Results 121 to 130 of about 8,838,426 (323)

Interpreting the Black Box: Interpretable Machine Learning and Systems Pharmacology in Small-Molecule Therapeutics

Pharmaceutics
Small-molecule drug development faces high attrition rates driven by complex pharmacokinetics and unforeseen toxicities. While deep learning offers high predictive accuracy, its opaque “black-box” nature hinders mechanistic transparency, clinical trust ...
Huan Zhang, Yangyang Wang, Jihan Wang, Hui Li   +3 more
doaj   +1 more source

Extending Service Beyond Active Duty: Generalizability of Rheumatoid Arthritis Research Conducted Among US Veterans

Arthritis Care &Research, EarlyView.
Biomedical research involving US veterans continues to advance health care beyond the Veterans Health Administration. This is particularly true in rheumatoid arthritis (RA), where veteran‐centric research has uncovered novel insights into pathogenesis, risk factors, and disease manifestations, informing clinical care and research across both veteran ...
Austin M. Wheeler, Joshua F. Baker, Jennifer L. Barton, Maria I. Danila, Tate M. Johnson, Kamini Kuchinad, Kaleb Michaud, Amir A. Razmjou, J. Steuart Richards, Namrata Singh, Isaac D. Smith, Katherine D. Wysham, Grant W. Cannon, Bryant R. England, Amy Joseph, Deana Lazaro, Katherine P. Liao, Courtney Loecker, Andreas Reimold, Beth I. Wallace, Ted R. Mikuls   +20 more
wiley   +1 more source

Data quality in causal machine learning with applications to algorithmic fairness [PDF]

The success of modern machine learning methods can be attributed to three main factors: i) The availability of increasing amounts of high quality data, ii) the sustained growth in computational resources, iii) the invention of algorithms that can reap ...
Fawkes, Jake Howarth
core   +1 more source

Machine learning-causal inference based on multi-omics data reveals the association of altered gut bacteria and bile acid metabolism with neonatal jaundice

Gut Microbes
Early identification of neonatal jaundice (NJ) appears to be essential to avoid bilirubin encephalopathy and neurological sequelae. The interaction between gut microbiota and metabolites plays an important role in early life.
Wanling Chen, Peng Zhang, Xueli Zhang, Tiantian Xiao, Jianhai Zeng, Kaiping Guo, Huixian Qiu, Guoqiang Cheng, Zhangxing Wang, Wenhao Zhou, Shujuan Zeng, Mingbang Wang   +11 more
doaj   +1 more source

Causal Machine Learning: A Deductive–Inductive Framework for Sociological Research

KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie
Causal explanation is central to sociological research, shaping both theoretical development and empirical inquiry. This paper argues that causal machine learning—which integrates deductive identification strategies with inductive estimation techniques ...
Nanum Jeon, Jennie E. Brand
semanticscholar   +1 more source

Unsupervised Machine Learning Identifies Shared Biomechanical and Clinical Variation in Individuals with Knee Osteoarthritis and High BMI: A Secondary Analysis of the IDEA Trial

Arthritis Care &Research, Accepted Article.
Objective High body mass index (BMI≥27 kg/m2) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and ...
Ashley N. Buck, J. Elyse Borgert, Hyeon Lee, Liubov Arbeeva, Yvonne M. Golightly, Richard F. Loeser, Leigh F. Callahan, Stephen P. Messier, Brian Pietrosimone, Amanda E. Nelson, James S. Marron   +10 more
wiley   +1 more source

Causality, Machine Learning, and Feature Selection: A Survey

Sensors
Causality, which involves distinguishing between cause and effect, is essential for understanding complex relationships in data. This paper provides a review of causality in two key areas: causal discovery and causal inference.
Asmae Lamsaf, Rui Carrilho, João C. Neves, Hugo Proença   +3 more
doaj   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Elements of Causal Inference [PDF]

, 2017
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and ...
Janzing, Dominik, Peters, Jonas, Schölkopf, Bernhard   +2 more
core  

Improving Outcomes in Machine Learning and Data-Driven Learning Systems using Structural Causal Models [PDF]

, 2023
The field of causal inference has experienced rapid growth and development in recent years. Its significance in addressing a diverse array of problems and its relevance across various research and application domains are increasingly being acknowledged ...
Mbogu, Henry Maduka
core