Results 71 to 80 of about 26,557 (311)

Modulation of Homer1 EVH1 domain internal dynamics by putative autism‐associated mutations

FEBS Letters, EarlyView.
The putative autism‐associated M65I and S97L variants of the EVH1 domain of the postsynaptic scaffold protein Homer1 do not exhibit substantial changes in their overall structure or partner binding. Both of them, but especially the M65I variant, show altered internal dynamics relative to the wild‐type domain on the μs‐ms timescale, indicated by the ...
Fanni Farkas, Brigitta Maruzs, Zsófia E. Kálmán, Tomas Klumpler, Gyula Batta, Bálint Péterfia, Zoltán Gáspári   +6 more
wiley   +1 more source

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining [PDF]

, 2022
Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data.
Samuel M., Blau, Shyam, Dwaraknath, Xiaowei, Xie, Kristin A., Persson, Mingjian, Wen   +4 more
core   +1 more source

α‐Synuclein aggregation landscape from phase separation to neurotoxic intermediates

FEBS Letters, EarlyView.
Alpha‐synuclein aggregation in Parkinson's disease involves a complex landscape of transient intermediates, including oligomers, fibrils and liquid–liquid phase separation (LLPS). A view is emerging in which LLPS maturation into solid‐like condensates may contribute to the formation of neurotoxic species.
Silvia Arino, Giuliana Fusco, Alfonso De Simone   +2 more
wiley   +1 more source

PDCNet: A Polarimetric Data-Enhanced Contrastive Learning Network for PolSAR Land Cover Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Polarimetric synthetic aperture radar (PolSAR) has rich polarization information, offering an efficient and reliable means of collecting information. However, how to effectively leverage these complex data to extract polarization features remains a key ...
Bo Ren, Chaoyue Hua, Biao Hou, Jian Lv, Chen Yang, Licheng Jiao, Jocelyn Chanussot   +6 more
doaj   +1 more source

Graph Clustering with High-Order Contrastive Learning

Entropy, 2023
Graph clustering is a fundamental and challenging task in unsupervised learning. It has achieved great progress due to contrastive learning. However, we find that there are two problems that need to be addressed: (1) The augmentations in most graph ...
Wang Li, En Zhu, Siwei Wang, Xifeng Guo
doaj   +1 more source

Modelling stem cell differentiation related processes—A practical overview for biologists

FEBS Letters, EarlyView.
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar, Xinqi Yan, Lucas Jansen Klomp, Stefano Schivo, Janine N. Post   +4 more
wiley   +1 more source

Geometric Multimodal Contrastive Representation Learning [PDF]

, 2022
Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels.
Poklukar, Petra,, Yin, Hang,, Melo, Francisco S.,, Paiva, Ana,, Vasco, Miguel,, Kragic, Danica,   +5 more
core  

Investigating Contrastive Pair Learning’s Frontiers in Supervised, Semisupervised, and Self-Supervised Learning

Journal of Imaging
In recent years, contrastive learning has been a highly favored method for self-supervised representation learning, which significantly improves the unsupervised training of deep image models. Self-supervised learning is a subset of unsupervised learning
Bihi Sabiri, Amal Khtira, Bouchra El Asri, Maryem Rhanoui   +3 more
doaj   +1 more source

An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images

Remote Sensing, 2023
Band selection (BS) is an efficacious approach to reduce hyperspectral information redundancy while preserving the physical meaning of hyperspectral images (HSIs).
Xiaorun Li, Yufei Liu, Ziqiang Hua, Shuhan Chen   +3 more
doaj   +1 more source

Contrastive Learning for Inference in Dialogue

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023
Accepted to ...
Etsuko Ishii, Yan Xu 0012, Bryan Wilie, Ziwei Ji 0001, Holy Lovenia, Willy Chung, Pascale Fung   +6 more
openaire   +2 more sources