Results 61 to 70 of about 764,403 (277)
On the Use of Local Assessments for Monitoring Centrally Reviewed Endpoints with Missing Data in Clinical Trials* [PDF]
, 2015 Due to ethical and logistical concerns it is common for data monitoring committees to periodically monitor accruing clinical trial data to assess the safety, and possibly efficacy, of a new experimental treatment. When formalized, monitoring is typically
Brummel, Sean S., Gillen, Daniel L.
core +1 more source
Exact neutrosophic analysis of missing value in augmented randomized complete block design
Complex & Intelligent Systems, 2023 AbstractThe augmented randomized complete block design (ARCBD) is widely used in plant breeding programs to screen numerous new treatments. The error variance is estimated based on the replicated control treatments run over a randomized complete block design and is used to test the new treatments that are administrated each once in the extended units ...
Abdulrahman AlAita, Hooshang Talebi
openaire +2 more sources
AAA+ protein unfoldases—the Moirai of the proteome
FEBS Letters, EarlyView.AAA+ unfoldases are essential molecular motors that power protein degradation and disaggregation. This review integrates recent cryo‐electron microscopy (cryo‐EM) structures and single‐molecule biophysical data to reconcile competing models of substrate translocation.
Stavros Azinas, Marta Carroni
wiley +1 more source
SAGE Open, 2018 Missing Not at Random (MNAR) data present challenges for the social sciences, especially when combined with Missing Completely at Random (MCAR) data for dichotomous test items.
Celeste Combrinck +3 more
doaj +1 more source
Effect of Missing Data Types and Imputation Methods on Supervised Classifiers: An Evaluation Study
Big Data and Cognitive Computing, 2023 Data completeness is one of the most common challenges that hinder the performance of data analytics platforms. Different studies have assessed the effect of missing values on different classification models based on a single evaluation metric, namely ...
Menna Ibrahim Gabr +2 more
doaj +1 more source
An Investigation of Missing Data Methods for Classiffcation Trees [PDF]
, 2006 There are many different missing data methods used by classification tree algorithms, but few studies have been done comparing their appropriateness and performance.
Ding, Yufeng, Simonoff, Jeffrey S.
core
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion
CoRRWe study transfer learning for matrix completion in a Missing Not-at-Random (MNAR) setting that is motivated by biological problems. The target matrix $Q$ has entire rows and columns missing, making estimation impossible without side information. To address this, we use a noisy and incomplete source matrix $P$, which relates to $Q$ via a feature shift ...
Akhil Jalan +4 more
openaire +2 more sources
pH‐mediated activation of the lysosomal arginine sensor SLC38A9
FEBS Letters, EarlyView.Cells monitor nutrient levels via the lysosomal transporter SLC38A9 to activate the mechanistic target of rapamycin complex 1 (mTORC1). This study reveals that SLC38A9 function is regulated by pH. We identified histidine 544 as a critical pH sensor that undergoes conformational changes to control amino acid efflux from lysosomes; therefore, it ...
Xuelang Mu, Ampon Sae Her, Tamir Gonen
wiley +1 more source
The human gut microbiome across the life course
FEBS Letters, EarlyView.Despite significant individual variation and continuous change throughout life, the human gut microbiome follows some life stage‐specific trends. This article provides a brief overview of how gut microbiome composition shifts across different phases of life. Created in BioRender. Özkurt, E. (2026) https://BioRender.com/8q4nrnc.
Alise J. Ponsero +4 more
wiley +1 more source
PERBANDINGAN MEKANISME DATA HILANG PADA MODEL NORMAL [PDF]
, 2009 Data hilang merupakan sutu fenomena yang umum terjadi dalam penelitian survei atau experimental, berdasarkan fakta tersebut berbagai metode statistika dikembangkan untuk mengatasinya.
Yadi, Suprijadi, Zulhanif, Zulhanif
core