Results 91 to 100 of about 576,901 (299)
Molecular Oncology, EarlyView.This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska +13 more
wiley +1 more source
TATS: toolbox for time series data augmentation [PDF]
PeerJ Computer ScienceAugmenting time series data plays a crucial role in enhancing the generalization of classification models, especially in scenarios where labeled datasets are limited.
Dawid Warchoł, Mariusz Oszust
doaj +2 more sources
Molecular Oncology, EarlyView.Combining osimertinib with the STING agonist ADU‐S100 activates innate and adaptive immunity to overcome the non‐inflamed microenvironment of Egfr‐mutant lung cancer. This combination increases NK and CD8+ T‐cell infiltration, associated with activation of the STING‐IRF3 pathway and local immunogenic cell death.
Jun Nishimura +19 more
wiley +1 more source
Molecular Oncology, EarlyView.The novel styrylquinazolinone‐based molecule W1B effectively suppresses glioblastoma by inhibiting IGF1R and EGFR. In high‐glucose microenvironments driving tumor resistance, W1B acts synergistically with the EGFR inhibitor dacomitinib. This combination safely blocks compensatory survival signaling in zebrafish xenograft models. Showcasing promising in
Patryk Rurka +9 more
wiley +1 more source
Crossfire: cross-domain feature integration for robust time series classification [PDF]
PeerJ Computer ScienceFeature-based time series classification (TSC) methods have traditionally relied on time-domain features, which can limit their effectiveness in capturing the full spectrum of temporal dynamics.
Celal Alagöz
doaj +2 more sources
Patient risk stratification for hospital-associated C. diff as a time-series classification task
, 2019 A patient's risk for adverse events is affected by temporal processes including the nature and timing of diagnostic and therapeutic activities, and the overall evolution of the patient's pathophysiology over time.
Horvitz, Eric +2 more
core
Multivariate time series classification with temporal abstractions [PDF]
, 2009 The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data.
Hauskrecht, M +7 more
core
Oncogenic DMTF1β promotes cancer cell motility by regulating autophagy through ULK1 stabilization
Molecular Oncology, EarlyView.In the current study, we demonstrate that the oncogene DMTF1β regulates ULK1 stability by reducing its proteasomal degradation in cancer cells. This stabilization enables ULK1 to induce autophagy, which in turn facilitates cancer cell migration. Consequently, reduced DMTF1β levels lead to decreased autophagy and impaired cancer cell migration.
Jun Xu +13 more
wiley +1 more source
Discriminant analysis of multivariate time series using wavelets [PDF]
In analyzing ECG data, the main aim is to differentiate between the signal patterns of those of healthy subjects and those of individuals with specific heart conditions.
M. Andrés Alonso, Ann Elizabeth Maharaj
core
Self-labeling techniques for semi-supervised time series classification: an empirical study [PDF]
An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data.
Triguero, Isaac +4 more
core +1 more source