Results 81 to 90 of about 228,260 (299)

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

Large‐scale bidirectional arrayed genetic screens identify OXR1 and EMC4 as modifiers of αSynuclein aggregation

FEBS Open Bio, EarlyView.
Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane, Lea Nikolić, Lorenzo Maraio, Thomas Goiran, Nathan Karpilovsky, Stefano Sellitto, Vangelis Bouris, Jiang‐An Yin, Ronald Melki, Edward A Fon, Adriano Aguzzi, Elena De Cecco   +11 more
wiley   +1 more source

Enhanced Network Representation Learning With Community Aware and Relational Attention

IEEE Access, 2020
Network representation learning is proposed to make it easier to perform complex inference processes on large-scale networks. It aims to represent each node in the network as a low-dimensional potential representation while preserving the structure and ...
Mingqiang Zhou, Dan Liu, Yihan Kong, Haijiang Jin   +3 more
doaj   +1 more source

Multi-View Representation Learning via Dual Optimal Transportation

IEEE Access, 2021
Recently, multi-view representation learning has gained rapid growth in various fields. Most of previous multi-view learning methods rely on strong notions of distances that often provide no useful gradients in deep network training, which greatly ...
Peng Li, Jing Gao, Bin Zhai, Jianing Zhang, Zhikui Chen   +4 more
doaj   +1 more source

Why human connection is the true metric of research success

FEBS Open Bio, EarlyView.
Human‐centred mentorship can be shaped by mentor attributes, actions, intrinsic drive and career ambition. Drawing on reflections across Singapore and France, as well as workshop insights from FEBS‐IUBMB ENABLE 2024, this article shows that human‐centred mentorship creates the conditions for sustainable growth, well‐being and retention in research ...
Timothy Lin Yun Tan, Mattias Wei Ren Kon, Xavier Coumoul, Fun Man Fung   +3 more
wiley   +1 more source

Learning network representations

The European Physical Journal Special Topics, 2017
In this review I present several representation learning methods, and discuss the latest advancements with emphasis in applications to network science. Representation learning is a set of techniques that has the goal of efficiently mapping data structures into convenient latent spaces. Either for dimensionality reduction or for gaining semantic content,
openaire   +2 more sources

Associative Compression Networks for Representation Learning

CoRR, 2018
This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders (VAEs) in that the prior distribution used to model each code is conditioned on a similar code from the dataset.
Alex Graves, Jacob Menick, Aäron van den Oord   +2 more
openaire   +2 more sources

Emerging insights into CC and CXC chemokines and their receptors in Mycobacterium tuberculosis infection

FEBS Open Bio, EarlyView.
The dual roles of CC and CXC chemokines in distinguishing active, latent, and subclinical tuberculosis were reviewed, along with an evaluation of their potential as diagnostic biomarkers and therapeutic targets to advance precision medicine in tuberculosis management. The graphical abstract was generated with AI assistance (Gemini 3.0).
Xuying Yin, Dangsheng Xiao, Jiezuan Yang
wiley   +1 more source

Representation learning of in-degree-based digraph with rich information

Complex & Intelligent Systems
Network representation learning aims to map the relationship between network nodes and context nodes to a low-dimensional representation vector space. Directed network representation learning considers mapping directional of node vector.
Yan Sun, Cun Zhu, JianFu Chen, Kejia Lan, Jiuchang Pei   +4 more
doaj   +1 more source

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source