AIMC Topic: Algorithms

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Core Concepts in Pharmacoepidemiology: Principled Use of Artificial Intelligence and Machine Learning in Pharmacoepidemiology and Healthcare Research.

Pharmacoepidemiology and drug safety
Artificial intelligence (AI) and machine learning (ML) are important tools across many fields of health and medical research. Pharmacoepidemiologists can bring essential methodological rigor and study design expertise to the design and use of these t...

Development and Validation of Machine Learning Models for Identifying Prediabetes and Diabetes in Normoglycemia.

Diabetes/metabolism research and reviews
BACKGROUND: Prediabetes and diabetes are both abnormal states of glucose metabolism (AGM) that can lead to severe complications. Early detection of AGM is crucial for timely intervention and treatment. However, fasting blood glucose (FBG) as a mass p...

Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge.

The Lancet. Digital health
Deep learning has shown great potential to automate abdominal organ segmentation and quantification. However, most existing algorithms rely on expert annotations and do not have comprehensive evaluations in real-world multinational settings. To addre...

EuDockScore: Euclidean graph neural networks for scoring protein-protein interfaces.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions are essential for a variety of biological phenomena including mediating biochemical reactions, cell signaling, and the immune response. Proteins seek to form interfaces which reduce overall system energy. Alth...

DPNN-ac4C: a dual-path neural network with self-attention mechanism for identification of N4-acetylcytidine (ac4C) in mRNA.

Bioinformatics (Oxford, England)
MOTIVATION: The modification of N4-acetylcytidine (ac4C) in RNA is a conserved epigenetic mark that plays a crucial role in post-transcriptional regulation, mRNA stability, and translation efficiency. Traditional methods for detecting ac4C modificati...

Deep coupled registration and segmentation of multimodal whole-brain images.

Bioinformatics (Oxford, England)
MOTIVATION: Recent brain mapping efforts are producing large-scale whole-brain images using different imaging modalities. Accurate alignment and delineation of anatomical structures in these images are essential for numerous studies. These requiremen...

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.

Radiology. Imaging cancer
Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performanc...

Sitetack: a deep learning model that improves PTM prediction by using known PTMs.

Bioinformatics (Oxford, England)
MOTIVATION: Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their ana...

A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms.

JAMA ophthalmology
IMPORTANCE: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a varie...

Crowd Counting Using Meta-Test-Time Adaptation.

International journal of neural systems
Machine learning algorithms are commonly used for quickly and efficiently counting people from a crowd. Test-time adaptation methods for crowd counting adjust model parameters and employ additional data augmentation to better adapt the model to the s...