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An overview of real‐world data sources for oncology and considerations for research

Ca-A Cancer Journal for Clinicians, 2022
Lynne Penberthy   +2 more
exaly  

Cancer statistics, 2023

Ca-A Cancer Journal for Clinicians, 2023
Rebecca L Siegel   +2 more
exaly  

Clustering in High-dimensional Data Spaces

2002
By high-dimensional we mean dimensionality of the same order as the number of objects or observations to cluster, and the latter in the range of thousands upwards. Bellman’s “curse of dimensionality” applies to many widely-used data analysis methods in high-dimensional spaces. One way to address this problem is by array permuting methods, involving row/
openaire   +1 more source

Innovations in research and clinical care using patient‐generated health data

Ca-A Cancer Journal for Clinicians, 2020
H S L Jim   +2 more
exaly  

Cancer statistics, 2020

Ca-A Cancer Journal for Clinicians, 2020
Rebecca L Siegel, Kimberly D Miller
exaly  

Clustering High-Dimensional Data

This seminar on 'Clustering High-Dimensional Data,' led by Nikolay Oskolkov from Molecular Biosciences at Lund University, provides comprehensive training in clustering techniques using R and Python. Participants will learn to manage high-dimensional datasets, apply various clustering algorithms, and evaluate their performance to enhance their research
openaire   +1 more source

High-Dimensional Data

2007
John A. Lee, Michel Verleysen
openaire   +1 more source

Cancer statistics, 2019

Ca-A Cancer Journal for Clinicians, 2019
Rebecca L Siegel   +2 more
exaly  

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