A fuzzy c-means clustering approach for haplotype reconstruction based on minimum error correction
Studying human genetic evolution has attracted considerable attention. Haplotypes determination provides key information about human genetics, and facilitates understanding probable causal relations between traits and diseases. In general, experimental methods of haplotypes reconstruction are exorbi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914821001350 |
| _version_ | 1818689382214795264 |
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| author | Mohammad Hossein Olyaee Alireza Khanteymoori Ebrahim Fazli |
| author_facet | Mohammad Hossein Olyaee Alireza Khanteymoori Ebrahim Fazli |
| author_sort | Mohammad Hossein Olyaee |
| collection | DOAJ |
| description | Studying human genetic evolution has attracted considerable attention. Haplotypes determination provides key information about human genetics, and facilitates understanding probable causal relations between traits and diseases. In general, experimental methods of haplotypes reconstruction are exorbitant in terms of time and resources. The state-of-the-art high throughput sequencing, enables leveraging computational methods for this task. However, current sequencing algorithms suffer from truncated accuracy once the error rate of their input fragment increases. In this article, we put forward FCMHap, an efficient and accurate method, which involves two steps. In the first step, it constructs a weighted fuzzy conflict graph obtained based on the similarities of the input fragments and divides the input fragments in two clusters by partitioning the graph in an iterative manner. Since the input fragments consist of noise and gaps, in the next step, it adopts the cluster centers by utilizing the fuzzy c-means (FCM) algorithm. The proposed method has been evaluated on several real datasets and compared with a selected set of current approaches. The evaluation results substantiate that this method can be an accompaniment of those approaches. |
| first_indexed | 2024-12-17T12:09:13Z |
| format | Article |
| id | doaj.art-103f6f36493f43439b2c325991e08ae8 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-12-17T12:09:13Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-103f6f36493f43439b2c325991e08ae82022-12-21T21:49:28ZengElsevierInformatics in Medicine Unlocked2352-91482021-01-0125100646A fuzzy c-means clustering approach for haplotype reconstruction based on minimum error correctionMohammad Hossein Olyaee0Alireza Khanteymoori1Ebrahim Fazli2Faculty of Engineering, Department of Computer Engineering, University of Gonabad, Gonabad, IranDepartment of Computer Engineering, University of Zanjan, Zanjan, Iran; Corresponding author.Department of Computer, Zanjan Branch, Islamic Azad University, Zanjan, IranStudying human genetic evolution has attracted considerable attention. Haplotypes determination provides key information about human genetics, and facilitates understanding probable causal relations between traits and diseases. In general, experimental methods of haplotypes reconstruction are exorbitant in terms of time and resources. The state-of-the-art high throughput sequencing, enables leveraging computational methods for this task. However, current sequencing algorithms suffer from truncated accuracy once the error rate of their input fragment increases. In this article, we put forward FCMHap, an efficient and accurate method, which involves two steps. In the first step, it constructs a weighted fuzzy conflict graph obtained based on the similarities of the input fragments and divides the input fragments in two clusters by partitioning the graph in an iterative manner. Since the input fragments consist of noise and gaps, in the next step, it adopts the cluster centers by utilizing the fuzzy c-means (FCM) algorithm. The proposed method has been evaluated on several real datasets and compared with a selected set of current approaches. The evaluation results substantiate that this method can be an accompaniment of those approaches.http://www.sciencedirect.com/science/article/pii/S2352914821001350BioinformaticsSingle individual haplotypeFuzzy c-means clusteringMinimum error correctionDiploid |
| spellingShingle | Mohammad Hossein Olyaee Alireza Khanteymoori Ebrahim Fazli A fuzzy c-means clustering approach for haplotype reconstruction based on minimum error correction Informatics in Medicine Unlocked Bioinformatics Single individual haplotype Fuzzy c-means clustering Minimum error correction Diploid |
| title | A fuzzy c-means clustering approach for haplotype reconstruction based on minimum error correction |
| title_full | A fuzzy c-means clustering approach for haplotype reconstruction based on minimum error correction |
| title_fullStr | A fuzzy c-means clustering approach for haplotype reconstruction based on minimum error correction |
| title_full_unstemmed | A fuzzy c-means clustering approach for haplotype reconstruction based on minimum error correction |
| title_short | A fuzzy c-means clustering approach for haplotype reconstruction based on minimum error correction |
| title_sort | fuzzy c means clustering approach for haplotype reconstruction based on minimum error correction |
| topic | Bioinformatics Single individual haplotype Fuzzy c-means clustering Minimum error correction Diploid |
| url | http://www.sciencedirect.com/science/article/pii/S2352914821001350 |
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