Results 61 to 70 of about 141,001 (306)

Re‐Irradiation in Pediatric Diffuse Midline Glioma: A Multi‐Institutional Retrospective Study

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Children with recurrent diffuse midline gliomas (DMGs) have limited therapeutic options at recurrence. Re‐irradiation (RT2) may be used at progression, but with uncertainty about the benefit. Methods We conducted a multi‐institutional retrospective study of children aged < 18 with DMG treated at three centers (Toronto, Canada ...
Ajay Thomas Alex   +13 more
wiley   +1 more source

Dimensionality Reduction Data Sets [PDF]

open access: yes, 2021
Benchmark data sets from various sources that can be used to test and compare dimensionality reduction ...
Igor Matheus Souza Moreira   +1 more
core   +1 more source

Phase Angle as an Early Functional Biomarker of Cancer‐Related Fatigue in Pediatric Oncology: A Prospective Longitudinal Study

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Pediatric cancer remains a leading cause of morbidity and mortality worldwide, particularly in low‐and middle‐income countries. Cancer treatment may impair nutritional status, alter body composition, and exacerbate cancer‐related fatigue (CRF).
Luís Carlos Lopes‐Junior   +11 more
wiley   +1 more source

Dimensionality Reduction and Feature Selection using a Mixed-norm Penalty Function [PDF]

open access: yes, 2006
Dimensionality reduction, which is the process of mapping high-dimension patterns to lower dimension subspaces, is a key issues in enhancing the processing efficiency of high dimensional data such as hyperspectral images.
Zeng, Huiwen
core  

Proximities in dimensionality reduction [PDF]

open access: yes, 2022
International audienceDimensionality reduction aims at representing high-dimensional data in a lower-dimensional representation, while preserving their structure (clusters, outliers, manifold).
Journaux, Ludovic   +4 more
core   +1 more source

Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis

open access: yesGenome Biology, 2019
Background Dimensionality reduction is an indispensable analytic component for many areas of single-cell RNA sequencing (scRNA-seq) data analysis. Proper dimensionality reduction can allow for effective noise removal and facilitate many downstream ...
Shiquan Sun   +3 more
doaj   +1 more source

Research on Dimensionality Reduction in Network Traffic Anomaly Detection [PDF]

open access: yesJisuanji gongcheng, 2020
To implement anomaly detection for a high dimensional network with mass flow data,data dimensionality should be reduced to relieve transmission and storage burdens from the system.This paper introduces network traffic anomaly detection process and ...
CHEN Liangchen, GAO Shu, LIU Baoxu, TAO Mingfeng
doaj   +1 more source

Effectiveness of Resistance Intradialytic Exercise Compared to Aerobic Intradialytic Exercise for Patients With Chronic Kidney Disease: A Randomized Controlled Clinical Trial

open access: yesTherapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Patients with chronic kidney disease undergoing hemodialysis commonly experience reduced physical function, fatigue, poor sleep quality, and impaired health‐related quality of life. Intradialytic exercise has been proposed as a non‐pharmacological strategy to improve these outcomes.
Klebson da Silva Almeida   +6 more
wiley   +1 more source

Organoids in pediatric cancer research

open access: yesFEBS Letters, EarlyView.
Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley   +1 more source

Multi-Instance Dimensionality Reduction [PDF]

open access: yes, 2010
Multi-instance learning deals with problems that treat bags of instances as training examples. In single-instance learning problems, dimensionality reduction is an essential step for high-dimensional data analysis and has been studied for years.
Sun, Yu-Yin, Ng, Michael, Zhou, Zhi-Hua
core   +3 more sources

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