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BiGM-lncLoc: Bi-level Multi-Graph Meta-Learning for Predicting Cell-Specific Long Noncoding RNAs Subcellular Localization.
Interdiscip Sci
December 2024
School of Information, Yunnan Normal University, Kunming, 650500, China.
The precise spatiotemporal expression of long noncoding RNAs (lncRNAs) plays a pivotal role in biological regulation, and aberrant expression of lncRNAs in different subcellular localizations has been intricately linked to the onset and progression of a variety of cancers. Computational methods provide effective means for predicting lncRNA subcellular localization, but current studies either ignore cell line and tissue specificity or the correlation and shared information among cell lines. In this study, we propose a novel approach, BiGM-lncLoc, treating the prediction of lncRNA subcellular localization across cell lines as a multi-graph meta-learning task.
View Article and Find Full Text PDFContent-Aware Few-Shot Meta-Learning for Cold-Start Recommendation on Portable Sensing Devices.
Sensors (Basel)
August 2024
School of Mathematics and Computer Science, The Zhejiang A&F University, Hangzhou 311300, China.
The cold-start problem in sequence recommendations presents a critical and challenging issue for portable sensing devices. Existing content-aware approaches often struggle to effectively distinguish the relative importance of content features and typically lack generalizability when processing new data. To address these limitations, we propose a content-aware few-shot meta-learning (CFSM) model to enhance the accuracy of cold-start sequence recommendations.
View Article and Find Full Text PDFA mutual reconstruction network model for few-shot classification of histological images: addressing interclass similarity and intraclass diversity.
Quant Imaging Med Surg
August 2024
Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, China.
Background: The automated classification of histological images is crucial for the diagnosis of cancer. The limited availability of well-annotated datasets, especially for rare cancers, poses a significant challenge for deep learning methods due to the small number of relevant images. This has led to the development of few-shot learning approaches, which bear considerable clinical importance, as they are designed to overcome the challenges of data scarcity in deep learning for histological image classification.
View Article and Find Full Text PDFAttribute-guided prototype network for few-shot molecular property prediction.
Brief Bioinform
July 2024
College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China.
The molecular property prediction (MPP) plays a crucial role in the drug discovery process, providing valuable insights for molecule evaluation and screening. Although deep learning has achieved numerous advances in this area, its success often depends on the availability of substantial labeled data. The few-shot MPP is a more challenging scenario, which aims to identify unseen property with only few available molecules.
View Article and Find Full Text PDFTackling Few-Shot Challenges in Automatic Modulation Recognition: A Multi-Level Comparative Relation Network Combining Class Reconstruction Strategy.
Sensors (Basel)
July 2024
Graduate School, Space Engineering University, Beijing 101416, China.
Automatic Modulation Recognition (AMR) is a key technology in the field of cognitive communication, playing a core role in many applications, especially in wireless security issues. Currently, deep learning (DL)-based AMR technology has achieved many research results, greatly promoting the development of AMR technology. However, the few-shot dilemma faced by DL-based AMR methods greatly limits their application in practical scenarios.
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