Psoriasis

Deep fuzzy physics-informed neural networks for forward and inverse PDE problems.

As a grid-independent approach for solving partial differential equations (PDEs), Physics-Informed Neural Networks (PINNs) have garnered significant attention due to their unique capability to simultaneously learn from both data and the governing physical equations. Existing PINNs methods always assume that the data is stable and reliable, but data obtained from commercial simulation software ofte...

Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach.

Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural network (PINN) model that encodes the physics of a closed-loop blood circulation system embedding a left ventricle (LV). The PINN model is trained to satisf...

Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers.

Curated online interaction databases and gene ontology tools have streamlined the analysis of highly complex gene/protein networks. However, understan...

Spectral integrated neural networks (SINNs) for solving forward and inverse dynamic problems.

This study introduces an innovative neural network framework named spectral integrated neural networks (SINNs) to address both forward and inverse dyn...

PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers.

Approximating nonlinear differential equations using a neural network provides a robust and efficient tool for various scientific computing tasks, inc...

Image-Based Artificial Intelligence in Psoriasis Assessment: The Beginning of a New Diagnostic Era?

Psoriasis, a chronic inflammatory skin disease, affects millions of people worldwide. It imposes a significant burden on patients' quality of life and...

Inverse design of chemoenzymatic epoxidation of soyabean oil through artificial intelligence-driven experimental approach.

This paper presents an inverse design methodology that utilizes artificial intelligence (AI)-driven experiments to optimize the chemoenzymatic epoxida...

Artificial intelligence-based data extraction for next generation risk assessment: Is fine-tuning of a large language model worth the effort?

To underpin scientific evaluations of chemical risks, agencies such as the European Food Safety Authority (EFSA) heavily rely on the outcome of system...

XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG.

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the obs...

Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection.

Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathem...

Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns.

Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant programmed cell death (PCD) implicated in AML pat...

Clinical Validation of Digitally Acquired Clinical Data and Machine Learning Models for Remote Measurement of Psoriasis and Psoriatic Arthritis: A Proof-of-Concept Study.

OBJECTIVE: Psoriatic disease remains underdiagnosed and undertreated. We developed and validated a suite of novel, sensor-based smartphone assessments...

Deep learning-assisted multispectral imaging for early screening of skin diseases.

INTRODUCTION: Melanocytic nevi (MN), warts, seborrheic keratoses (SK), and psoriasis are four common types of skin surface lesions that typically requ...

AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria.

Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificia...

Assessing the response quality and readability of chatbots in cardiovascular health, oncology, and psoriasis: A comparative study.

BACKGROUND: Chatbots using the Large Language Model (LLM) generate human responses to questions from all categories. Due to staff shortages in healthc...

Binding and sensing diverse small molecules using shape-complementary pseudocycles.

We describe an approach for designing high-affinity small molecule-binding proteins poised for downstream sensing. We use deep learning-generated pseu...

Prognostic significance of migrasomes in neuroblastoma through machine learning and multi-omics.

This study explores migrasomes' role in neuroblastoma, a common malignant tumor in children, and their potential impact on tumor formation. We analyze...

Neural network aided extended Kalman filtering for inverse imaging of cardiac transmembrane potential.

The aim of this study is to address the limitations in reconstructing the electrical activity of the heart from the body surface electrocardiogram, wh...

The circadian syndrome is a better predictor for psoriasis than the metabolic syndrome via an explainable machine learning method - the NHANES survey during 2005-2006 and 2009-2014.

OBJECTIVE: To explore the association between circadian syndrome (CircS) and Metabolic Syndrome (MetS) with psoriasis. Compare the performance of MetS...

An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging.

Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the...