Results 271 to 280 of about 287,343 (334)
Objective Sporadic late‐onset Alzheimer's disease (AD) is characterized by a long pre‐clinical phase where amyloid‐beta (Aβ) and tau begin to accumulate in the brain. The primary objective was to determine the age at which AD starts by finding the average population age when both positron emission tomography (PET) Aβ (Aβ‐PET) and plasma Aβ42/40 become ...
Rodrigo Cánovas +29 more
wiley +1 more source
Missing Value Imputation Using Subspace Methods with Applications on Survey Data
Tommi Vatanen
openalex +1 more source
Objective The optimal treatment for distal medium vessel occlusion (DMVO) stroke remains uncertain, and evidence comparing endovascular therapy (EVT) with medical management (MM) is limited. We aimed to develop and validate a predictive modeling tool to assess individual treatment benefit in DMVO stroke using explainable counterfactual treatment ...
Mohamed F. Doheim +14 more
wiley +1 more source
Denoising autoencoder framework for reconstructing missing periodontal clinical records. [PDF]
Mathew A, Yadalam PK.
europepmc +1 more source
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International Journal of Decision Support System Technology, 2022
Many real world datasets may contain missing values for various reasons. These incomplete datasets can pose severe issues to the underlying machine learning algorithms and decision support systems. It may result in high computational cost, skewed output and invalid deductions. Various solutions exist to mitigate this issue; the most popular strategy is
openaire +1 more source
Many real world datasets may contain missing values for various reasons. These incomplete datasets can pose severe issues to the underlying machine learning algorithms and decision support systems. It may result in high computational cost, skewed output and invalid deductions. Various solutions exist to mitigate this issue; the most popular strategy is
openaire +1 more source
Imputing missing yield trial data
Theoretical and Applied Genetics, 1990The Additive Main effects and Multiplicative Interaction (AMMI) statistical model has been demonstrated effective for understanding genotype-environment interactions in yields, estimating yields more accurately, selecting superior genotypes more reliably, and allowing more flexible and efficient experimental designs. However, AMMI had required data for
H G, Gauch, R W, Zobel
openaire +2 more sources

