Results 111 to 120 of about 1,144,226 (279)

Interventional Domain Adaptation

, 2020
Domain adaptation (DA) aims to transfer discriminative features learned from source domain to target domain. Most of DA methods focus on enhancing feature transferability through domain-invariance learning. However, source-learned discriminability itself might be tailored to be biased and unsafely transferable by spurious correlations, \emph{i.e ...
Wen, Jun, Shui, Changjian, Kuang, Kun, Yuan, Junsong, Huang, Zenan, Gong, Zhefeng, Zheng, Nenggan   +6 more
openaire   +2 more sources

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

Molecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova, Zuzana Kozovska, Ondrej Pos, Kristina Pavlov, Sachin Gulati, Peter Makovicky, Kristina Jakic, Monika Burikova, Eva Sedlackova, Barbora Svitkova, Silvia Tyciakova, Vojtech Bystry, Nicolas Blavet, Boris Tichy, Matej Hrnciar, Jaroslav Budis, Miroslav Tomas, Peter Dubovan, Georgina Kolnikova, Veronika Repaska, Nikoleta Mojzesova, Eva Zomborska, Daniel Pindak, Michal Mego, Tomas Szemes, Miroslava Matuskova   +25 more
wiley   +1 more source

Pseudo-Labeling Domain Adaptation Using Multi-Model Learning

IEEE Access
With the constant growth of state-of-the-art models, obtaining sufficient labeled data to train these models for specific domains has become increasingly costly.
Victor Akihito Kamada Tomita, Ricardo Marcondes Marcacini   +1 more
doaj   +1 more source

Targeted modulation of IGFL2‐AS1 reveals its translational potential in cervical adenocarcinoma

Molecular Oncology, EarlyView.
Cervical adenocarcinoma patients face worse outcomes than squamous cell carcinoma counterparts despite similar treatment. The identification of IGFL2‐AS1's differential expression provides a molecular basis for distinguishing these histotypes, paving the way for personalized therapies and improved survival in vulnerable populations globally.
Ricardo Cesar Cintra, Lucca Paolo Hsu Helmich, Daniel Rodrigues de Bastos, Laura Sichero, Patrícia Savio de Araujo‐Souza, Fabio Passetti, Luisa Lina Villa   +6 more
wiley   +1 more source

Cross-Scene Counting Based on Domain Adaptation-Extreme Learning Machine

IEEE Access, 2018
Cross-scene counting is difficult if only limited training samples are available in the new scene. In this paper, a cross-scene counting model is learned with information transferred from other scenes.
Biao Yang, Jin-Meng Cao, Nan Wang, Yu-Yu Zhang, Guo-Zeng Cui   +4 more
doaj   +1 more source

Agnostic Domain Adaptation

, 2011
The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting of transfer learning or domain adaptation: Here, training data from a source domain, aim to learn a classifier which performs well on a target domain governed by a different
Vezhnevets Alexander, Buhmann Joachim
openaire   +2 more sources

COMP–PMEPA1 axis promotes epithelial‐to‐mesenchymal transition in breast cancer cells

Molecular Oncology, EarlyView.
This study reveals that cartilage oligomeric matrix protein (COMP) promotes epithelial‐to‐mesenchymal transition (EMT) in breast cancer. We identify PMEPA1 (protein TMEPAI) as a novel COMP‐binding partner that mediates EMT via binding to the TSP domains of COMP, establishing the COMP–PMEPA1 axis as a key EMT driver in breast cancer.
Konstantinos S. Papadakos, Gilar Gorji‐bahri, Lejla Gradjan, Julia Zajac, Kacper Gil, Yvonne Thokozile Nduku, Anna M. Blom   +6 more
wiley   +1 more source

Keratin 19 as a prognostic marker and contributing factor of metastasis and chemoresistance in high‐grade serous ovarian cancer

Molecular Oncology, EarlyView.
Keratin 19 (KRT19) is overexpressed in high‐grade serous ovarian cancer with high levels of Kallikrein‐related peptidases (KLK) 4–7 and is associated with poor survival. In vivo analyses demonstrate that elevated KRT19 increases peritoneal tumour burden.
Sophia Bielesch, Isabel Vogel, Sina Nokodian, Johanna Moeller, Antonia Blechschmidt, Vicky Hecht, Vanessa Kuentzel, Katharina Thiedig, Melissa Schwab, Oliver Schilling, Holger Bronger, Marion Kiechle, Viktor Magdolen, Tobias Dreyer   +13 more
wiley   +1 more source

Hippo pathway at the crossroads of stemness and therapeutic resistance in breast cancer

Molecular Oncology, EarlyView.
Dysregulation of the Hippo pathway drives nuclear accumulation of YAP/TAZ, activating stemness‐related transcriptional programs that sustain breast cancer stemness and fuel therapeutic resistance across subtypes, underscoring Hippo signaling as a targetable vulnerability. Figure created and edited with BioRender.com.
Giulia Schiavoni, Antonella Palmese, Stefano Scalera, Laura Cipriani, Davide Mascolo, Patrizia Vici, Teresa Arcuri, Lorena Filomeno, Eriseld Krasniqi, Giovanni Blandino, Giulia Bon, Marcello Maugeri‐Saccà   +11 more
wiley   +1 more source