AIMC Topic: Deep Learning

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Developing a method for predicting DNA nucleosomal sequences using deep learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundDeep learning excels at processing raw data because it automatically extracts and classifies high-level features. Despite biology's low popularity in data analysis, incorporating computer technology can improve biological research.Objective...

Parkinson's disease prediction using improved crayfish optimization based hybrid deep learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundPredicting the course of Parkinson's disease is essential for prompt diagnosis and treatment, which may enhance patient outcomes.ObjectiveThis study presents a novel method for Parkinson's disease prediction using freely accessible resource...

Approximation of functionals on Korobov spaces with Fourier Functional Networks.

Neural networks : the official journal of the International Neural Network Society
Learning from functional data with deep neural networks has become increasingly useful, and numerous neural network architectures have been developed to tackle high-dimensional problems raised in practical domains. Despite the impressive practical ac...

Deep temporal representation learning for language identification.

Neural networks : the official journal of the International Neural Network Society
Language identification (LID) is a key component in downstream tasks. Recently, the self-supervised speech representation learned by Wav2Vec 2.0 (W2V2) has been demonstrated to be very effective for various speech-related tasks. In LID, it is commonl...

The Future of Surgical Diagnostics: Artificial Intelligence-Enhanced Detection of Ganglion Cells for Hirschsprung Disease.

Laboratory investigation; a journal of technical methods and pathology
Hirschsprung disease, a congenital disease characterized by the absence of ganglion cells, presents significant surgical challenges. Addressing a critical gap in intraoperative diagnostics, we introduce transformative artificial intelligence approach...

A Multi-task learning U-Net model for end-to-end HEp-2 cell image analysis.

Artificial intelligence in medicine
Antinuclear Antibody (ANA) testing is pivotal to help diagnose patients with a suspected autoimmune disease. The Indirect Immunofluorescence (IIF) microscopy performed with human epithelial type 2 (HEp-2) cells as the substrate is the reference metho...

Deep learning for NAD/NADP cofactor prediction and engineering using transformer attention analysis in enzymes.

Metabolic engineering
Understanding and manipulating the cofactor preferences of NAD(P)-dependent oxidoreductases, the most widely distributed enzyme group in nature, is increasingly crucial in bioengineering. However, large-scale identification of the cofactor preference...

Federated Learning for Predicting Postoperative Remission of Patients with Acromegaly: A Multicentered Study.

World neurosurgery
BACKGROUND: Decentralized federated learning (DFL) may serve as a useful framework for machine learning (ML) tasks in multicentered studies, maximizing the use of clinical data without data sharing. We aim to propose the first workflow of DFL for ML ...

Deep learning enabled integration of tumor microenvironment microbial profiles and host gene expressions for interpretable survival subtyping in diverse types of cancers.

mSystems
The tumor microbiome, a complex community of microbes found in tumors, has been found to be linked to cancer development, progression, and treatment outcome. However, it remains a bottleneck in distangling the relationship between the tumor microbiom...