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ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs.
Al-Saggaf, Ubaid M; Usman, Muhammad; Naseem, Imran; Moinuddin, Muhammad; Jiman, Ahmad A; Alsaggaf, Mohammed U; Alshoubaki, Hitham K; Khan, Shujaat.
Afiliación
  • Al-Saggaf UM; Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Usman M; Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Naseem I; Department of Computer Engineering, Chosun University, Gwangju, South Korea.
  • Moinuddin M; Research and Development, Love For Data, Karachi, Pakistan.
  • Jiman AA; School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, WA, Australia.
  • Alsaggaf MU; College of Engineering, Karachi Institute of Economics and Technology, Korangi Creek, Karachi, Pakistan.
  • Alshoubaki HK; Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Khan S; Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia.
Front Bioeng Biotechnol ; 9: 752658, 2021.
Article en En | MEDLINE | ID: mdl-34722479
Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab-based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Bioeng Biotechnol Año: 2021 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Suiza
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Bioeng Biotechnol Año: 2021 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Suiza