AIMC Topic: Deep Learning

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Predicting protein-peptide binding residues via interpretable deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, most of them highly rely on...

Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images scalable deep learning.

World journal of gastroenterology
BACKGROUND: Hepatic steatosis is a major cause of chronic liver disease. Two-dimensional (2D) ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective.

InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol.

Bioinformatics (Oxford, England)
MOTIVATION: Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine in structural details of. As such, many computational methods have been developed to aid in peptide-protein ...

Small molecule generation via disentangled representation learning.

Bioinformatics (Oxford, England)
MOTIVATION: Expanding our knowledge of small molecules beyond what is known in nature or designed in wet laboratories promises to significantly advance cheminformatics, drug discovery, biotechnology and material science. In silico molecular design re...

Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.

International journal of epidemiology
BACKGROUND: Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep le...

Rapid and accurate identification of ribosomal RNA sequences via deep learning.

Nucleic acids research
Advances in transcriptomic and translatomic techniques enable in-depth studies of RNA activity profiles and RNA-based regulatory mechanisms. Ribosomal RNA (rRNA) sequences are highly abundant among cellular RNA, but if the target sequences do not inc...

Deep learning for signal clock and exposure estimation in rolling shutter optical camera communication.

Optics express
In rolling shutter (RS)-based optical camera communication (OCC) links, selecting the appropriate camera's exposure time is critical, as it limits the reception bandwidth. In long exposures, the pixels accumulate over time the incoming irradiance of ...

Deep Learning and Explainable Artificial Intelligence to Predict Patients' Choice of Hospital Levels in Urban and Rural Areas.

Studies in health technology and informatics
Maldistribution of healthcare resources among urban and rural areas is a significant challenge worldwide. People living in rural areas may have limited access to medical resources, and often neglect their health problems or receive insufficient care ...

Correlation Between Quality Evaluation Metrics and Teeth Detection Results in Panoramic X-Rays Using Deep Learning.

Studies in health technology and informatics
Panoramic images are one of the most requested exams by dentists for allowing the visualization of the entire mouth. Interpreting X-ray images is a time-consuming task in which misdiagnoses can occur due to the inexperience or fatigue of professional...