AIMC Topic: Deep Learning

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Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method.

Bioinformatics (Oxford, England)
MOTIVATION: The solvent accessible surface is an essential structural property measure related to the protein structure and protein function. Relative solvent accessible area (RSA) is a standard measure to describe the degree of residue exposure in t...

DeepSec: a deep learning framework for secreted protein discovery in human body fluids.

Bioinformatics (Oxford, England)
MOTIVATION: Human proteins that are secreted into different body fluids from various cells and tissues can be promising disease indicators. Modern proteomics research empowered by both qualitative and quantitative profiling techniques has made great ...

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

Bioinformatics (Oxford, England)
MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell...

[Analysis of the performance of a multi-view fusion and active contour constraint based deep learning algorithm for ossicles segmentation on 10 μm otology CT].

Zhonghua yi xue za zhi
To explore the performance of a deep learning algorithm that combined multi-view fusion with active contour constrained for ossicles segmentation on the 10 μm otology CT images. The 10 μm otology CT image data from 79 cases (56 cases were from volu...

Radiomics, deep learning and early diagnosis in oncology.

Emerging topics in life sciences
Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response prediction of cancer. To ease radiologists' task and help with challenging c...

Deep learning multi-shot 3D localization microscopy using hybrid optical-electronic computing.

Optics letters
Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical-electronic computing approach that jointly optimizes an optical encoder (a set of ...

iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization.

Bioinformatics (Oxford, England)
MOTIVATION: DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, identification of DNA methylation sites is critical for better understanding and revealing their functional me...

BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting entity relationship can greatly benefit important biomedical problems. Recently, a large amount of biomedical heterogeneous networks (BioHNs) are generated and offer opportunities for developing network-based learning approache...

Automatic improvement of deep learning-based cell segmentation in time-lapse microscopy by neural architecture search.

Bioinformatics (Oxford, England)
MOTIVATION: Live cell segmentation is a crucial step in biological image analysis and is also a challenging task because time-lapse microscopy cell sequences usually exhibit complex spatial structures and complicated temporal behaviors. In recent yea...

[Development of a deep learning based prototype artificial intelligence system for the detection of dental caries in children].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
To develop a prototype artificial intelligence image recognition system for detecting dental caries, especially those without cavities, in children. Seven hundred and twelve intraoral photos, which were taken by dental professionals using a digital...