TY  - JOUR
N1  - cited By 5
TI  - iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations
SP  - 12953
AV  - none
EP  - 12965
SN  - 21693536
PB  - Institute of Electrical and Electronics Engineers Inc.
N2  - In the biological systems, Acetylation is a crucial post-translational modification, prevalent in various physiological functions and pathological conditions like carcinoma and malignancies. To better understand serine acetylation, the first step is the efficient identification of the same. Although multiple large-scale in-vivo, ex-vivo, and in-vitro methods have been applied to detect serine acetylation biomarkers, these experimental methods are time-consuming and labor-intensive. This research aims to develop an in-silico solution to supplement wetlab experiments for efficient detection of serine acetylation sites by combining Chou's Pseudo Amino Acid Composition (PseAAC) with deep neural networks (DNNs). By employing well-known DNNs for feature learning and classification of peptide sequences, our approach obsoletes the need to separately perform costly and cumbersome feature learning process. Based on performance evaluation using standard evaluation metrics, CNN and FCN based models, for AcetylSerine site identification, surpassed previously reported predictors which shows the efficacy of proposed approach. © 2013 IEEE.
KW  - Acetylation; Proteins
KW  -  Acetylserine; Amino-acids; Biological neural networks; Deep feature; Encodings; Feature learning; Neural representations; Post-translational modifications; Pseudo Amino Acid Compositions; Task analysis
KW  -  Deep neural networks
ID  - scholars17774
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123356892&doi=10.1109%2fACCESS.2022.3144226&partnerID=40&md5=9e2504b1bb721bed0046165e76dcf0ab
A1  - Naseer, S.
A1  - Fati, S.M.
A1  - Muneer, A.
A1  - Ali, R.F.
JF  - IEEE Access
VL  - 10
Y1  - 2022///
ER  -