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Jamdade R, Al-Shaer K, Al-Sallani M, Al-Harthi E, Mahmoud T, Gairola S, Shabana HA. Multilocus marker-based delimitation of Salicornia persica and its population discrimination assisted by supervised machine learning approach. PLoS One 2022; 17:e0270463. [PMID: 35895732 PMCID: PMC9328517 DOI: 10.1371/journal.pone.0270463] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 06/10/2022] [Indexed: 11/18/2022] Open
Abstract
The Salicornia L. has been considered one of the most taxonomically challenging genera due to high morphological plasticity, intergradation between related species, and lack of diagnostic features in preserved herbarium specimens. In the United Arab Emirates (UAE), only one species of this genus, Salicornia europaea, has been reported, though investigating its identity at the molecular level has not yet been undertaken. Moreover, based on growth form and morphology variation between the Ras-Al-Khaimah (RAK) population and the Umm-Al-Quwain (UAQ) population, we suspect the presence of different species or morphotypes. The present study aimed to initially perform species identification using multilocus DNA barcode markers from chloroplast DNA (cpDNA) and nuclear ribosomal DNA (nrDNA), followed by the genetic divergence between two populations (RAK and UAQ) belonging to two different coastal localities in the UAE. The analysis resulted in high-quality multilocus barcode sequences subjected to species discrimination through the unsupervised OTU picking and supervised learning methods. The ETS sequence data from our study sites had high identity with the previously reported sequences of Salicornia persica using NCBI blast and was further confirmed using OTU picking methods viz., TaxonDNAs Species identifier and Assemble Species by Automatic Partitioning (ASAP). Moreover, matK sequence data showed a non-monophyletic relationship, and significant discrimination between the two populations through alignment-based unsupervised OTU picking, alignment-free Co-Phylog, and alignment & alignment-free supervised learning approaches. Other markers viz., rbcL, trnH-psbA, ITS2, and ETS could not distinguish the two populations individually, though their combination with matK (cpDNA & cpDNA+nrDNA) showed enough population discrimination. However, the ITS2+ETS (nrDNA) exhibited much higher genetic divergence, further splitting both the populations into four haplotypes. Based on the observed morphology, genetic divergence, and the number of haplotypes predicted using the matK marker, it can be suggested that two distinct populations (RAK and UAQ) do exist. Further extensive morpho-taxonomic studies are required to determine the inter-population variability of Salicornia in the UAE. Altogether, our results suggest that S. persica is the species that grow in the present study area in UAE, and do not support previous treatments as S. europaea.
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Affiliation(s)
- Rahul Jamdade
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
- * E-mail:
| | - Khawla Al-Shaer
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
| | - Mariam Al-Sallani
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
| | - Eman Al-Harthi
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
| | - Tamer Mahmoud
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
- Nature Conservation Sector, Egyptian Environmental Affairs Agency, Cairo, Egypt
| | - Sanjay Gairola
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
| | - Hatem A. Shabana
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority (EPAA), Sharjah, United Arab Emirates
- Nature Conservation Sector, Egyptian Environmental Affairs Agency, Cairo, Egypt
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Jamdade R, Upadhyay M, Al Shaer K, Al Harthi E, Al Sallani M, Al Jasmi M, Al Ketbi A. Evaluation of Arabian Vascular Plant Barcodes (rbcL and matK): Precision of Unsupervised and Supervised Learning Methods towards Accurate Identification. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10122741. [PMID: 34961211 PMCID: PMC8708657 DOI: 10.3390/plants10122741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/16/2021] [Accepted: 09/23/2021] [Indexed: 06/14/2023]
Abstract
Arabia is the largest peninsula in the world, with >3000 species of vascular plants. Not much effort has been made to generate a multi-locus marker barcode library to identify and discriminate the recorded plant species. This study aimed to determine the reliability of the available Arabian plant barcodes (>1500; rbcL and matK) at the public repository (NCBI GenBank) using the unsupervised and supervised methods. Comparative analysis was carried out with the standard dataset (FINBOL) to assess the methods and markers' reliability. Our analysis suggests that from the unsupervised method, TaxonDNA's All Species Barcode criterion (ASB) exhibits the highest accuracy for rbcL barcodes, followed by the matK barcodes using the aligned dataset (FINBOL). However, for the Arabian plant barcode dataset (GBMA), the supervised method performed better than the unsupervised method, where the Random Forest and K-Nearest Neighbor (gappy kernel) classifiers were robust enough. These classifiers successfully recognized true species from both barcode markers belonging to the aligned and alignment-free datasets, respectively. The multi-class classifier showed high species resolution following the two classifiers, though its performance declined when employed to recognize true species. Similar results were observed for the FINBOL dataset through the supervised learning approach; overall, matK marker showed higher accuracy than rbcL. However, the lower rate of species identification in matK in GBMA data could be due to the higher evolutionary rate or gaps and missing data, as observed for the ASB criterion in the FINBOL dataset. Further, a lower number of sequences and singletons could also affect the rate of species resolution, as observed in the GBMA dataset. The GBMA dataset lacks sufficient species membership. We would encourage the taxonomists from the Arabian Peninsula to join our campaign on the Arabian Barcode of Life at the Barcode of Life Data (BOLD) systems. Our efforts together could help improve the rate of species identification for the Arabian Vascular plants.
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Affiliation(s)
- Rahul Jamdade
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority, Sharjah P.O. Box 2926, United Arab Emirates; (K.A.S.); (E.A.H.); (M.A.S.); (M.A.J.); (A.A.K.)
| | - Maulik Upadhyay
- Population Genomics Group, Department of Veterinary Sciences, Ludwig Maximillians University, 80539 Munich, Germany;
| | - Khawla Al Shaer
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority, Sharjah P.O. Box 2926, United Arab Emirates; (K.A.S.); (E.A.H.); (M.A.S.); (M.A.J.); (A.A.K.)
| | - Eman Al Harthi
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority, Sharjah P.O. Box 2926, United Arab Emirates; (K.A.S.); (E.A.H.); (M.A.S.); (M.A.J.); (A.A.K.)
| | - Mariam Al Sallani
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority, Sharjah P.O. Box 2926, United Arab Emirates; (K.A.S.); (E.A.H.); (M.A.S.); (M.A.J.); (A.A.K.)
| | - Mariam Al Jasmi
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority, Sharjah P.O. Box 2926, United Arab Emirates; (K.A.S.); (E.A.H.); (M.A.S.); (M.A.J.); (A.A.K.)
| | - Asma Al Ketbi
- Sharjah Seed Bank and Herbarium, Environment and Protected Areas Authority, Sharjah P.O. Box 2926, United Arab Emirates; (K.A.S.); (E.A.H.); (M.A.S.); (M.A.J.); (A.A.K.)
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3
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Randhawa GS, Hill KA, Kari L. ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels. BMC Genomics 2019; 20:267. [PMID: 30943897 PMCID: PMC6448311 DOI: 10.1186/s12864-019-5571-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 02/27/2019] [Indexed: 11/11/2022] Open
Abstract
Background Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods. Results We propose a novel combination of supervised Machine Learning with Digital Signal Processing, resulting in ML-DSP: an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels. We test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of >97%. A quantitative comparison with state-of-the-art classification software tools is performed, on two small benchmark datasets and one large 4322 vertebrate mtDNA genomes dataset. Our results show that ML-DSP overwhelmingly outperforms the alignment-based software MEGA7 (alignment with MUSCLE or CLUSTALW) in terms of processing time, while having comparable classification accuracies for small datasets and superior accuracies for the large dataset. Compared with the alignment-free software FFP (Feature Frequency Profile), ML-DSP has significantly better classification accuracy, and is overall faster. We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4,710 bacterial genomes into phyla with 95.5% accuracy. Lastly, our analysis shows that the “Purine/Pyrimidine”, “Just-A” and “Real” numerical representations of DNA sequences outperform ten other such numerical representations used in the Digital Signal Processing literature for DNA classification purposes. Conclusions Due to its superior classification accuracy, speed, and scalability to large datasets, ML-DSP is highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures and identifying their mechanistic determinants, and in evaluating genome integrity.
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Affiliation(s)
- Gurjit S Randhawa
- Department of Computer Science, University of Western Ontario, London, ON, Canada.
| | - Kathleen A Hill
- Department of Biology, University of Western Ontario, London, ON, Canada
| | - Lila Kari
- School of Computer Science, University of Waterloo, Waterloo, ON, Canada
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4
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Abstract
Multiclass classification in cancer diagnostics, using DNA or Gene Expression Signatures, but also classification of bacteria species fingerprints in MALDI-TOF mass spectrometry data, is challenging because of imbalanced data and the high number of dimensions with respect to the number of instances. In this study, a new oversampling technique called LICIC will be presented as a valuable instrument in countering both class imbalance, and the famous “curse of dimensionality” problem. The method enables preservation of non-linearities within the dataset, while creating new instances without adding noise. The method will be compared with other oversampling methods, such as Random Oversampling, SMOTE, Borderline-SMOTE, and ADASYN. F1 scores show the validity of this new technique when used with imbalanced, multiclass, and high-dimensional datasets.
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An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes. PLoS One 2018; 13:e0206409. [PMID: 30427878 PMCID: PMC6235296 DOI: 10.1371/journal.pone.0206409] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 10/14/2018] [Indexed: 01/11/2023] Open
Abstract
For many disease-causing virus species, global diversity is clustered into a taxonomy of subtypes with clinical significance. In particular, the classification of infections among the subtypes of human immunodeficiency virus type 1 (HIV-1) is a routine component of clinical management, and there are now many classification algorithms available for this purpose. Although several of these algorithms are similar in accuracy and speed, the majority are proprietary and require laboratories to transmit HIV-1 sequence data over the network to remote servers. This potentially exposes sensitive patient data to unauthorized access, and makes it impossible to determine how classifications are made and to maintain the data provenance of clinical bioinformatic workflows. We propose an open-source supervised and alignment-free subtyping method (Kameris) that operates on k-mer frequencies in HIV-1 sequences. We performed a detailed study of the accuracy and performance of subtype classification in comparison to four state-of-the-art programs. Based on our testing data set of manually curated real-world HIV-1 sequences (n = 2, 784), Kameris obtained an overall accuracy of 97%, which matches or exceeds all other tested software, with a processing rate of over 1,500 sequences per second. Furthermore, our fully standalone general-purpose software provides key advantages in terms of data security and privacy, transparency and reproducibility. Finally, we show that our method is readily adaptable to subtype classification of other viruses including dengue, influenza A, and hepatitis B and C virus.
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6
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Yang CH, Wu KC, Chuang LY, Chang HW. Decision Tree Algorithm-Generated Single-Nucleotide Polymorphism Barcodes of rbcL Genes for 38 Brassicaceae Species Tagging. Evol Bioinform Online 2018; 14:1176934318760856. [PMID: 29551885 PMCID: PMC5846911 DOI: 10.1177/1176934318760856] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Accepted: 01/24/2018] [Indexed: 01/17/2023] Open
Abstract
DNA barcode sequences are accumulating in large data sets. A barcode is generally a sequence larger than 1000 base pairs and generates a computational burden. Although the DNA barcode was originally envisioned as straightforward species tags, the identification usage of barcode sequences is rarely emphasized currently. Single-nucleotide polymorphism (SNP) association studies provide us an idea that the SNPs may be the ideal target of feature selection to discriminate between different species. We hypothesize that SNP-based barcodes may be more effective than the full length of DNA barcode sequences for species discrimination. To address this issue, we tested a ribulose diphosphate carboxylase (rbcL) SNP barcoding (RSB) strategy using a decision tree algorithm. After alignment and trimming, 31 SNPs were discovered in the rbcL sequences from 38 Brassicaceae plant species. In the decision tree construction, these SNPs were computed to set up the decision rule to assign the sequences into 2 groups level by level. After algorithm processing, 37 nodes and 31 loci were required for discriminating 38 species. Finally, the sequence tags consisting of 31 rbcL SNP barcodes were identified for discriminating 38 Brassicaceae species based on the decision tree-selected SNP pattern using RSB method. Taken together, this study provides the rational that the SNP aspect of DNA barcode for rbcL gene is a useful and effective sequence for tagging 38 Brassicaceae species.
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Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.,Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Kuo-Chuan Wu
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.,Department of Computer Science and Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering, Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Hsueh-Wei Chang
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan.,Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.,Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan
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7
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More RP, Mane RC, Purohit HJ. matK-QR classifier: a patterns based approach for plant species identification. BioData Min 2016; 9:39. [PMID: 27990177 PMCID: PMC5148893 DOI: 10.1186/s13040-016-0120-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 12/02/2016] [Indexed: 12/24/2022] Open
Abstract
Background DNA barcoding is widely used and most efficient approach that facilitates rapid and accurate identification of plant species based on the short standardized segment of the genome. The nucleotide sequences of maturaseK (matK) and ribulose-1, 5-bisphosphate carboxylase (rbcL) marker loci are commonly used in plant species identification. Here, we present a new and highly efficient approach for identifying a unique set of discriminating nucleotide patterns to generate a signature (i.e. regular expression) for plant species identification. Methods In order to generate molecular signatures, we used matK and rbcL loci datasets, which encompass 125 plant species in 52 genera reported by the CBOL plant working group. Initially, we performed Multiple Sequence Alignment (MSA) of all species followed by Position Specific Scoring Matrix (PSSM) for both loci to achieve a percentage of discrimination among species. Further, we detected Discriminating Patterns (DP) at genus and species level using PSSM for the matK dataset. Combining DP and consecutive pattern distances, we generated molecular signatures for each species. Finally, we performed a comparative assessment of these signatures with the existing methods including BLASTn, Support Vector Machines (SVM), Jrip-RIPPER, J48 (C4.5 algorithm), and the Naïve Bayes (NB) methods against NCBI-GenBank matK dataset. Results Due to the higher discrimination success obtained with the matK as compared to the rbcL, we selected matK gene for signature generation. We generated signatures for 60 species based on identified discriminating patterns at genus and species level. Our comparative assessment results suggest that a total of 46 out of 60 species could be correctly identified using generated signatures, followed by BLASTn (34 species), SVM (18 species), C4.5 (7 species), NB (4 species) and RIPPER (3 species) methods As a final outcome of this study, we converted signatures into QR codes and developed a software matK-QR Classifier (http://www.neeri.res.in/matk_classifier/index.htm), which search signatures in the query matK gene sequences and predict corresponding plant species. Conclusions This novel approach of employing pattern-based signatures opens new avenues for the classification of species. In addition to existing methods, we believe that matK-QR Classifier would be a valuable tool for molecular taxonomists enabling precise identification of plant species. Electronic supplementary material The online version of this article (doi:10.1186/s13040-016-0120-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ravi Prabhakar More
- Environmental Genomics Division, CSIR-National Environmental Engineering Research Institute, Nagpur, 440020 Maharashtra India ; Present Institute: Division of Molecular Entomology, ICAR- National Bureau of Agricultural Insect Resources (NBAIR), Hebbal, Bengaluru, 560024 Karnataka India
| | | | - Hemant J Purohit
- Environmental Genomics Division, CSIR-National Environmental Engineering Research Institute, Nagpur, 440020 Maharashtra India
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Fiscon G, Weitschek E, Cella E, Lo Presti A, Giovanetti M, Babakir-Mina M, Ciotti M, Ciccozzi M, Pierangeli A, Bertolazzi P, Felici G. MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification. BioData Min 2016; 9:38. [PMID: 27980679 PMCID: PMC5139023 DOI: 10.1186/s13040-016-0116-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 11/20/2016] [Indexed: 12/04/2022] Open
Abstract
Background Continuous improvements in next generation sequencing technologies led to ever-increasing collections of genomic sequences, which have not been easily characterized by biologists, and whose analysis requires huge computational effort. The classification of species emerged as one of the main applications of DNA analysis and has been addressed with several approaches, e.g., multiple alignments-, phylogenetic trees-, statistical- and character-based methods. Results We propose a supervised method based on a genetic algorithm to identify small genomic subsequences that discriminate among different species. The method identifies multiple subsequences of bounded length with the same information power in a given genomic region. The algorithm has been successfully evaluated through its integration into a rule-based classification framework and applied to three different biological data sets: Influenza, Polyoma, and Rhino virus sequences. Conclusions We discover a large number of small subsequences that can be used to identify each virus type with high accuracy and low computational time, and moreover help to characterize different genomic regions. Bounding their length to 20, our method found 1164 characterizing subsequences for all the Influenza virus subtypes, 194 for all the Polyoma viruses, and 11 for Rhino viruses. The abundance of small separating subsequences extracted for each genomic region may be an important support for quick and robust virus identification. Finally, useful biological information can be derived by the relative location and abundance of such subsequences along the different regions. Electronic supplementary material The online version of this article (doi:10.1186/s13040-016-0116-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Giulia Fiscon
- Institute of Systems Analysis and Computer Science A. Ruberti (IASI), National Research Council (CNR), Via dei Taurini 19, Rome, 00185 Italy
| | - Emanuel Weitschek
- Institute of Systems Analysis and Computer Science A. Ruberti (IASI), National Research Council (CNR), Via dei Taurini 19, Rome, 00185 Italy.,Department of Engineering, Uninettuno International University, Corso Vittorio Emanuele II 39, Rome, 00186 Italy
| | - Eleonora Cella
- Department of Infectious Diseases, Istituto Superiore di Sanita, Viale Regina Margherita 299, Rome, 00161 Italy.,Public Health and Infectious Diseases, Sapienza University, Piazzale Aldo Moro 5, Rome, 00185 Italy
| | - Alessandra Lo Presti
- Department of Infectious Diseases, Istituto Superiore di Sanita, Viale Regina Margherita 299, Rome, 00161 Italy
| | - Marta Giovanetti
- Department of Infectious Diseases, Istituto Superiore di Sanita, Viale Regina Margherita 299, Rome, 00161 Italy.,Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133 Italy
| | | | - Marco Ciotti
- Laboratory of Molecular Virology, Polyclinic Tor Vergata Foundation, Viale Oxford 81, Rome, 00133 Italy
| | - Massimo Ciccozzi
- Institute of Systems Analysis and Computer Science A. Ruberti (IASI), National Research Council (CNR), Via dei Taurini 19, Rome, 00185 Italy.,Department of Infectious Diseases, Istituto Superiore di Sanita, Viale Regina Margherita 299, Rome, 00161 Italy
| | - Alessandra Pierangeli
- Virology Laboratory, Department of Molecular Medicine, Sapienza University, Viale di Porta Tiburtina 2, Rome, 00185 Italy
| | - Paola Bertolazzi
- Institute of Systems Analysis and Computer Science A. Ruberti (IASI), National Research Council (CNR), Via dei Taurini 19, Rome, 00185 Italy
| | - Giovanni Felici
- Institute of Systems Analysis and Computer Science A. Ruberti (IASI), National Research Council (CNR), Via dei Taurini 19, Rome, 00185 Italy
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Zhang A, Hao M, Yang C, Shi Z. BarcodingR: an integrated
r
package for species identification using
DNA
barcodes. Methods Ecol Evol 2016. [DOI: 10.1111/2041-210x.12682] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Ai‐bing Zhang
- College of Life Sciences Capital Normal University Beijing 100048 China
| | - Meng‐di Hao
- College of Life Sciences Capital Normal University Beijing 100048 China
| | - Cai‐qing Yang
- College of Life Sciences Capital Normal University Beijing 100048 China
| | - Zhi‐yong Shi
- College of Life Sciences Capital Normal University Beijing 100048 China
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Gan R, Chen N, Huang D. Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models. PeerJ 2016; 4:e2684. [PMID: 27843718 PMCID: PMC5103820 DOI: 10.7717/peerj.2684] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 10/13/2016] [Indexed: 02/04/2023] Open
Abstract
This study compares and evaluates the prediction of hepatitis in Guangxi Province, China by using back propagation neural networks based genetic algorithm (BPNN-GA), generalized regression neural networks (GRNN), and wavelet neural networks (WNN). In order to compare the results of forecasting, the data obtained from 2004 to 2013 and 2014 were used as modeling and forecasting samples, respectively. The results show that when the small data set of hepatitis has seasonal fluctuation, the prediction result by BPNN-GA will be better than the two other methods. The WNN method is suitable for predicting the large data set of hepatitis that has seasonal fluctuation and the same for the GRNN method when the data increases steadily.
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Affiliation(s)
- Ruijing Gan
- School of Preclinical Medicine, Guangxi Medical University , Nanning, Guangxi , China
| | - Ni Chen
- School of Preclinical Medicine, Guangxi Medical University , Nanning, Guangxi , China
| | - Daizheng Huang
- School of Preclinical Medicine, Guangxi Medical University , Nanning, Guangxi , China
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