Results 121 to 130 of about 141,001 (306)
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 +3 more
wiley +1 more source
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 +8 more
wiley +1 more source
Preservation of Dissipativity in Dimensionality Reduction
Communications in Nonlinear Science and Numerical SimulationSystems with predetermined Lyapunov functions play an important role in many areas of applied mathematics, physics and engineering: dynamic optimization methods (objective functions and their modifications), machine learning (loss functions), thermodynamics and kinetics (free energy and other thermodynamic potentials), adaptive control (various ...
Sergey V. Stasenko, Alexander N. Kirdin
openaire +2 more sources
Molecular Oncology, EarlyView.BCL9 and BCL9L drive bladder cancer progression by enhancing β‐catenin signaling, promoting proliferation, migration, invasion, and organoid growth. Genetic depletion of BCL9(L) suppresses malignant phenotypes, while pharmacological disruption of the β‐catenin/BCL9(L) complex with ZW4864 inhibits canonical Wnt signaling and tumor‐associated cellular ...
Roland Kotolloshi +11 more
wiley +1 more source
Multifactor dimensionality reduction analysis summary. [PDF]
, 2016 Multifactor dimensionality reduction analysis summary.
Sydney C. D’Souza (2838479) +5 more
core +1 more source
Cascade Support Vector Machines with Dimensionality Reduction
Applied Computational Intelligence and Soft Computing, 2015 Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing.
Oliver Kramer
doaj +1 more source
Astrocyte heterogeneity in brain metastases
Molecular Oncology, EarlyView.Astrocytes emerge as pivotal regulators of metastatic colonization, survival, immune remodeling, and therapy response associated with an increasing heterogeneity that requires spatially and longitudinally resolved approaches to uncover regulatory programs and guide context‐specific therapies.
Carolina Hernández‐Oliver +2 more
wiley +1 more source
A visualization metric for dimensionality reduction [PDF]
, 2011 Data visualization of high-dimensional data is possible through the use of dimensionality reduction techniques. However, in deciding which dimensionality reduction techniques to use in practice, quantitative metrics are necessary for evaluating the ...
Tsai, Flora S., Flora S. Tsai
core +1 more source
Multiple Kernel Spectral Regression for Dimensionality Reduction
Journal of Applied Mathematics, 2013 Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples.
Bing Liu, Shixiong Xia, Yong Zhou
doaj +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source