ANALISA VISUAL MENGGUNAKAN ETETOOLKIT FRAMEWORK TERHADAP PENYAKIT BETA-THALASSEMIA DI JAWA TENGAH BAGIAN SELATAN
Keywords:
β-thalassemia, Mutations, ETEToolkit, Central JavaAbstract
Deteksi peristiwa biomolekuler dalam visual yang akan dianalisis menggunakan komputasi untuk mendeteksi efektivitas dan akurasi penyakit. Sebagai hasil utama, banyak analisis visual, mulai dari pengelompokan gen hingga filogenetik, menghasilkan pohon hierarkis. Toolkit Lingkungan Eksplorasi Pohon (ETE) yang membantu manipulasi, analisis, dan visualisasi pohon hierarkis otomatis. Kemudian, dalam makalah ini, daftar mutasi β-thalassemia yang merupakan kelompok kelainan darah herediter yang ditandai oleh anomali dalam sintesis rantai beta hemoglobin yang menghasilkan berbagai fenotipe mulai dari anemia berat hingga individu tanpa gejala klinis. Hasil ini adalah ETEToolkit dapat menguraikan mutasi ini untuk ditampilkan melalui pohon dan penyelarasan dalam satu bingkai, kemudian kita dapat menyesuaikan dan merender ke dalam gambar PDF. Mutasi ini berlokasi di pusat Jawa, Indonesia.
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Huerta, C.J., Bork, P., Serra, F., 2016, ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data, Mol Bio Eco, 33(6):1635-1638, doi:0.1093/molbev/msw046
Hughes, T., Hyun, Y., Liberles, D.A., 2004, Visualizing very large phylogenetic trees in three dimensional hyperbolic space, BMC Bioinformatics. 5:48, doi:10.1186/1471-2105-5-48
Johnson, R., Zhang, T., 2015, Semi-supervised convolutional neural networks for text categorization via region embedding. neural information processing systems, 919–927, arxiv:1504.01255
Kosakovsky, P.S.L., Muse, S.V., 2004, Column sorting: rapid calculation of the phylogenetic likelihood function, Syst Biol 53(5): 685-692, doi:10.1080/10635150490522269
Kim, Y., Jernite, Y., Sontag, D., Rush, A.M., 2016, Character-aware neural language models, AAAI, 2741–2749, arxiv:1508.06615
Lantip R, Basalamah M, Mulatsih S, Sofro ASM. 2015. Molecular Scanning of β-Thalassemia in the Southern Region of Central Java, Indonesia; Towards a Local Prevention Program. Int J for hemoglobin res. 39(5):330-333. doi:10.3109/03630269.2015.1065420.
Azzimonti, D., Ginsbourger, D., 2018, Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation, J of Comp and Graph Stat. 27(2):255-267. doi: 10.1080/10618600.2017.1360781
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., 2003, A neural probabilistic language model. Innovations in machine learning. 194:137–186. doi:10.1.1.133.9693
Binhua T, Zixiang P, Kang Y, Asif. 2019. Recent Advances of Deep learning in Bioinformatics and Computational Biology, Front Gen, 10:214. doi:10.3389/fgene.2019.00214
Bojanowski P, Grave E, Joulin A, Mikolov T. 2017. Enriching word vectors with subword information, arXiv preprint. arXiv:1607.04606
Blei, D.M., Ng, A.Y., Jordan, M.I., 2003, Latent dirichlet allocation, J of machine Learning res. 3:993–1022. doi:10.1.1.110.4050
Cavalli, S.L.L., 1997. Genes Peoples and Languages. Proc Nat Aca Sci. 94(15): 7719 – 7724, doi:10.1046/j.1365-2540.2001.0962c.x
Carrizo, S.F., 2004, Phylogenetic trees: an information visualisation perspective, Proc of Asia-Pacific Bioinformatics Conf (APBC2004). 29:315-320. acmid: 976563
Cambria, E., White, B., 2014, Jumping NLP curves: A review of natural language processing research, IEEE Comp Intelligence Mag. 9(2): 48–57. doi:10.1109/MCI.2014.2307227
Cambria, E., Poria, S., Gelbukh, A., Thelwall, M., 2017, Sentiment analysis is a big suitcase, IEEE Intelligent Systems, 32(6): 74–80, doi:10.1109/MIS.2017.4531228
Collobert, R., Weston, J., 2008, A unified architecture for natural language processing: Deep neural networks with multitask learning, Proc of the 25th int conf on Machine learning, 160–167, doi.:10.1145/1390156.1390177
Chen, X., Xu, L., Liu, Z., Sun, M., Luan, H., 2015, Joint learning of character and word embeddings, Proc of the 24th 
Int Joint Conf on Artificial Intelligence, 1236-1242. acmid:2832421
Dai, A.M., Le, Q.V., 2015, Semi-supervised sequence learning. Adv in neural inf proc sys. Proc of the 28th Int Conf on Neural Inf Proce Sys. 2:3079–3087. acmid:2969583
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.,2018, Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint, arXiv:1810.04805 

Santos, C.N., Gatti, M., 2014, Deep convolutional neural networks for sentiment analysis of short texts. Proc of COLING the 25th Int Conf on Comp Ling, 69–78 

Dumais, S.T., 2004, Latent semantic analysis, Ann rev of inf sci and tech, 38(1):188-230, doi:10.1002/aris.1440380105
Encarnacao, L.,2017, Information Visualization, IEEE Comp Graph and App, 37(2):6-7, doi:10.1109/MCG.2017.25
ETEToolkit Developer Team ., 7 April 2019, ETEToolkit Documentation, http://etetoolkit.org/docs/latest/tutorial/index.html
Elman, J.L., 1991, Distributed representations, simple recurrent networks, and grammatical structure, Machine learning, 7(2,3):195–225. doi:10.1007/BF00114844
Galanello, R., Origa, R.. 2010, Beta-thalassemia, Orp J of Rare Dis, 5:11, doi:10.1186/1750-1172-5-11
Giordano, P., Harteveld, C., Bakker, E., 2014, Genetic epidemiology and preventive healthcare in multiethnic societies: The hemoglobinopathies, Int J Env Res Pub Health, 11(6):6136–6146
Gittens, A., Achlioptas, D., Mahoney, M.W., 2017, Skip-gram-zipf+ uniform= vector additivity, the 55th A M of the Association for Comp Ling, 1:69–76, doi:10.3390/ijerph110606136
Glorot, X., Bordes, A., Bengio, Y.. 2011. Domain adaptation for large-scale sentiment classification: A deep learning approach, the 28th int conf on machine learning (ICML-11), 513–520, acmid:3104547
Glenberg, A.M., Robertson, D.A., 2000, Symbol grounding and meaning: A comparison of high-dimensional and embodied theories of meaning, J of mem and lang, 43(3):379–401, doi:10.1006/jmla.2000.2714
Hernanda, P.Y., Tursilowati, L., Arkesteijn, S.G., 2012, Towards a prevention program for b-thalassemia: The molecular spectrum in East Java, Indonesia, Hemoglobin, 36(1):1–6, doi: 10.3109/03630269.2011.642914
Herman, K.M., Blunsom, P., 2013, The role of syntax in vector space models of compositional semantics, Proc of the 51th A M of the Association for Comp Ling, Association for Computational Linguistics. 894-904

Herbelot, A., Baroni, M., 2017, High-risk learning: acquiring new word vectors from tiny data, arXiv preprint, arXiv:1707.06556
Huerta, C.J., Dopazo, J., Gabaldon, T., 2010, ETE: a python Environment for Tree Exploration, BMC Bioinformatics 11:24, doi:10.1186/1471-2105-11-24
Huerta, C.J., Dopazo, H., Dopazo, J., Gabaldón, T., 2007., The human phylome. Genome Biologyl. 8(6):R109.
Huerta, C.J., Bueno, A., Dopazo, J., Gabaldón, T., 2008, PhylomeDB: a database for genome-wide collections of gene phylogenies. Nuc Acids Res, 36:491-496, doi:10.1186/gb-2007-8-6-r109
Huerta, C.J., Bork, P., Serra, F., 2016, ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data, Mol Bio Eco, 33(6):1635-1638, doi:0.1093/molbev/msw046
Hughes, T., Hyun, Y., Liberles, D.A., 2004, Visualizing very large phylogenetic trees in three dimensional hyperbolic space, BMC Bioinformatics. 5:48, doi:10.1186/1471-2105-5-48
Johnson, R., Zhang, T., 2015, Semi-supervised convolutional neural networks for text categorization via region embedding. neural information processing systems, 919–927, arxiv:1504.01255
Kosakovsky, P.S.L., Muse, S.V., 2004, Column sorting: rapid calculation of the phylogenetic likelihood function, Syst Biol 53(5): 685-692, doi:10.1080/10635150490522269
Kim, Y., Jernite, Y., Sontag, D., Rush, A.M., 2016, Character-aware neural language models, AAAI, 2741–2749, arxiv:1508.06615
Lantip R, Basalamah M, Mulatsih S, Sofro ASM. 2015. Molecular Scanning of β-Thalassemia in the Southern Region of Central Java, Indonesia; Towards a Local Prevention Program. Int J for hemoglobin res. 39(5):330-333. doi:10.3109/03630269.2015.1065420.
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Published
2019-04-30
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Mutawalli L, Syaifur Rizal MR, Artama WT, Fajriyah R, Muhimmah I, Rujito L. ANALISA VISUAL MENGGUNAKAN ETETOOLKIT FRAMEWORK TERHADAP PENYAKIT BETA-THALASSEMIA DI JAWA TENGAH BAGIAN SELATAN. JIRE [Internet]. 2019 Apr. 30 [cited 2025 Jul. 16];2(1):16-27. Available from: https://e-journal.stmiklombok.ac.id/index.php/jire/article/view/75
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