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 (Frontiers in Bioengineering and Biotechnology, 2021)
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 the 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 the 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 benchmark dataset and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.