Results 101 to 110 of about 3,322,529 (303)

Loss of proton‐sensing TDAG8 increases tumor progression in mouse models of colon cancer

Molecular Oncology, EarlyView.
Loss of the pH‐sensing receptor TDAG8 accelerates colorectal cancer progression in mice. Animals lacking TDAG8 expression had increased tumor growth, DNA damage, and recruitment of tumor‐associated immune cells, including macrophages, neutrophils, and monocytes.
Ermanno Malagola, Yasmine Illi, Anna Bircher, Marijn Wilmink, Rachele F. Malagutti, Solenn Ottié, Cordelia Schuler, Pedro A. Ruiz, Cheryl de Vallière, Klaus Seuwen, Gerhard Rogler, Martin Hausmann   +11 more
wiley   +1 more source

Replication data for: Polarization and Ideology: Partisan Sources of Low-Dimensionality in Scaled Roll-Call Analyses

, 2013
In this article, we challenge the conclusion that the preferences of members of Congress are best represented as existing in a low-dimensional space. We conduct Monte Carlo simulations altering assumptions regarding the dimensionality and distribution of
Sparks, David, Montgomery, Jacob, Aldrich, John   +2 more
core   +1 more source

Proximities in dimensionality reduction

, 2022
International audienceDimensionality reduction aims at representing high-dimensional data in a lower-dimensional representation, while preserving their structure (clusters, outliers, manifold).
Journaux, Ludovic, De Bodt, Cyril, Sautot, Lucile, Bodt De, Cyril, Lee, John Aldo   +4 more
core   +1 more source

Epigenetic heterogeneity and plasticity in therapy‐induced tumor states through single‐cell multi‐omics

Molecular Oncology, EarlyView.
Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim, Hwiyeong Lee, Jin Hong, Daechan Park   +3 more
wiley   +1 more source

Multi-Label Dimensionality Reduction [PDF]

, 2011
: Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality ...

core  

Dimensionality Reduction Data Sets

, 2021
Benchmark data sets from various sources that can be used to test and compare dimensionality reduction ...
Igor Matheus Souza Moreira, Caio Marcos Flexa Rodrigues   +1 more
core   +1 more source

In vitro and in silico modelling of ROS1‐positive non‐small cell lung cancer reveals fusion‐dependent tyrosine kinase inhibitor responses

Molecular Oncology, EarlyView.
Drug resistance limits treatment success in a subset of lung cancers driven by ROS1 gene alterations. Using patient‐derived cells and computer simulations, we studied three key mutations and how they affect five targeted drugs. The mutations reduced drug effectiveness in different ways by altering protein structure and behavior.
Farhan Ul Haq, Marc Terrones, Felicia Rodrigues Fortes, Anne Schepers, Alessia Denis, Christophe Deben, Ken Op de Beeck, Guy Van Camp, Geert Vandeweyer   +8 more
wiley   +1 more source

Multi-Instance Dimensionality Reduction

, 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   +2 more sources

Dimensionality reduction of quality of life indicators

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2012
Selecting indicators for assessing the quality of life at the regional level is not unambigous. Currently, there are no precisely defined indicators that would give comprehensive information about the quality of life on a local level.
Andrea Jindrová, Julie Poláčková
doaj   +1 more source

Dimensionality reduction of massive sparse datasets using coresets

, 2019
© 2016 NIPS Foundation - All Rights Reserved. In this paper we present a practical solution with performance guarantees to the problem of dimensionality reduction for very large scale sparse matrices. We show applications of our approach to computing the
Feldman, Dan, Volkov, Mikhail, Rus, Daniela   +2 more
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