Results 101 to 110 of about 341,780 (320)

Rapid Proteome‐Wide Discovery of Protein–Protein Interactions With ppIRIS

Advanced Science, EarlyView.
ppIRIS is a lightweight deep learning framework for proteome‐wide protein–protein interaction prediction directly from sequence. By fusing evolutionary and structural embeddings with a regularized Siamese architecture, ppIRIS achieves state‐of‐the‐art accuracy across species, enables minute‐scale screening, and reveals biologically validated bacterial ...
Luiz Felipe Piochi, Di Tang, Johan Malmström, Yasaman Karami, Hamed Khakzad   +4 more
wiley   +1 more source

Hybrid particle swarm optimization and semi-supervised extreme learning machine for cellular network localization

International Journal of Distributed Sensor Networks, 2017
The research of localization technology based on received signal strength and machine learning has recently attracted a lot of attentions, since with the help of enough labeled training data this technology is able to achieve high positioning accuracy ...
Fagui Liu, Hengrui Qin, Xin Yang, Yi Yu
doaj   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation [PDF]

, 2021
Ankit Singh
openalex   +1 more source

Reinforcement Learning Guided Semi-Supervised Learning

Advances in Neural Information Processing Systems 37
In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce. However, most current SSL methods rely on heuristics or predefined rules for generating pseudo-labels and leveraging unlabeled data.
Heidari, Marzi, Zhang, Hanping, Guo, Yuhong   +2 more
openaire   +2 more sources

Studying Macromolecular Composition in Cell–Cell Interfaces Using 3D Membrane Reconstitution Systems

Advanced Science, EarlyView.
A comprehensive understanding of the interactions between proteins, lipids and glycocalyx components at the immune synapse is still lacking. Here, an artificial and a semi‐artificial model contact system were established to reconstitute the cell‐cell contact in 3D. The model systems enable the examination of macromolecule enrichment or depletion at the
Franziska Ragaller, Amelie Maribel Schneider, Ellen Sjule, Renhua Sun, Xiao Han, Luca Andronico, Edward Jenkins, Michael Dustin, Adnane Achour, Erdinc Sezgin   +9 more
wiley   +1 more source

A semi-supervised learning detection method for vision-based monitoring of construction sites by integrating teacher-student networks and data augmentation

, 2021
Bo Xiao, Yuxuan Zhang, Yuan Chen, Xianfei Yin   +3 more
openalex   +1 more source

Pierceable, Water‐Resistant, and Transparent Nanofilm Electrodes Comprising Carbon Nanotubes for Long‐Term Monitoring of Plant Electrophysiology

Advanced Science, EarlyView.
Ultra‐flexible electrodes composed of single‐walled carbon nanotubes on a polymeric substrate exhibit excellent transparency, water resistance, and conformability to hairy surfaces. These non‐invasive nanofilms are easily pierced by trichomes, overcoming the structural barriers in plant electrophysiology.
Yusuke Hori, Tatsuhiro Horii, Shinji Masuda, Toshinori Fujie   +3 more
wiley   +1 more source

INB3P: A Multi‐Modal and Interpretable Co‐Attention Framework Integrating Property‐Aware Explanations and Memory‐Bank Contrastive Fusion for Blood–Brain Barrier Penetrating Peptide Discovery

Advanced Science, EarlyView.
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv, Qianyang Wu, Jian Liu, Binlu Yang, Yuanhao Li, Junlin Xu, Yajie Meng, Leyi Wei, Zilong Zhang, Quan Zou, Xiulai Li, Feifei Cui   +11 more
wiley   +1 more source

Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network [PDF]

, 2021
Jiarui Chen, Yain‐Whar Si, Chon-Wai Un, Shirley W. I. Siu   +3 more
openalex   +1 more source