Multi-aspect gene relation analysis

被引:0
|
作者
Yamakawa, H [1 ]
Maruhashi, K [1 ]
Nakao, Y [1 ]
Yamaguchi, M [1 ]
机构
[1] Fujitsu Labs Ltd, Nakahara Ku, Kawasaki, Kanagawa 2118588, Japan
来源
PACIFIC SYMPOSIUM ON BIOCOMPUTING 2005 | 2005年
关键词
D O I
暂无
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Recent progress in high-throughput screening technologies has led to the production of massive amounts data that we can use to understand biological systems. To interpret this data, biologists often need to analyze the characteristics of a set of genes by using Gene Ontology (GO) annotation. We are proposing a novel method for assisting such an analysis. Given a set of genes, the method automatically extracts several analyzing aspects in terms of a subset of genes that are attached to some related GO terms. It then creates a gene-attribute bipartite graph that highlights the aspect selected by the user according to his/her interests. We describe this method in detail and report on an experiment where the proposed method is applied to the analysis of rat kidney expression data.
引用
收藏
页码:233 / 244
页数:12
相关论文
共 50 条
  • [1] Multilingual and Multi-Aspect Hate Speech Analysis
    Ousidhoum, Nedjma
    Lin, Zizheng
    Zhang, Hongming
    Song, Yangqiu
    Yeung, Dit-Yan
    2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019): PROCEEDINGS OF THE CONFERENCE, 2019, : 4675 - 4684
  • [2] A multi-aspect framework for explainable sentiment analysis
    Prakash, V. Jothi
    Vijay, S. Arul Antran
    PATTERN RECOGNITION LETTERS, 2024, 178 : 122 - 129
  • [3] Multi-aspect data analysis in brain informatics
    Zhong, N
    PATTERN RECOGNITION AND MACHINE INTELLIGENCE, PROCEEDINGS, 2005, 3776 : 98 - 107
  • [4] Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer
    Su, Zhixiang
    Wang, Di
    Miao, Chunyan
    Cui, Lizhen
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 5, 2023, : 6533 - 6540
  • [5] Reproducibility Analysis and Enhancements for Multi-aspect Dense Retriever with Aspect Learning
    Bi, Keping
    Sun, Xiaojie
    Guo, Jiafeng
    Cheng, Xueqi
    ADVANCES IN INFORMATION RETRIEVAL, ECIR 2024, PT IV, 2024, 14611 : 194 - 209
  • [6] Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution
    Zhang, Yifan
    Yang, Fan
    Hosseinia, Marjan
    Mukherjee, Arjun
    2020 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2020), 2020, : 532 - 539
  • [7] Cross-collection Multi-aspect Sentiment Analysis
    Kaporo, Hemed
    ARTIFICIAL INTELLIGENCE METHODS IN INTELLIGENT ALGORITHMS, 2019, 985 : 107 - 118
  • [8] Multi-Aspect Dense Retrieval
    Kong, Weize
    Khadanga, Swaraj
    Li, Cheng
    Gupta, Shaleen Kumar
    Zhang, Mingyang
    Xu, Wensong
    Bendersky, Michael
    PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 3178 - 3186
  • [9] Comparison of Multi-Aspect Multi-Baseline SAR Interferometry and Multi-Aspect TomoSAR Reconstruction Results
    Schmitt, Michael
    Stilla, Uwe
    10TH EUROPEAN CONFERENCE ON SYNTHETIC APERTURE RADAR (EUSAR 2014), 2014,
  • [10] Multi-aspect Sentiment Analysis Using Domain Ontologies
    Sharma, Srishti
    Saraswat, Mala
    Dubey, Anil Kumar
    KNOWLEDGE GRAPHS AND SEMANTIC WEB, KGSWC 2022, 2022, 1686 : 263 - 276