Psoriasis

Interpretable inverse iteration mean shift networks for clustering tasks.

Neural networks have become the standard approach for tasks such as computer vision, machine translation and pattern recognition. While they exhibit significant feature representation capabilities, they often lack interpretability. This suggests that it might be beneficial to explore interpretable machine learning approaches in order to inform the neural network architectures. On the contrary, the...

Prediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning.

INTRODUCTION: High-dose methotrexate is a typical chemotherapy that is widely used in the treatment of osteosarcoma. However, the unique dose-response relationship of methotrexate makes its treatment window relatively narrow, and its clinical use is in a dilemma: either the drug concentration in the patient's body cannot reach the effective concentration level, or adverse reactions may occur due t...

Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis.

BACKGROUND: The development of medical treatments has traditionally relied on researchers leveraging scientific knowledge to hypothesize disease mecha...

A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques.

Neuroblastoma presents a wide variety of clinical phenotypes, demonstrating different levels of benignity and malignancy among its subtypes. Early dia...

Leveraging multi-modal feature learning for predictions of antibody viscosity.

The shift toward subcutaneous administration for biologic therapeutics has gained momentum due to its patient-friendly nature, convenience, reduced he...

Artificial intelligence to predict treatment response in rheumatoid arthritis and spondyloarthritis: a scoping review.

To analyse the types and applications of artificial intelligence (AI) technologies to predict treatment response in rheumatoid arthritis (RA) and spon...

Common genetic variants do not impact clinical prediction of methotrexate treatment outcomes in early rheumatoid arthritis.

BACKGROUND: Methotrexate (MTX) is the mainstay initial treatment of rheumatoid arthritis (RA), but individual response varies and remains difficult to...

Identification of novel IL17-related genes as prognostic and therapeutic biomarkers of psoriasis using comprehensive bioinformatics analysis and machine learning.

Psoriasis is a common chronic skin disorder with a polygenic background. It is widely acknowledged that Th17/IL-17A axis plays a key role in the patho...

Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study.

BACKGROUND: Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relap...

A Learnable Prior Improves Inverse Tumor Growth Modeling.

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment proto...

Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning.

Although the cerebellum is typically associated with supervised learning algorithms, it also exhibits extensive involvement in reward processing. In t...

HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-Body Mesh Recovery.

Recovering whole-body mesh by inferring the abstract pose and shape parameters from visual content can obtain 3D bodies with realistic structures. How...

An antibody developability triaging pipeline exploiting protein language models.

Therapeutic monoclonal antibodies (mAbs) are a successful class of biologic drugs that are frequently selected from phage display libraries and transg...

Modifying the severity and appearance of psoriasis using deep learning to simulate anticipated improvements during treatment.

A neural network was trained to generate synthetic images of severe and moderate psoriatic plaques, after being trained on 375 photographs of patients...

An interpretable machine learning approach for detecting psoriatic arthritis in a UK primary care psoriasis cohort using electronic health records from the Clinical Practice Research Datalink.

OBJECTIVES: Develop an interpretable machine learning model to detect patients with newly diagnosed psoriatic arthritis (PsA) in a cohort of psoriasis...

Graph-based prototype inverse-projection for identifying cortical sulcal pattern abnormalities in congenital heart disease.

Examining the altered arrangement and patterning of sulcal folds offers insights into the mechanisms of neurodevelopmental differences in psychiatric ...

IConDiffNet: an unsupervised inverse-consistent diffeomorphic network for medical image registration.

Deformable image registration (DIR) is critical in many medical imaging applications. Diffeomorphic transformations, which are smooth invertible mappi...

Plant-inspired decentralized controller for robust orientation control of soft robotic manipulators.

Due to the complexity of deformations in soft manipulators, achieving precise control of their orientation is particularly challenging, especially in ...

Simulation of human-vehicle interaction at right-turn unsignalized intersections: A game-theoretic deep maximum entropy inverse reinforcement learning method.

The safety of pedestrians in urban transportation systems has emerged as a significant research topic. As a vulnerable group within this transportatio...

Towards autonomous robotic THz-based in vivo skin sensing: the PicoBot.

Terahertz (THz) light has the unique properties of being very sensitive to water, non-ionizing, and having sub-millimeter depth resolution, making it ...