Predicting gene regulatory interactions based on spatial gene expression data and deep learning
Reverse engineering of gene regulatory networks (GRNs) is a central task in systems
biology. Most of the existing methods for GRN inference rely on gene co-expression analysis …
biology. Most of the existing methods for GRN inference rely on gene co-expression analysis …
ImPLoc: a multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images
W Long, Y Yang, HB Shen - Bioinformatics, 2020 - academic.oup.com
Motivation The tissue atlas of the human protein atlas (HPA) houses immunohistochemistry
(IHC) images visualizing the protein distribution from the tissue level down to the cell level …
(IHC) images visualizing the protein distribution from the tissue level down to the cell level …
HAMIL: Hierarchical aggregation-based multi-instance learning for microscopy image classification
Y Yang, Y Tu, H Lei, W Long - Pattern Recognition, 2023 - Elsevier
Multi-instance learning is common for computer vision tasks, especially in biomedical image
processing. Traditional methods for multi-instance learning focus on designing feature …
processing. Traditional methods for multi-instance learning focus on designing feature …
GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images
JX Hu, Y Yang, YY Xu, HB Shen - Bioinformatics, 2022 - academic.oup.com
Motivation Recognition of protein subcellular distribution patterns and identification of
location biomarker proteins in cancer tissues are important for understanding protein …
location biomarker proteins in cancer tissues are important for understanding protein …
MIGGRI: A multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images
Recent breakthrough in spatial transcriptomics has brought great opportunities for exploring
gene regulatory networks (GRNs) from a brand-new perspective. Especially, the local …
gene regulatory networks (GRNs) from a brand-new perspective. Especially, the local …
Multi-marginal contrastive learning for multi-label subcellular protein localization
Protein subcellular localization (PSL) is an important task to study human cell functions and
cancer pathogenesis. It has attracted great attention in the computer vision community …
cancer pathogenesis. It has attracted great attention in the computer vision community …
空战目标轨迹预测技术研究综述.
郭正玉， 刘浩宇， 苏雨 - Aero Weaponry, 2024 - search.ebscohost.com
约翰· 波伊德提出的OODA 理论[1r2](Or 观察, Or 判断, Dr 决策, Ar 行动) 被用于对空战中的战斗
流程进行基本描述. 随着现代空战场中的信息不断丰富, 传输速率不断提高, 传输节点不断增多 …
流程进行基本描述. 随着现代空战场中的信息不断丰富, 传输速率不断提高, 传输节点不断增多 …
Hamil: Hierarchical aggregation-based multi-instance learning for microscopy image classification
Y Tu, H Lei, W Long, Y Yang - arXiv preprint arXiv:2103.09764, 2021 - arxiv.org
Multi-instance learning is common for computer vision tasks, especially in biomedical image
processing. Traditional methods for multi-instance learning focus on designing feature …
processing. Traditional methods for multi-instance learning focus on designing feature …
[PDF][PDF] CSI 5180. Topics in Artificial Intelligence
M Turcotte - 2019 - eiti.uottawa.ca
At the fall 2019, I will be lecturing the first edition of Machine Learning for Bioinformatics
Applications. Its emphasis will primarily be on the analysis of complex biological data using …
Applications. Its emphasis will primarily be on the analysis of complex biological data using …