- Self|supervised learning for macromolecular structure classification ...🔍
- Complementary multi|modality molecular self|supervised learning ...🔍
- Towards unsupervised classification of macromolecular complexes ...🔍
- Self|supervised Learning and Graph Classification under Heterophily🔍
- Active learning to classify macromolecular structures in situ for less ...🔍
- Few|shot learning for classification of novel macromolecular ...🔍
- Structure|aware protein self|supervised learning🔍
- Self|supervised learning for macromolecular structure ...🔍
Self|supervised learning for macromolecular structure classification ...
Self-supervised learning for macromolecular structure classification ...
In this work, we use Contrastive Self-supervised Learning (CSSL) to improve the previous approaches for macromolecular structure classification from cryo-ET ...
Self-supervised learning for macromolecular structure classification ...
However, a major limitation has been insufficient labelled cryo-ET data. In this work, we use Contrastive Self-supervised Learning (CSSL) to ...
Self-supervised learning for macromolecular structure classification ...
In this work, we use Contrastive Self-supervised Learning (CSSL) to improve the previous approaches for macromolecular structure classification ...
Self-supervised learning for macromolecular structure classification ...
Motivation Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at ...
Self-supervised learning for macromolecular structure classification ...
Recently, deep learning methods have demonstrated promising performance in classification and segmentation of macromolecule structures captured ...
Self-supervised learning for macromolecular structure classification ...
This work designs a cryo-ET domain-specific data-augmentation pipeline that first pretrain an encoder with unlabelled data using CSSL and then fine-tune the ...
Complementary multi-modality molecular self-supervised learning ...
Abstract. Self-supervised learning plays an important role in molecular representation learning because labeled molecular data are usually ...
Towards unsupervised classification of macromolecular complexes ...
Recently, supervised deep learning methods have been applied to decipher the 3D spatial distribution of macromolecules. However, in order to discover unknown ...
MiLoPYP: self-supervised molecular pattern mining and particle ...
Through this training process, MiLoPYP learns to embed macromolecules with shared structural similarities close to each other while placing ...
Self-supervised Learning and Graph Classification under Heterophily
On the other hand, it is still unclear how to effectively capture the structural pattern of graphs and how to measure the capability of the self ...
Active learning to classify macromolecular structures in situ for less ...
Deep learning-based subtomogram classification has played critical roles for such tasks. As supervised approaches, however, their performance relies on ...
Few-shot learning for classification of novel macromolecular ...
However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), ...
Towards unsupervised classification of macromolecular complexes ...
The field of view and the resolution are both large enough to enable a joint study of the cellular context and structures. ... self-supervised ...
Structure-aware protein self-supervised learning - Jingbo Zhou
We conduct experiments on three downstream tasks: the binary classification into membrane/non-membrane proteins, the location classification.
Self-supervised learning for macromolecular structure ... - Altmetric
Self-supervised learning for macromolecular structure classification based on cryo-electron tomograms · Mentioned by · Citations · Readers on.
Active Learning to Classify Macromolecular Structures in situ for ...
Supervised deep learning based methods have also been proposed for 3D subtomogram classification task thanks to its high-throughput processing capability.
Self-supervised deep learning encodes high-resolution features of ...
Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering.
FSCC: Few-Shot Learning for Macromolecule Classification Based ...
Although existing supervised deep learning–based methods have improved classification accuracy, such trained models have limited ability to classify novel ...
Self-supervised learning of materials concepts from crystal ...
Here we show that our self-supervised deep learning approach can successfully learn material embeddings from crystal structures of over 120 000 ...
[PDF] Active Learning to Classify Macromolecular Structures in situ ...
A Hybrid Active Learning (HAL) framework for querying subtomograms for labelling from a large unlabeled subtomogram pool and can achieve comparable testing ...