Lupus

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history.

BACKGROUND: Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their modes of action, identification of effective AEDs often relies on ad hoc trials, which is particularly challenging for pediatric patients. Thus, ther...

Predicting anti-trypanosome effect of carbazole-derived compounds by powerful SVM with novel kernel function and comprehensive learning PSO.

In order to predict the anti-trypanosome effect of carbazole-derived compounds by quantitative structure-activity relationship, five models were established by the linear method, random forest, radial basis kernel function support vector machine, linear combination mix-kernel function support vector machine, and nonlinear combination mix-kernel function support vector machine (NLMIX-SVM). The heur...

Reliable anti-cancer drug sensitivity prediction and prioritization.

The application of machine learning (ML) to solve real-world problems does not only bear great potential but also high risk. One fundamental challenge...

Deciphering Abnormal Platelet Subpopulations in COVID-19, Sepsis and Systemic Lupus Erythematosus through Machine Learning and Single-Cell Transcriptomics.

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and ...

Diagnosis of neuropsychiatric systemic lupus erythematosus by label-free serum microsphere-coupled SERS fingerprints with machine learning.

Surface-enhanced Raman spectroscopy (SERS) is a powerful optical technique for non-invasive and label-free bioanalysis of liquid biopsy, facilitating ...

CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer's disease compound-protein interactions prediction.

Alzheimer's disease (AD) is one of the most prevalent chronic neurodegenerative disorders globally, with a rapidly growing population of AD patients a...

Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning.

OBJECTIVE: The macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Ear...

Targeted metabolomics combined with machine learning to identify and validate new biomarkers for early SLE diagnosis and disease activity.

BACKGROUND: The early diagnosis of systemic lupus erythematosus (SLE) and the assessment of disease activity progression remain a great challenge. Tar...

A potential new way to facilitate HCV elimination: The prediction of viremia in anti-HCV seropositive patients using machine learning algorithms.

BACKGROUND AND STUDY AIMS: The present study was undertaken to design a new machine learning (ML) model that can predict the presence of viremia in he...

ACP-ESM2: The prediction of anticancer peptides based on pre-trained classifier.

Anticancer peptides (ACPs) are a type of protein molecule that has anti-cancer activity and can inhibit cancer cell growth and survival. Traditional c...

Predictive modeling of co-infection in lupus nephritis using multiple machine learning algorithms.

This study aimed to analyze peripheral blood lymphocyte subsets in lupus nephritis (LN) patients and use machine learning (ML) methods to establish an...

DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning.

Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph...

Prediction of anti-cancer drug synergy based on cross-matching network and cancer molecular subtypes.

At present, anti-cancer drug synergy therapy is one of the most important methods to overcome drug resistance and reduce drug toxicity in cancer treat...

Human-multimodal deep learning collaboration in 'precise' diagnosis of lupus erythematosus subtypes and similar skin diseases.

BACKGROUND: Lupus erythematosus (LE) is a spectrum of autoimmune diseases. Due to the complexity of cutaneous LE (CLE), clinical skin image-based arti...

Stack-AAgP: Computational prediction and interpretation of anti-angiogenic peptides using a meta-learning framework.

BACKGROUND: Angiogenesis plays a vital role in the pathogenesis of several human diseases, particularly in the case of solid tumors. In the realm of c...

Face anti-spoofing with cross-stage relation enhancement and spoof material perception.

Face Anti-Spoofing (FAS) seeks to protect face recognition systems from spoofing attacks, which is applied extensively in scenarios such as access con...

Predefined-time distributed optimization and anti-disturbance control for nonlinear multi-agent system with neural network estimator: A hierarchical framework.

This paper addresses the predefined-time distributed optimization of nonlinear multi-agent system using a hierarchical control approach. Considering u...

Prediction of systemic lupus erythematosus-related genes based on graph attention network and deep neural network.

Systemic lupus erythematosus (SLE) is an autoimmune disorder intricately linked to genetic factors, with numerous approaches having identified genes l...

Deep learning model to predict lupus nephritis renal flare based on dynamic multivariable time-series data.

OBJECTIVES: To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data...

Comparison of the Capacity of Several Machine Learning Tools to Assist Immunofluorescence-Based Detection of Anti-Neutrophil Cytoplasmic Antibodies.

The success of artificial intelligence and machine learning is an incentive to develop new algorithms to increase the rapidity and reliability of medi...