Results 51 to 60 of about 1,256,985 (257)

Dammarenediol II enhances etoposide‐induced apoptosis by targeting O‐GlcNAc transferase and Akt/GSK3β/mTOR signaling in liver cancer

Molecular Oncology, EarlyView.
Etoposide induces DNA damage, activating p53‐dependent apoptosis via caspase‐3/7, which cleaves PARP1. Dammarenediol II enhances this apoptotic pathway by suppressing O‐GlcNAc transferase activity, further decreasing O‐GlcNAcylation. The reduction in O‐GlcNAc levels boosts p53‐driven apoptosis and influences the Akt/GSK3β/mTOR signaling pathway ...
Jaehoon Lee, Byung‐Cheol Han, Gi‐Bang Koo, Jihye Park, Mijin Kwon, Young Bin Park, Jae‐Mun Choi, Seung‐Ho Lee, Sangho Roh   +8 more
wiley   +1 more source

Hierarchical multi-instance multi-label learning for Chinese patent text classification

Connection Science
To further enhance the accuracy of the Chinese patent classification, this paper proposes a model, based on the patent structure and takes the patent claim as subjects, with multi-instance multi-label learning as the main method.
Yunduo Liu, Fang Xu, Yushan Zhao, Zichen Ma, Tengke Wang, Shunxiang Zhang, Yuhao Tian   +6 more
doaj   +1 more source

Using multi-instance hierarchical clustering learning system to predict yeast gene function. [PDF]

PLoS ONE, 2014
Time-course gene expression datasets, which record continuous biological processes of genes, have recently been used to predict gene function. However, only few positive genes can be obtained from annotation databases, such as gene ontology (GO).
Bo Liao, Yun Li, Yan Jiang, Lijun Cai
doaj   +1 more source

Deep Multi-Instance Transfer Learning

, 2014
We present a new approach for transferring knowledge from groups to individuals that comprise them. We evaluate our method in text, by inferring the ratings of individual sentences using full-review ratings. This approach, which combines ideas from transfer learning, deep learning and multi-instance learning, reduces the need for laborious human ...
Kotzias, Dimitrios, Denil, Misha, Blunsom, Phil, de Freitas, Nando   +3 more
openaire   +2 more sources

Learning Interpretable Rules for Multi-label Classification

, 2018
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously.
A Gabriel, AA Freitas, AJ Knobbe, B Liu, B Minnaert, D Malerba, E Gibaja, E Gibaja, E Loza Mencía, E Montañés, F Charte, F Herrera, F Janssen, F Thabtah, G Bosc, G Tsoumakas, Grigorios Tsoumakas, H Allahyari, J Arunadevi, J Demšar, J Fürnkranz, J Han, J Hipp, J Read, JN Sulzmann, K Dembczyński, K Dembczyński, L Chekina, L Raedt De, LE Sucar, M Atzmüller, M Beckerle, M Friedman, M Zhang, Miltiadis Allamanis, MR Boutell, P Kralj Novak, PJ Hayes, R Senge, RM Cameron-Jones, Shantanu Godbole, W Duivesteijn, W Waegeman, WW Cohen   +43 more
core   +1 more source

Overview of molecular signatures of senescence and associated resources: pros and cons

FEBS Open Bio, EarlyView.
Cells can enter a stress response state termed cellular senescence that is involved in various diseases and aging. Detecting these cells is challenging due to the lack of universal biomarkers. This review presents the current state of senescence identification, from biomarkers to molecular signatures, compares tools and approaches, and highlights ...
Orestis A. Ntintas, Sylvia Vagena, Pavlos Pantelis, Giorgos Theocharous, Russel Petty, Konstantinos Evangelou, Vassilis G. Gorgoulis   +6 more
wiley   +1 more source

Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis [PDF]

, 2015
Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification.
Aksoy, Selim, Gecer, Baris, Tasar, Onur, Yalcinkaya, Ozge   +3 more
core  

Applicability of mitotic figure counting by deep learning: a development and pan‐cancer validation study

FEBS Open Bio, EarlyView.
In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes, Maria X. Isaksen, Frida Julbø, Manohar Pradhan, Andreas Kleppe, Sepp De Raedt, Ole‐Johan Skrede, Turid Torheim, John Arne Nesheim, Hans Martin Mohn, Hanne A. Askautrud, Karolina Cyll, Wanja Kildal, Emma Rewcastle, Melinda Lillesand, Vebjørn Kvikstad, Emiel Janssen, Robert Jones, Odd Terje Brustugun, Bjørn Brennhovd, Erik Skaaheim Haug, Lill‐Tove Rasmussen Busund, Elin Richardsen, Sigve Andersen, Tom Dønnem, Kristina Lindemann, Gunnar Kristensen, Neil A. Shepherd, Marco Novelli, Knut Liestøl, David Kerr, Håvard E. Danielsen, Tarjei S. Hveem   +32 more
wiley   +1 more source

Person re-identification based on deep multi-instance learning [PDF]

2017 25th European Signal Processing Conference (EUSIPCO), 2017
Publication in the conference proceedings of EUSIPCO, Kos island, Greece ...
Varga, Domonkos István, Szirányi, Tamás   +1 more
openaire   +2 more sources

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

, 2018
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects
Fang, Wei, Lee, Chung-Wei, Wang, Yu-Chiang Frank, Yeh, Chih-Kuan   +3 more
core   +1 more source