanced Raman spectroscopy (SERS) is rapidly gaining attention as a fast and inexpensive method of biomarker quantification, which can be combined with deep learning to elucidate complex biomarker-disease relationships. Current standard prac...

Deep computer vision with artificial intelligence based sign language recognition to assist hearing and speech-impaired individuals.

Scientific reports
Sign language (SL) is a non-verbal language applied by deaf and hard-of-hearing individuals for daily communication between them. Studies in SL recognition (SLR) have recently become essential developments. The current successes present the base for ...

Reconstruction of total-body multi parametric images with shortened-duration dynamic [Ga]Ga-PSMA-11 and [Ga]Ga-FAPI-04 PET scans.

Physics in medicine and biology
The lengthy 1 h dynamic positron emission tomography (PET) scans discomfort patients, add motion artifacts, and inflate costs, highlighting the need for tech advancements to reduce scan times. Therefore, we attempted to reconstruct multi-parametric i...