Results 161 to 170 of about 25,341,143 (317)

Machine Learning to Predict Remission Between 6 and 24 Months in Rheumatoid Arthritis: Insights From JAK, an International Registry Collaboration

Arthritis &Rheumatology, EarlyView.
Objective To develop, externally validate, and simplify a machine learning model to predict remission between 6 and 24 months in patients with rheumatoid arthritis (RA) initiating tumor necrosis factor inhibitors, JAK inhibitors, interleukin‐6 inhibitors, abatacept, or rituximab using data from 11 international registries in the JAK‐pot collaboration ...
Zubeyir Salis, Denis Mongin, Denis Choquette, Louis Coupal, Catalin Codreanu, Florenzo Iannone, Roberto Caporali, Tore K. Kvien, Sella Provan, Ruth Fritsch‐Stork, Dan Nordström, Nina Trokovic, Karel Pavelka, Jakub Závada, Ana Rodrigues, Ziga Rotar, Prodromos Sidiropoulos, Irini Flouri, Céline Lamacchia, Michele Iudici, Delphine Courvoisier, Kim Lauper, Axel Finckh   +22 more
wiley   +1 more source

Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming

Reviews on Advanced Materials Science
Ashraf Muhammad Waqas, Khan Adnan, Tu Yongming, Wang Chao, Ben Kahla Nabil, Javed Muhammad Faisal, Ullah Safi, Tariq Jawad   +7 more
doaj   +1 more source

Network toxicology reveals key genes of amiodarone induced pulmonary fibrosis: based on machine learning and SHAP analysis. [PDF]

Front Pharmacol
Yan X, Liu Y, Fan R.
europepmc   +1 more source

Inferring rheumatoid arthritis disease activity status from the electronic health records across health systems

Arthritis &Rheumatology, Accepted Article.
Objective Disease activity plays a central role in rheumatoid arthritis (RA) clinical studies. The inconsistent availability of data on disease activity in real‐world electronic health records (EHR) data has limited the ability to generate real‐world evidence (RWE). This study aimed to develop and validate scalable machine learning (ML) models to infer
David Cheng, Xuan Wang, Gregory C. McDermott, Jennifer S. Hanberg, Zoe Love, Katherine Zhong, Mary Jeffway, Jue Hou, Vidul Panickan, Rahul Sangar, Ying Qi, Connor Melley, Lauren Costa, Dakota Feil, Rachael Matty, Dana Weisenfeld, Abisayo Animashaun, Aimee Schreiner, Sara Morini, Lauren Rusnak, Andrew Cagan, Misti Paudel, J. Michael Gaziano, Brian Sauer, Michael Weinblatt, Joshua Baker, Bryant England, Yuk‐Lam Ho, Kelly Cho, Paul Monach, Grant W. Cannon, Nancy Shadick, Ted R. Mikuls, Tianxi Cai, Katherine P. Liao   +34 more
wiley   +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

Research on the predicted height of water-conducting fracture zones based on the BO-RFR model and SHAP analysis. [PDF]

Sci Rep
Qiu M, Wen Y, Teng C, Shi M.
europepmc   +1 more source

Addressing Small Data Challenges in Biopharmaceutical Development and Manufacturing: A Mini Review of Multi‐Fidelity Techniques

Biotechnology and Bioengineering, EarlyView.
ABSTRACT The growing demand for biopharmaceutical products reflects their effectiveness in medical treatments. However, developing new biopharmaceuticals remains a major bottleneck, often taking up to a decade before market approval. Machine learning (ML) models have the potential to accelerate this process, but their success depends on access to large
Mohammad Golzarijalal, Uwe Aickelin, Ellen Otte   +2 more
wiley   +1 more source

Network toxicology integrated with machine learning and SHAP analysis identifies overlapping immune signatures between Di(2-ethylhexyl) phthalate (DEHP) and Sjögren's syndrome. [PDF]

BMC Pharmacol Toxicol
Lili C, Zhongfu T, Ming L, Huang C.
europepmc   +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

Interpretable machine learning for cognitive impairment prediction in Parkinson's disease: a multicenter validation study with SHAP analysis. [PDF]

Front Aging Neurosci
Wang Z, Yan J.
europepmc   +1 more source