AIMC Topic: Deep Learning

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An end-to-end deep learning method for protein side-chain packing and inverse folding.

Proceedings of the National Academy of Sciences of the United States of America
Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to ...

Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time.

Sensors (Basel, Switzerland)
With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health ...

Deep learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures.

Scientific reports
Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the treatment of cancer. Even though somatic mutations have been linked to tumorigenesis and metastasis, it is less...

Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature.

Journal of biomedical semantics
BACKGROUND: Drug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abs...

Applying deep learning to iterative screening of medium-sized molecules for protein-protein interaction-targeted drug discovery.

Chemical communications (Cambridge, England)
We combined a library of medium-sized molecules with iterative screening using multiple machine learning algorithms that were ligand-based, which resulted in a large increase of the hit rate against a protein-protein interaction target. This was demo...

Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks.

Medical physics
PURPOSE: Delineation of the clinical target volume (CTV) and organs-at-risk (OARs) is important in cervical cancer radiotherapy. But it is generally labor-intensive, time-consuming, and subjective. This paper proposes a parallel-path attention fusion...

Deep learning in computational dermatopathology of melanoma: A technical systematic literature review.

Computers in biology and medicine
Deep learning (DL) has become one of the major approaches in computational dermatopathology, evidenced by a significant increase in this topic in the current literature. We aim to provide a structured and comprehensive overview of peer-reviewed publi...

Cross-camera Performance of Deep Learning Algorithms to Diagnose Common Ophthalmic Diseases: A Comparative Study Highlighting Feasibility to Portable Fundus Camera Use.

Current eye research
PURPOSE: To compare the inter-camera performance and consistency of various deep learning (DL) diagnostic algorithms applied to fundus images taken from desktop Topcon and portable Optain cameras.

Comparing Common Retinal Vessel Caliber Measurement Software with an Automatic Deep Learning System.

Current eye research
PURPOSE: To compare the Retina-based Microvascular Health Assessment System (RMHAS) with Integrative Vessel Analysis (IVAN) for retinal vessel caliber measurement.

Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics.

European journal of radiology
PURPOSE: Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnostic dilemma in clinical practice. To improve the accuracy and efficiency of diagnosis, we evaluated the performance of deep learning and radiomics metho...