Impact of ANN in Revealing of Viral Peptides.
All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to suppress the growth of a variety of fungi, bacteria, and viruses. Because AMPs interact with structural components of the microbial cell membrane and h...
| Publicado en: | BioMed Research International pp. 1 - 9 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/8/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158405600&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158405600 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/8/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158405600 158405600 158405600 10.1155/2022/7760734 158405600 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Impact of ANN in Revealing of Viral Peptides. aug: au: Rajkumar, M. Bhukya, Shankar Nayak Ahalya, N. Elumalai, G. Sivanandam, K. Almutairi, Khalid M. A. Alonazi, Wadi B. Soma, S. R. Urugo, Markos Makiso affil: Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India sug: subj: Neural Networks (Computer) Antimicrobial Peptides Sequence Analysis Literature False Positive Results Data Analysis Software Machine Learning Algorithms ab: All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to suppress the growth of a variety of fungi, bacteria, and viruses. Because AMPs interact with structural components of the microbial cell membrane and have a wide range of cellular targets, bacteria are unlikely to be able to develop resistance to them in the short term. The underlying structure of AMPs is critical in determining the selectivity with which they target their respective targets. As far as we know, peptides have not been tested in a lab to see if they can fight bacteria, fungus, and viruses in real life. In this paper, we develop an artificial neural network (ANN) using a back propagation neural network (BPNN) that enables optimal classification of tendency of a peptide sequence that involves the activities of antifungal, antibacterial, or antiviral. The BPNN is trained on the datasets collected across different repositories and then the overfitting is avoided using particle swarm optimization (PSO) algorithm. Hence, at the time of testing, the BPNN clearly finds the predicted samples belonging to the same classes and this avoids the problem of finding the false positives. The simulation is conducted to test the efficacy of the model against various metrics that includes accuracy, precision, recall, and f1-measure. The effectiveness of the BPNN-PSO model in classifying instances at a faster rate than other techniques is demonstrated by its performance. The principle is straightforward, it is not difficult to programme, it converges more quickly, and it generally offers a superior solution. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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