AI Medical Compendium Topic

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VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Immunogenetics
Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/ ) is an integrated pipeline designed to enhance epitope prediction and prioritization. This study a...

Design and validation of Withings ECG Software 2, a tiny neural network based algorithm for detection of atrial fibrillation.

Computers in biology and medicine
BACKGROUND: Atrial Fibrillation (AF) is the most common form of arrhythmia in the world with a prevalence of 1%-2%. AF is also associated with an increased risk of cardiovascular diseases (CVD), such as stroke, heart failure, and coronary artery dise...

Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools.

Critical care (London, England)
BACKGROUND: Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic informati...

AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data.

Scientific reports
AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these...

MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras.

Journal of computer-aided molecular design
Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecular descriptors or fingerprints. Since relatively recently, graph neural network (GNN) algorithms hav...

geodl: An R package for geospatial deep learning semantic segmentation using torch and terra.

PloS one
Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are es...

Evaluation of Responses to Questions About Keratoconus Using ChatGPT-4.0, Google Gemini and Microsoft Copilot: A Comparative Study of Large Language Models on Keratoconus.

Eye & contact lens
OBJECTIVES: Large language models (LLMs) are increasingly being used today and are becoming increasingly important for providing accurate clinical information to patients and physicians. This study aimed to evaluate the effectiveness of generative pr...

MIRD Pamphlet No. 31: MIRDcell V4-Artificial Intelligence Tools to Formulate Optimized Radiopharmaceutical Cocktails for Therapy.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because 1 radiopharmaceutical is unlikely to have the desired therapeutic effect because of nonuniform uptake by the targeted cells. ...

MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.

Genome biology
We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction terms (energetic couplings) for specified biophysical models, including from multimodal phenotypic data...

Denoising low-field MR images with a deep learning algorithm based on simulated data from easily accessible open-source software.

Journal of magnetic resonance (San Diego, Calif. : 1997)
In this study, we introduce a denoising method aimed at improving the contrast ratio in low-field MRI (LFMRI) using an advanced 3D deep convolutional residual network model. Our approach employs synthetic brain imaging datasets that closely mimic the...