Cargando…

Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction

Multi-Instance (MI) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with multiple instances. Many studies in this literature attempted to find an appropriate Multi-Instance Learning (MIL) method for genome-wi...

Descripción completa

Detalles Bibliográficos
Autores principales:	Xu, Yonghui, Min, Huaqing, Wu, Qingyao, Song, Hengjie, Ye, Bicui
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group 2017
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5292966/ https://www.ncbi.nlm.nih.gov/pubmed/28165495 http://dx.doi.org/10.1038/srep41831

Ejemplares similares

AdaBoost Based Multi-Instance Transfer Learning for Predicting Proteome-Wide Interactions between Salmonella and Human Proteins
por: Mei, Suyu, et al.
Publicado: (2014)

Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens
por: Wu, Jian-Sheng, et al.
Publicado: (2015)

Localized instance fusion of MRI data of Alzheimer’s disease for classification based on instance transfer ensemble learning
por: Tan, Xiaoheng, et al.
Publicado: (2018)

Gene function prediction based on combining gene ontology hierarchy with multi-instance multi-label learning
por: Li, Zejun, et al.
Publicado: (2018)

Instance Transfer Learning with Multisource Dynamic TrAdaBoost
por: Zhang, Qian, et al.
Publicado: (2014)

Multi-label multi-instance transfer learning for simultaneous reconstruction and cross-talk modeling of multiple human signaling pathways
por: Mei, Suyu, et al.
Publicado: (2015)

Drug activity prediction using multiple-instance learning via joint instance and feature selection
por: Zhao, Zhendong, et al.
Publicado: (2013)

Using Multi-Instance Hierarchical Clustering Learning System to Predict Yeast Gene Function
por: Liao, Bo, et al.
Publicado: (2014)

Multi-instance learning of graph neural networks for aqueous pK(a) prediction
por: Xiong, Jiacheng, et al.
Publicado: (2021)

Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning
por: Liu, Hong, et al.
Publicado: (2022)

Enhancing unsupervised medical entity linking with multi-instance learning
por: Yan, Cheng, et al.
Publicado: (2021)

Uncertainty Ordinal Multi-Instance Learning for Breast Cancer Diagnosis
por: Xu, Xinzheng, et al.
Publicado: (2022)

Implementation of multiple-instance learning in drug activity prediction
por: Fu, Gang, et al.
Publicado: (2012)

Multi-Objective Multi-Instance Learning: A New Approach to Machine Learning for eSports
por: Birant, Kokten Ulas, et al.
Publicado: (2022)

Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning
por: Tan, Luxin, et al.
Publicado: (2023)

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification
por: Bing, Lu, et al.
Publicado: (2017)

Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer
Publicado: (2022)

Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface
por: Hossain, Ibrahim, et al.
Publicado: (2018)

Colorectal Cancer Survival Prediction Using Deep Distribution Based Multiple-Instance Learning
por: Li, Xingyu, et al.
Publicado: (2022)

Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images()
por: Li, Minglei, et al.
Publicado: (2022)

Automated skin biopsy histopathological image annotation using multi-instance representation and learning
por: Zhang, Gang, et al.
Publicado: (2013)

Pathological diagnosis and prognosis of Gastric cancer through a multi-instance learning method
por: Cao, Runnan, et al.
Publicado: (2021)

Instance Sequence Queries for Video Instance Segmentation with Transformers
por: Xu, Zhujun, et al.
Publicado: (2021)

Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning
por: Ding, Yan, et al.
Publicado: (2023)

Multi-Objective Evolutionary Instance Selection for Regression Tasks
por: Kordos, Mirosław, et al.
Publicado: (2018)

Mask then classify: multi-instance segmentation for surgical instruments
por: Kurmann, Thomas, et al.
Publicado: (2021)

Multiple instance learning of Calmodulin binding sites
por: Minhas, Fayyaz ul Amir Afsar, et al.
Publicado: (2012)

DeepLION: Deep Multi-Instance Learning Improves the Prediction of Cancer-Associated T Cell Receptors for Accurate Cancer Detection
por: Xu, Ying, et al.
Publicado: (2022)

InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection
por: Zeng, Hong, et al.
Publicado: (2020)

Deep multiple-instance learning accurately predicts gene haploinsufficiency and deletion pathogenicity
por: Liu, Zhihan, et al.
Publicado: (2023)

Instances supérieures
Publicado: (1959)

Augmenting Multi-Instance Multilabel Learning with Sparse Bayesian Models for Skin Biopsy Image Analysis
por: Zhang, Gang, et al.
Publicado: (2014)

An MRI Study on Effects of Math Education on Brain Development Using Multi-Instance Contrastive Learning
por: Zhang, Yupei, et al.
Publicado: (2021)

SY-Net: A Rice Seed Instance Segmentation Method Based on a Six-Layer Feature Fusion Network and a Parallel Prediction Head Structure
por: Ye, Sheng, et al.
Publicado: (2023)

Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images
por: Sandarenu, Piumi, et al.
Publicado: (2022)

Implicitly learning when to be ready: From instances to categories
por: Kruijne, Wouter, et al.
Publicado: (2021)

An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance
por: Jomthanachai, Suriyan, et al.
Publicado: (2023)

Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates
por: Lang, Franziska, et al.
Publicado: (2023)

Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning
por: Nieboer, Marleen M., et al.
Publicado: (2021)

Semi-Supervised Instance-Segmentation Model for Feature Transfer Based on Category Attention
por: Wang, Hao, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_rmlvg3vugk3i97quauvnimm1ef