AI Medical Compendium Journal:
Methods (San Diego, Calif.)

Showing 101 to 110 of 183 articles

Mouse4mC-BGRU: Deep learning for predicting DNA N4-methylcytosine sites in mouse genome.

Methods (San Diego, Calif.)
DNA N4-methylcytosine (4mC) is an important DNA modification and plays a crucial role in a variety of biological processes. Accurate 4mC site identification is fundamental to improving the understanding of 4mC biological functions and mechanisms. How...

AAPred-CNN: Accurate predictor based on deep convolution neural network for identification of anti-angiogenic peptides.

Methods (San Diego, Calif.)
Recently, deep learning techniques have been developed for various bioactive peptide prediction tasks. However, there are only conventional machine learning-based methods for the prediction of anti-angiogenic peptides (AAP), which play an important r...

Prediction of coronary heart disease based on combined reinforcement multitask progressive time-series networks.

Methods (San Diego, Calif.)
Coronary heart disease is the first killer of human health. At present, the most widely used approach of coronary heart disease diagnosis is coronary angiography, a surgery that could potentially cause some physical damage to the patients, together w...

Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes.

Methods (San Diego, Calif.)
As one of the most common post-transcriptional epigenetic modifications, N6-methyladenine (6 mA), plays an essential role in various cellular processes and disease pathogenesis. Therefore, accurately identifying 6 mA modifications is necessary for a ...

Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions.

Methods (San Diego, Calif.)
Computational prediction of drug-target interactions (DTIs) is of particular importance in the process of drug repositioning because of its efficiency in selecting potential candidates for DTIs. A variety of computational methods for predicting DTIs ...

MTGNN: Multi-Task Graph Neural Network based few-shot learning for disease similarity measurement.

Methods (San Diego, Calif.)
Similar diseases are usually caused by molecular origins or similar phenotypes. Confirming the relationship between diseases can help researchers gain a deep insight of the pathogenic mechanisms of emerging complex diseases, and improve the correspon...

WHISTLE server: A high-accuracy genomic coordinate-based machine learning platform for RNA modification prediction.

Methods (San Diego, Calif.)
The primary sequences of DNA, RNA and protein have been used as the dominant information source of existing machine learning tools, especially for contexts not fully explored by wet-experimental approaches. Since molecular markers are profoundly orch...

COVID-19 lesion detection and segmentation-A deep learning method.

Methods (San Diego, Calif.)
PURPOSE: In this paper, we utilized deep learning methods to screen the positive COVID-19 cases in chest CT. Our primary goal is to supply rapid and precise assistance for disease surveillance on the medical imaging aspect.

Graph matching survey for medical imaging: On the way to deep learning.

Methods (San Diego, Calif.)
The interest on graph matching has not stopped growing since the late seventies. The basic idea of graph matching consists of generating graph representations of different data or structures and compare those representations by searching corresponden...

Learning from imbalanced COVID-19 chest X-ray (CXR) medical imaging data.

Methods (San Diego, Calif.)
The trendy task of digital medical image analysis has been continually evolving. It has been an area of prominent and growing importance from both research and deployment perspectives. Nonetheless, it is necessary to realize that the use of algorithm...