Penerapan metode pengelompokan regularized markov clustering (R-MCL) untuk menganalisa kekerabatan virus dengue = Implementation regularized markov clustering (R-MCL) algortihm to analyze family of dengue virus / Darno Raharjo
Darno Raharjo;
Alhadi Bustaman, supervisor; Dian Lestari, supervisor; Djati Kerami, examiner; Titin Siswantining, examiner
([Publisher not identified]
, 2015)
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[ABSTRAK Virus dengue terdiri atas 10 protein penyusun yang berbeda dan diklasifikasikanmenjadi empat serotipe utama (DEN 1 ? DEN 4). Penelitian ini dirancang untukmelakukan pengelompokan terhadap 30 sekuens protein virus dengue yangdiambil dari Virus Pathogen Database and Analysis Resource (ViPR)menggunakan metode Regularized Markov Clustering (R?MCL) dan untukmenganalisis hasilnya. Dengan menggunakan program Python 3.4, algoritmaR-MCL diimplementasikan dan menghasilkan 8 kelompok dengan pusatkelompok lebih dari satu di beberapa kelompok. Banyaknya pusat kelompokmenunjukkan tingkat kepadatan interaksi. Interaksi protein ? protein yangterhubung padat dalam jaringan cenderung membentuk kompleks protein yangberfungsi sebagai unit proses biologi tertentu. Hasil analisis menunjukkan hasilpengelompokan dengan R-MCL menghasilkan kelompok ? kelompokkekerabatan virus dengue berdasarkan peran yang sama dari protein penyusunnya,tanpa memperhatikan serotipenya. ABSTRACT Dengue virus consists 10 different constituent proteins and are classified into fourmajor serotypes (DEN 1 - DEN 4). This study was designed to perform clusteringagainst 30 protein sequences of dengue virus taken from Virus Pathogen Databaseand Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL)algorithm and tp analyze the result. By using Python program 3.4, R-MCLalgorithm produces 8 clusters with more than one centroid in several clusters. Thenumber of centroid shows the density level of interaction. The density ofinteractions protein - protein connected in a network tend to form a proteincomplex that serves as the unit of specific biological processes. The analyzingresult shows the R-MCL clustering produces clusters of dengue virus familybased on the similirity role of their constituent protein, regardless serotypes;Dengue virus consists 10 different constituent proteins and are classified into fourmajor serotypes (DEN 1 - DEN 4). This study was designed to perform clusteringagainst 30 protein sequences of dengue virus taken from Virus Pathogen Databaseand Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL)algorithm and tp analyze the result. By using Python program 3.4, R-MCLalgorithm produces 8 clusters with more than one centroid in several clusters. Thenumber of centroid shows the density level of interaction. The density ofinteractions protein - protein connected in a network tend to form a proteincomplex that serves as the unit of specific biological processes. The analyzingresult shows the R-MCL clustering produces clusters of dengue virus familybased on the similirity role of their constituent protein, regardless serotypes;Dengue virus consists 10 different constituent proteins and are classified into fourmajor serotypes (DEN 1 - DEN 4). This study was designed to perform clusteringagainst 30 protein sequences of dengue virus taken from Virus Pathogen Databaseand Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL)algorithm and tp analyze the result. By using Python program 3.4, R-MCLalgorithm produces 8 clusters with more than one centroid in several clusters. Thenumber of centroid shows the density level of interaction. The density ofinteractions protein - protein connected in a network tend to form a proteincomplex that serves as the unit of specific biological processes. The analyzingresult shows the R-MCL clustering produces clusters of dengue virus familybased on the similirity role of their constituent protein, regardless serotypes, Dengue virus consists 10 different constituent proteins and are classified into fourmajor serotypes (DEN 1 - DEN 4). This study was designed to perform clusteringagainst 30 protein sequences of dengue virus taken from Virus Pathogen Databaseand Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL)algorithm and tp analyze the result. By using Python program 3.4, R-MCLalgorithm produces 8 clusters with more than one centroid in several clusters. Thenumber of centroid shows the density level of interaction. The density ofinteractions protein - protein connected in a network tend to form a proteincomplex that serves as the unit of specific biological processes. The analyzingresult shows the R-MCL clustering produces clusters of dengue virus familybased on the similirity role of their constituent protein, regardless serotypes] |
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No. Panggil : | T44667 |
Entri utama-Nama orang : | |
Entri tambahan-Nama orang : | |
Entri tambahan-Nama badan : | |
Subjek : | |
Penerbitan : | [Place of publication not identified]: [Publisher not identified], 2015 |
Program Studi : |
Bahasa : | ind |
Sumber Pengatalogan : | LibUI ind rda |
Tipe Konten : | text |
Tipe Media : | unmediated ; computer |
Tipe Carrier : | volume ; online resource |
Deskripsi Fisik : | xiii, 64 pages : illustration ; 28 cm + appendix |
Naskah Ringkas : | |
Lembaga Pemilik : | Universitas Indonesia |
Lokasi : | Perpustakaan UI, Lantai 3 |
No. Panggil | No. Barkod | Ketersediaan |
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T44667 | 15-17-706441290 | TERSEDIA |
Ulasan: |
Tidak ada ulasan pada koleksi ini: 20415399 |