Results 201 to 210 of about 155,620 (242)

AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes

Advanced Intelligent Discovery, EarlyView.
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song, Xinmin Li, Hongbo Yu, Le Xiong, Guixia Liu, Weihua Li, Yun Tang   +6 more
wiley   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Clinically Informed Intelligent Classification of Ovarian Cancer Cells by Label‐Free Holographic Imaging Flow Cytometry

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone, Beatrice Cavina, Daniele Gaetano Sirico, Martina Mugnano, Vittorio Bianco, Lisa Miccio, Anna Myriam Perrone, Anna Maria Porcelli, Giuseppe Gasparre, Ivana Kurelac, Pasquale Memmolo, Pietro Ferraro   +11 more
wiley   +1 more source

Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of image quality metrics in combination with machine learning enables automatic image quality assessment for fluorescence microscopy images. The method can be integrated into the experimental pipeline for optical microscopy and utilized to classify artifacts in experimental images and to build quality rankings with a reference‐free approach ...
Elena Corbetta, Thomas Bocklitz
wiley   +1 more source

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Adjusted survival curves with inverse probability weights

Computer Methods and Programs in Biomedicine, 2004
Kaplan-Meier survival curves and the associated nonparametric log rank test statistic are methods of choice for unadjusted survival analyses, while the semiparametric Cox proportional hazards regression model is used ubiquitously as a method for covariate adjustment.
Stephen R, Cole, Miguel A, Hernán
openaire   +3 more sources

Variable selection using inverse probability of censoring weighting

Statistical Methods in Medical Research, 2023
In this article, we propose two variable selection methods for adjusting the censoring information for survival times, such as the restricted mean survival time. To adjust for the influence of censoring, we consider an inverse probability of censoring weighted for subjects with events. We derive a least absolute shrinkage and selection operator (lasso)
openaire   +2 more sources

Accounting for nonmonotone missing data using inverse probability weighting

Statistics in Medicine, 2023
Inverse probability weighting can be used to correct for missing data. New estimators for the weights in the nonmonotone setting were introduced in 2018. These estimators are the unconstrained maximum likelihood estimator (UMLE) and the constrained Bayesian estimator (CBE), an alternative if UMLE fails to converge.
Ross, Rachael K., Daniels, Julie L., Edwards, Jessie K., Stringer, Jeffrey S.A., Westreich, Daniel, Cole, Stephen R.   +5 more
openaire   +3 more sources

Behind the Numbers: Inverse Probability Weighting

Radiology, 2014
Inverse probability weighting is a propensity score-based technique that can be used to compensate for imbalance in study groups. It is an alternative to regression-based adjustment of the outcomes. It has advantages over matching of cases on the basis of propensity scores when there are more than two groups to compare, when finding matches results in ...
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Constructing inverse probability weights for institutional comparisons in healthcare

Statistics in Medicine, 2020
In comparing quality of care between hospitals, disease‐specific quality indicators measure structural, process, or outcome elements related to the care of a particular condition. Such comparisons can be framed in terms of causal contrasts, answering the question of whether a patient (or a population of patients on average) would receive different care
Thai‐Son Tang, Peter C. Austin, Keith A. Lawson, Antonio Finelli, Olli Saarela   +4 more
openaire   +3 more sources

Estimation of attributable fractions using inverse probability weighting

Statistical Methods in Medical Research, 2010
The attributable fraction is commonly used in epidemiology to quantify the impact of an exposure on a disease. Several estimation methods have been suggested in the literature, including maximum likelihood estimation. In this article we propose an additional estimation method, based on inverse probability weighting.
openaire   +2 more sources