Positome: A Method for Improving Protein-Protein Interaction Quality and Prediction Accuracy

被引:0
|
作者
Dick, Kevin [1 ]
Dehne, Frank [2 ]
Golshani, Ashkan [3 ]
Green, James R. [1 ]
机构
[1] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON, Canada
[2] Carleton Univ, Sch Comp Sci, Ottawa, ON, Canada
[3] Carleton Univ, Inst Biochem, Dept Biol, Ottawa, ON, Canada
关键词
protein-protein interaction prediction; data quality; datasets; data provenance; machine learning; INTERACTION DATABASE; NETWORK; INTACT;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The progressive elucidation of positive protein-protein interactions (PPIs) as wet-lab techniques continue to improve in both throughput and precision has increased the number and quality of known PPIs across the spectrum of life. Creating high quality datasets of positive PPIs is critical for training PPI prediction algorithms and for assessing the performance of PPI detection efforts. We present the Positome, a web service to acquire sets of positive PPIs based on user-defined criteria pertaining to data provenance including interaction type, throughput level, and detection method selection in addition to filtration by multiple lines of evidence (i.e. PPIs reported by independent research groups). The Positome provides a tunable interface to obtain a specified subset of interacting PPIs from the BioGRID database. Both intra-and inter-species PPIs are supported. Using a number of model organisms, we demonstrate the trade-off between data quality and quantity, and the benefit of higher data quality on PPI prediction precision and recall. A web interface and REST web service are available at http://bioinf.sce.carleton.ca/POSITOME/.
引用
收藏
页码:162 / 169
页数:8
相关论文
共 50 条
  • [41] A Method for Predicting Protein-Protein Interaction Types
    Silberberg, Yael
    Kupiec, Martin
    Sharan, Roded
    PLOS ONE, 2014, 9 (03):
  • [42] Advances in Computational Methods for Protein-Protein Interaction Prediction
    Xian, Lei
    Wang, Yansu
    ELECTRONICS, 2024, 13 (06)
  • [43] Algorithmic approaches to protein-protein interaction site prediction
    Tristan T Aumentado-Armstrong
    Bogdan Istrate
    Robert A Murgita
    Algorithms for Molecular Biology, 10
  • [44] Protein-protein interaction prediction with correlated gene ontology
    Qian, M
    Wang, JZ
    PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS, 2005, 32 (05) : 449 - 455
  • [45] Better Link Prediction for Protein-Protein Interaction Networks
    Yuen, Ho Yin
    Jansson, Jesper
    2020 IEEE 20TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE 2020), 2020, : 53 - 60
  • [46] Prediction and characterization of protein-protein interaction networks in swine
    Wang, Fen
    Liu, Min
    Song, Baoxing
    Li, Dengyun
    Pei, Huimin
    Guo, Yang
    Huang, Jingfei
    Zhang, Deli
    PROTEOME SCIENCE, 2012, 10
  • [47] Algorithmic approaches to protein-protein interaction site prediction
    Aumentado-Armstrong, Tristan T.
    Istrate, Bogdan
    Murgita, Robert A.
    ALGORITHMS FOR MOLECULAR BIOLOGY, 2015, 10
  • [48] Active learning for human protein-protein interaction prediction
    Thahir P Mohamed
    Jaime G Carbonell
    Madhavi K Ganapathiraju
    BMC Bioinformatics, 11
  • [49] Deep learning frameworks for protein-protein interaction prediction
    Hu, Xiaotian
    Feng, Cong
    Ling, Tianyi
    Chen, Ming
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2022, 20 : 3223 - 3233
  • [50] Reciprocal Perspective for Improved Protein-Protein Interaction Prediction
    Dick, Kevin
    Green, James R.
    SCIENTIFIC REPORTS, 2018, 8