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Machine Learning & Bioinformatics

This paper explores the limitations of using conjoint triads in protein-protein interaction (PPI) prediction within the bioinformatics domain. Key issues include focusing solely on "local" features and integrating non-interface elements, leading to frequency bias and impractical time and space complexities. The study reviews contributions from various research works while proposing strategies to overcome these challenges. Highlighting advancements in biological and engineering approaches, this work aims to enhance machine learning methods in PPI predictions.

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Machine Learning & Bioinformatics

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  1. Machine Learning & Bioinformatics Tien-Hao Chang (Darby Chang) Machine Learning & Bioinformatics

  2. PPI Prediction Machine Learning & Bioinformatics

  3. Shenet al. 2007, PNAS

  4. Yu et al. 2010, BMC Bioinformatics

  5. Any drawbacks using conjoint triads? Machine Learning & Bioinformatics

  6. Drawbacks of conjoint triads • From biology • consider only “local” features (Guoet al. 2008) • involve non-interface features (Chang et al. 2010) • From engineering • frequency bias (Yu et al. 2010) • infeasible time/space complexity (submitted) Machine Learning & Bioinformatics

  7. Solving the drawbacks • From biology • consider only “local” features engineering • involve non-interface features biology • From engineering • frequency bias engineering • infeasible time/space complexity biology Machine Learning & Bioinformatics

  8. Guoet al. 2008, Nucleic Acids Research

  9. Chang et al. 2010, BMC Bioinformatics

  10. Chang et al. 2010, BMC Bioinformatics

  11. Yu et al. 2010, BMC Bioinformatics

  12. Again any drawbacks/ideas now? Machine Learning & Bioinformatics

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