ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs release_swt2mkatnfadxo5lxdjlc6e5ne

by Ubaid M. Al-Saggaf, Muhammad Usman, Imran Naseem, Muhammad Moinuddin, Ahmad A. Jiman, Mohammed U. Alsaggaf, Hitham K. Alshoubaki, Shujaat Khan

Published in Frontiers in Bioengineering and Biotechnology by Frontiers Media SA.

2021   Volume 9, p752658


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.
In application/xml+jats format

Archived Files and Locations

application/pdf  2.6 MB
file_fg3745ujlfcb5juk5enpnoxgbm (publisher) (webarchive)

Web Captures
2022-04-18 22:20:50 | 39 resources
webcapture_o6orjzmf55dqjabgdrlmjse2ma (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2021-10-14
Language   en ?
Container Metadata
Open Access Publication
In Keepers Registry
ISSN-L:  2296-4185
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: da9c7f55-3b9f-4e61-a79e-4e44e62fb024