Next Article in Journal
Sustainable Water Responsive Mechanically Adaptive and Self-Healable Polymer Composites Derived from Biomass
Previous Article in Journal
Biochar as an Effective Filler of Carbon Fiber Reinforced Bio-Epoxy Composites
Previous Article in Special Issue
Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements
Open AccessArticle

MPPIF-Net: Identification of Plasmodium Falciparum Parasite Mitochondrial Proteins Using Deep Features with Multilayer Bi-directional LSTM

by Samee Ullah Khan 1 and Ran Baik 2,*
1
Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul 143-747, Korea
2
Department of Computer Engineering, Convergence School of ICT, Honam University, #417 Eodeung-daero, Gwangsan-gu, Gwangju 506-090, Korea
*
Author to whom correspondence should be addressed.
Processes 2020, 8(6), 725; https://doi.org/10.3390/pr8060725
Received: 29 April 2020 / Revised: 14 June 2020 / Accepted: 15 June 2020 / Published: 22 June 2020
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
Mitochondrial proteins of Plasmodium falciparum (MPPF) are an important target for anti-malarial drugs, but their identification through manual experimentation is costly, and in turn, their related drugs production by pharmaceutical institutions involves a prolonged time duration. Therefore, it is highly desirable for pharmaceutical companies to develop computationally automated and reliable approach to identify proteins precisely, resulting in appropriate drug production in a timely manner. In this direction, several computationally intelligent techniques are developed to extract local features from biological sequences using machine learning methods followed by various classifiers to discriminate the nature of proteins. Unfortunately, these techniques demonstrate poor performance while capturing contextual features from sequence patterns, yielding non-representative classifiers. In this paper, we proposed a sequence-based framework to extract deep and representative features that are trust-worthy for Plasmodium mitochondrial proteins identification. The backbone of the proposed framework is MPPF identification-net (MPPFI-Net), that is based on a convolutional neural network (CNN) with multilayer bi-directional long short-term memory (MBD-LSTM). MPPIF-Net inputs protein sequences, passes through various convolution and pooling layers to optimally extract learned features. We pass these features into our sequence learning mechanism, MBD-LSTM, that is particularly trained to classify them into their relevant classes. Our proposed model is experimentally evaluated on newly prepared dataset PF2095 and two existing benchmark datasets i.e., PF175 and MPD using the holdout method. The proposed method achieved 97.6%, 97.1%, and 99.5% testing accuracy on PF2095, PF175, and MPD datasets, respectively, which outperformed the state-of-the-art approaches. View Full-Text
Keywords: mitochondrial protein; machine learning; bi-directional LSTM; plasmodium falciparum mitochondrial protein; machine learning; bi-directional LSTM; plasmodium falciparum
Show Figures

Figure 1

MDPI and ACS Style

Khan, S.U.; Baik, R. MPPIF-Net: Identification of Plasmodium Falciparum Parasite Mitochondrial Proteins Using Deep Features with Multilayer Bi-directional LSTM. Processes 2020, 8, 725.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop