AIMC Topic: Deep Learning

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Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences.

Database : the journal of biological databases and curation
The post-translational modifications occur as crucial molecular regulatory mechanisms utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post-translational modification of lysine/k residues, is linked to a variety...

Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and post-op data, excluding intraoperative information in surgical notes. Current models also usually predi...

Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants.

Frontiers in bioscience (Landmark edition)
Biotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production. Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, lig...

Development of the lyrics-based deep learning algorithm for identifying alcohol-related words (LYDIA).

Alcohol and alcoholism (Oxford, Oxfordshire)
BACKGROUND: Music is an integral part of our lives and is often played in public places like restaurants. People exposed to music that contained alcohol-related lyrics in a bar scenario consumed significantly more alcohol than those exposed to music ...

Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma.

Journal of Zhejiang University. Science. B
Hepatocellular carcinoma (HCC) is one of the most common malignancies and is a major cause of cancer-related mortalities worldwide (Forner et al., 2018; He et al., 2023). Sarcopenia is a syndrome characterized by an accelerated loss of skeletal muscl...

Automatic detection of small bowel lesions with different bleeding risks based on deep learning models.

World journal of gastroenterology
BACKGROUND: Deep learning provides an efficient automatic image recognition method for small bowel (SB) capsule endoscopy (CE) that can assist physicians in diagnosis. However, the existing deep learning models present some unresolved challenges.

Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning.

Frontiers in bioscience (Landmark edition)
BACKGROUND: The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of microRNA (miRNA) hold potential as a reliable and effective non-invasive diagnosis of PD.

Comparison of deep learning methods for the radiographic detection of patients with different periodontitis stages.

Dento maxillo facial radiology
OBJECTIVES: The objective of this study is to assess the accuracy of computer-assisted periodontal classification bone loss staging using deep learning (DL) methods on panoramic radiographs and to compare the performance of various models and layers.

Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.

Dento maxillo facial radiology
OBJECTIVES: Improved tools based on deep learning can be used to accurately number and identify teeth. This study aims to review the use of deep learning in tooth numbering and identification.

Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression.

Cancer research communications
UNLABELLED: Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking...