Results 151 to 160 of about 85,695 (269)

Identifying systemic lupus erythematosus from serum proteomic profiles using machine learning and genetic risk stratification

Arthritis &Rheumatology, Accepted Article.
Objectives Proteome‐wide risk models for lupus remain underexplored. We developed classification models to identify lupus from serum proteomic profiles. Methods Lupus patients and individuals with other autoimmune diseases in the UK Biobank were included.
Mehmet Hocaoǧlu, Jishnu Das, Amr H Sawalha   +2 more
wiley   +1 more source

SHAP-Secure Hardware Agent Platform

, 2007
This paper presents a novel implementation of an embedded Java microarchitecture for secure, realtime, and multi-threaded applications. Together with the support of modern features of object-oriented languages, such as exception handling, automatic garbage collection and interface types, a general-purpose platform is established which also fits for the
Zabel, Martin, Preußer, Thomas B., Reichel, Peter, Spallek, Rainer G.   +3 more
openaire   +1 more source

Adaptive Machine Learning Framework for Optimizing the Affinity Purification of Adeno‐Associated Viral Vectors

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Adeno‐associated viral (AAV) vectors for gene therapy are becoming integral to modern medicine, providing therapeutic options for diseases once deemed incurable. Currently, viral vector purification is a critical bottleneck in the gene therapy industry, impacting product efficacy and safety as well as accessibility and cost to patients ...
Kelvin P. Idanwekhai, Shriarjun Shastry, Morgan R. Hurst, Arianna Minzoni, Eduardo Barbieri, Luke Remmler, Eugene N. Muratov, Michael A. Daniele, Stefano Menegatti, Alexander Tropsha   +9 more
wiley   +1 more source

Do Governance Structures Drive Green Building Adoption? A Machine Learning Approach With Random Forests

Business Strategy and the Environment, EarlyView.
ABSTRACT This study examines the determinants of firms' propensity to adopt green buildings in the Euro Stoxx 300 and the S&P 500 indices, during 2012–2023. Using random forest binary classifiers, we assess the relative importance of financial, sectoral, geographic, and climate governance predictors and uncover nonlinear relationships often overlooked ...
María del Carmen Valls Martínez, José Manuel Santos‐Jaén, María Estefanía Sánchez Pacheco, Fernando José Zambrano Farías   +3 more
wiley   +1 more source

Clustering of disease trajectories with explainable machine learning: A case study on postoperative delirium phenotypes. [PDF]

PLOS Digit Health
Zheng X, Allam A, Schürch M, Chen X, Komninou MA, Schüpbach R, Bartussek J, Krauthammer M.   +7 more
europepmc   +1 more source

Analysis of Ruddlesden‐Popper and Dion‐Jacobson 2D Lead Halide Perovskites Through Integrated Experimental and Computational Analysis

Battery Energy, Volume 4, Issue 2, March 2025.
Optimized ML framework for predicting RP and Dj phases in perovskite solar cells. ABSTRACT Two‐dimensional (2D) lead halide perovskites (LHPs) have captured a range of interest for the advancement of state‐of‐the‐art optoelectronic devices, highly efficient solar cells, next‐generation energy harvesting technologies owing to their hydrophobic nature ...
Basir Akbar, Kil To Chong, Hilal Tayara
wiley   +1 more source

Artificial intelligence and machine learning‐assisted digital applications for biopharmaceutical manufacturing

Biotechnology Progress, EarlyView.
Abstract Artificial intelligence and automation are no longer just buzzwords in the biopharmaceutical industry. The manufacturing of a class of biologics, comprising monoclonal antibodies, cell therapies, and gene therapies, is far more complex than that of traditional small molecule drugs.
Shyam Panjwani, Hao Wei, John Mason
wiley   +1 more source

Machine Learning‐Driven Prediction and Optimization of Cu‐Based Catalysts for CO2 Hydrogenation to Methanol

Carbon and Hydrogen, EarlyView.
A machine‐learning framework integrating multimodel prediction, feature selection, and SHAP interpretability is developed to uncover structure–performance relationships of Cu‐based CO2‐to‐methanol catalysts. The optimized XGBoost model and an online prediction platform enable accurate selectivity prediction and data‐driven catalyst design.
Conglong Su, Zhangkai Jing, Qiuming Zhou, Yuan Zhu, Sishi Ye, Tianye Wang, Kuo Chen, Xuebin Luan, Zhigang Tang, Run Xu, Chaopeng Hou, Hongwei Li   +11 more
wiley   +1 more source

Computational and Machine‐Learning Studies of Ethylene Oligomerization

Carbon and Hydrogen, EarlyView.
This review focuses on recent advances in computational and machine‐learning studies of ethylene oligomerization, highlighting mainstream catalyst systems based on Co, Ta, Ti, Zr, and Hf, with particular emphasis on Fe‐ and Cr‐based catalysts and their controlling factors governing reactivity and LAO distribution.
Zhixin Qin, Zhihui Song, Heying Feng, Li Jin   +3 more
wiley   +1 more source

A Critical Review on Catalytic Regeneration of Amine Solutions for Energy‐Efficient CO2 Capture

Carbon and Hydrogen, EarlyView.
This review summarizes recent progress in acid‐catalyzed regeneration of CO2‐rich amine solutions. It highlights catalyst performance, mechanisms, and data‐driven design, aiming to bridge fundamental research with engineering practice for energy‐efficient carbon capture.
Qiyue Zhao, Chuanlei Liu, Peicheng Li, Yousheng Zhou, Benxian Shen, Hui Sun   +5 more
wiley   +1 more source