Results 141 to 150 of about 4,969,023 (298)

PAC-Bayesian Learning and Domain Adaptation

, 2012
In machine learning, Domain Adaptation (DA) arises when the distribution gen- erating the test (target) data differs from the one generating the learning (source) data.
Germain, Pascal, Habrard, Amaury, Laviolette, François, Morvant, Emilie   +3 more
core   +1 more source

PARP inhibitors elicit distinct transcriptional programs in homologous recombination competent castration‐resistant prostate cancer

Molecular Oncology, EarlyView.
PARP inhibitors are used to treat a small subset of prostate cancer patients. These studies reveal that PARP1 activity and expression are different between European American and African American prostate cancer tissue samples. Additionally, different PARP inhibitors cause unique and overlapping transcriptional changes, notably, p53 pathway upregulation.
Moriah L. Cunningham, Jasibel Vasquez‐Gonzalez, Samantha M. Barnada, Salome Tchotorlishvili, Latese Jones, Ryan Maguire, Genevieve Lewis, Kinza Rizwan, Jenny Deng, Salma Koachar, Drithi Patel, Hailey Shankle, Tessa Mulders, Namra Ajmal, Charalambos Solomides, Emad S. Alnemri, Teresa F. Alnemri, Ayesha A. Shafi, Leonard G. Gomella, Wm Kevin Kelly, Steven B. McMahon, Matthew J. Schiewer   +21 more
wiley   +1 more source

Adenosine‐to‐inosine editing of miR‐200b‐3p is associated with the progression of high‐grade serous ovarian cancer

Molecular Oncology, EarlyView.
A‐to‐I editing of miRNAs, particularly miR‐200b‐3p, contributes to HGSOC progression by enhancing cancer cell proliferation, migration and 3D growth. The edited form is linked to poorer patient survival and the identification of novel molecular targets.
Magdalena Niemira, Anna Skwarska, Karolina Chwialkowska, Agnieszka Ostrowska, Gabriela Sokolowska, Anna Zeller, Anna Erol, Andrzej Eljaszewicz, Bartosz Hanczaruk, Anna Michalska‐Falkowska, Agnieszka Tarasik, Joanna Reszec‐Gielazyn, Pawel Knapp, Marcin Moniuszko, Adam Kretowski   +14 more
wiley   +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

Investigating the cell of origin and novel molecular targets in Merkel cell carcinoma: a historic misnomer

Molecular Oncology, EarlyView.
This study indicates that Merkel cell carcinoma (MCC) does not originate from Merkel cells, and identifies gene, protein & cellular expression of immune‐linked and neuroendocrine markers in primary and metastatic Merkel cell carcinoma (MCC) tumor samples, linked to Merkel cell polyomavirus (MCPyV) status, with enrichment of B‐cell and other immune cell
Richie Jeremian, Sriraam Sivachandran, Melissa Galati, Brandon Ramchatesingh, Hibo Rijal, Johnny Hanna, Elena Netchiporouk, May Chergui, Margaret Redpath, Samy Abou Setah, Ivan V. Litvinov   +10 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

PYCR1 inhibition in bone marrow stromal cells enhances bortezomib sensitivity in multiple myeloma cells by altering their metabolism

Molecular Oncology, EarlyView.
This study investigated how PYCR1 inhibition in bone marrow stromal cells (BMSCs) indirectly affects multiple myeloma (MM) cell metabolism and viability. Culturing MM cells in conditioned medium from PYCR1‐silenced BMSCs impaired oxidative phosphorylation and increased sensitivity to bortezomib.
Inge Oudaert, Lauren van den Broecke, Osman Aksoy, Judith Lind, Sonia Vallet, Arne Van der Vreken, Gamze Ates, Ann Massie, Ken Maes, Kim De Veirman, Elke De Bruyne, Karin Vanderkerken, Klaus Podar, Eline Menu   +13 more
wiley   +1 more source

Adaptaquin is selectively toxic to glioma stem cells through disruption of iron and cholesterol metabolism

Molecular Oncology, EarlyView.
Adaptaquin selectively kills glioma stem cells while sparing differentiated brain cells. Transcriptomic and proteomic analyses show Adaptaquin disrupts iron and cholesterol homeostasis, with iron chelation amplifying cytotoxicity via cholesterol depletion, mitochondrial dysfunction, and elevated reactive oxygen species.
Adrien M. Vaquié, Davod R. Shah, Eliane E. S. Brechbühl, Michael McNicholas, Zhaoyang Xu, John H. Stockley, Laura Morcom, Diana Gold Diaz, Gemma C. Girdler, Rachel V. Seear, Gabriel Balmus, Rajiv R. Ratan, Harry Bulstrode, James A. Nathan, Manav Pathania, Kevin M. Brindle, David H. Rowitch   +16 more
wiley   +1 more source

Survivin and Aurora Kinase A control cell fate decisions during mitosis

Molecular Oncology, EarlyView.
Aurora A interacts with survivin during mitosis and regulates its centromeric role. Loss of Aurora A activity mislocalises survivin, the CPC and BubR1, leading to disruption of the spindle checkpoint and triggering premature mitotic exit, which we refer to as ‘mitotic slippage’.
Hana Abdelkabir, Shalitha Wickrama Arachchige, Sally P. Wheatley   +2 more
wiley   +1 more source

Reduced vascular leakage correlates with breast carcinoma T regulatory cell infiltration but not with metastatic propensity

Molecular Oncology, EarlyView.
A mouse model for vascular normalization and a human breast cancer cohort were studied to understand the relationship between vascular leakage and tumor immune suppression. For this, endothelial and immune cell RNAseq, staining for vascular function, and immune cell profiling were employed.
Liqun He, Chiara Testini, Neda Hekmati, Altea Bonello, Aglaia Schiza, Emmanuel Nwadozi, Mia Phillipson, Carina Strell, Michael Welsh   +8 more
wiley   +1 more source