AIMC Topic: Deep Learning

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A Review for Artificial Intelligence Based Protein Subcellular Localization.

Biomolecules
Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein re...

Deep learning in cancer genomics and histopathology.

Genome medicine
Histopathology and genomic profiling are cornerstones of precision oncology and are routinely obtained for patients with cancer. Traditionally, histopathology slides are manually reviewed by highly trained pathologists. Genomic data, on the other han...

Deep learning model for classifying shoulder pain rehabilitation exercises using IMU sensor.

Journal of neuroengineering and rehabilitation
BACKGROUND: Artificial intelligence is being used for rehabilitation, including monitoring exercise compliance through sensor technology. AI classification of shoulder exercise wearing an IMU sensor has only been reported in normal (i.e. painless) su...

Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques.

BMC bioinformatics
The global spread of the SARS-CoV-2 pandemic, originating in Wuhan, China, has had profound consequences on both health and the economy. Traditional alignment-based phylogenetic tree methods for tracking epidemic dynamics demand substantial computati...

Slideflow: deep learning for digital histopathology with real-time whole-slide visualization.

BMC bioinformatics
Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an intera...

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.

BMC bioinformatics
BACKGROUND: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large num...

Detection of biomagnetic signals from induced pluripotent stem cell-derived cardiomyocytes using deep learning with simulation data.

Scientific reports
The detection of spontaneous magnetic signals can be used for the non-invasive electrophysiological evaluation of induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs). We report that deep learning with a dataset that combines magnetic signa...

Employing deep learning and transfer learning for accurate brain tumor detection.

Scientific reports
Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosi...

Automated analysis of knee joint alignment using detailed angular values in long leg radiographs based on deep learning.

Scientific reports
Malalignment in the lower limb structure occurs due to various causes. Accurately evaluating limb alignment in situations where malalignment needs correction is necessary. To create an automated support system to evaluate lower limb alignment by quan...

Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.

Medical physics
BACKGROUND: The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction kernel choice is important for quantitative CT-based assessment as kernel differences can lead to...