AIMC Topic: Neural Networks, Computer

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Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?

Bioinformatics (Oxford, England)
MOTIVATION: Interpretable deep learning (DL) models that can provide biological insights, in addition to accurate predictions, are of great interest to the biomedical community. Recently, interpretable DL models that incorporate signaling pathways ha...

Optimization of water reuse and modelling by saline composition with nanoparticles based on machine learning architectures.

Water science and technology : a journal of the International Association on Water Pollution Research
Water is a necessary resource that enables the existence of all life forms, including humans. Freshwater usage has become increasingly necessary in recent years. Facilities for treating seawater are less dependable and effective. Deep learning method...

Mutate and observe: utilizing deep neural networks to investigate the impact of mutations on translation initiation.

Bioinformatics (Oxford, England)
MOTIVATION: The primary regulatory step for protein synthesis is translation initiation, which makes it one of the fundamental steps in the central dogma of molecular biology. In recent years, a number of approaches relying on deep neural networks (D...

GraphscoreDTA: optimized graph neural network for protein-ligand binding affinity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Computational approaches for identifying the protein-ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein-ligand binding affinity an...

Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function.

Bioinformatics (Oxford, England)
MOTIVATION: With the great number of peptide sequences produced in the postgenomic era, it is highly desirable to identify the various functions of therapeutic peptides quickly. Furthermore, it is a great challenge to predict accurate multi-functiona...

Clinical Impact of Deep Learning Reconstruction in MRI.

Radiographics : a review publication of the Radiological Society of North America, Inc
Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning reconstruction (DLR) has recently emerged as a technology used in the image reconstruction process of MRI, which is an essential procedure in generating MR imag...

Association of Biomarker-Based Artificial Intelligence With Risk of Racial Bias in Retinal Images.

JAMA ophthalmology
IMPORTANCE: Although race is a social construct, it is associated with variations in skin and retinal pigmentation. Image-based medical artificial intelligence (AI) algorithms that use images of these organs have the potential to learn features assoc...

Single Accelerometer to Recognize Human Activities Using Neural Networks.

Journal of biomechanical engineering
Exoskeletons have decreased physical effort and increased comfort in activities of daily living (ADL) such as walking, squatting, and running. However, this assistance is often activity specific and does not accommodate a wide variety of different ac...

Quantum Similarity Testing with Convolutional Neural Networks.

Physical review letters
The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous variable quantum systems. In this Lette...

Single-shot multispectral quantitative phase imaging of biological samples using deep learning.

Applied optics
Multispectral quantitative phase imaging (MS-QPI) is a high-contrast label-free technique for morphological imaging of the specimens. The aim of the present study is to extract spectral dependent quantitative information in single-shot using a highly...