AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolution.

Bioinformatics (Oxford, England)
MOTIVATION: Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundament...

Effective and efficient active learning for deep learning-based tissue image analysis.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning attained excellent results in digital pathology recently. A challenge with its use is that high quality, representative training datasets are required to build robust models. Data annotation in the domain is labor intensive ...

DeepOM: single-molecule optical genome mapping via deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Efficient tapping into genomic information from a single microscopic image of an intact DNA molecule is an outstanding challenge and its solution will open new frontiers in molecular diagnostics. Here, a new computational method for optic...

ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.

Bioinformatics (Oxford, England)
MOTIVATION: Plant Small Secreted Peptides (SSPs) play an important role in plant growth, development, and plant-microbe interactions. Therefore, the identification of SSPs is essential for revealing the functional mechanisms. Over the last few decade...

On the effectiveness of compact biomedical transformers.

Bioinformatics (Oxford, England)
MOTIVATION: Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks. Many existing pre-trained models, on the other hand, are resource-intensive and computationally heav...

PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.

Bioinformatics (Oxford, England)
MOTIVATION: The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotatio...

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.

Bioinformatics (Oxford, England)
MOTIVATION: While traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the site-of-metabolism (SOM) is essential in analyzing the promiscuity of enzy...

ARAX: a graph-based modular reasoning tool for translational biomedicine.

Bioinformatics (Oxford, England)
MOTIVATION: With the rapidly growing volume of knowledge and data in biomedical databases, improved methods for knowledge-graph-based computational reasoning are needed in order to answer translational questions. Previous efforts to solve such challe...

A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images.

Bioinformatics (Oxford, England)
MOTIVATION: Mass Spectrometry Imaging (MSI) analyzes complex biological samples such as tissues. It simultaneously characterizes the ions present in the tissue in the form of mass spectra, and the spatial distribution of the ions across the tissue in...

MFR-DTA: a multi-functional and robust model for predicting drug-target binding affinity and region.

Bioinformatics (Oxford, England)
MOTIVATION: Recently, deep learning has become the mainstream methodology for drug-target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ign...