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Статті в журналах з теми "Analisi microarray"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

Naufal, Shidqi Aqil, Adiwijaya Adiwijaya, and Widi Astuti. "Analisis Perbandingan Klasifikasi Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) untuk Deteksi Kanker dengan Data Microarray." JURIKOM (Jurnal Riset Komputer) 7, no. 1 (February 15, 2020): 162. http://dx.doi.org/10.30865/jurikom.v7i1.2014.

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Cancer is a disease that can cause human death in various countries. According to WHO in 2018, cancer causes 9.6 million human deaths worldwide. Globally, about 1 in 6 deaths is due to cancer. Therefore, we need a technology that can be used for cancer detection with high acuration so that cancer can be detected early. Microarrays technique can predict certain tissues in humans and can be classified as cancer or not. However, microarray data has a problem with very large dimensions. To overcome this problem, in this study use one of the dimension reduction techniques, namely Partial Least Square(PLS) and use Support vector Machine (SVM) and K-Nearest Neighbors as a classification method, which will be used to compare which is better.The system built was able to reach 98.54% in leukemia data with PLS-KNN, 100% in lung data with KNN, 66.52% in breast data with PLS-KNN, and 85.60% in colon data with PLS- SVM. KNN is able to get the best in three data from four valued data.

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2

MUNZIR, ANDI FUTRI HAFSAH, ,. ADIWIJAYA, and ANNISA ADITSANIA. "ANALISIS REDUKSI DIMENSI PADA KLASIFIKASI MICROARRAY MENGGUNAKAN MBP POWELL BEALE." E-Jurnal Matematika 7, no. 1 (February 3, 2018): 17. http://dx.doi.org/10.24843/mtk.2018.v07.i01.p179.

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Cancer is the second leading cause of death in the world based on World Health Organization (WHO) survey in 2015. It took DNA microarray technology to analyze and diagnose cancer. DNA microarray has large dimensions so it influences the process of cancer ‘s classification. GA and PCA are used as reduction method and MBP Powell Beale as classification method. The testing of MBP classification without dimension reduction results 70, 59% ? 100% accuracy. MBP+PCA results 76, 47% ? 100% accuracy. MBP + GA results 76, 47% ? 92, 31% accuracy.

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3

SHIMOMURA, Takashi, Xiaoming HAN, Akito HATA, Takuro NIIDOME, Takeshi MORI, and Yoshiki KATAYAMA. "Optimization of Peptide Density on Microarray Surface for Quantitative Phosphoproteomics." Analytical Sciences 27, no. 1 (2011): 13–17. http://dx.doi.org/10.2116/analsci.27.13.

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4

Yang, Liu, and Kristiaan Pelckmans. "Machine Learning Approaches to Survival Analysis: Case Studies in Microarray for Breast Cancer." International Journal of Machine Learning and Computing 4, no. 6 (2014): 483–90. http://dx.doi.org/10.7763/ijmlc.2014.v6.459.

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5

TODA, Kyoko, Seiichi ISHIDA, Kotoko NAKATA, Rieko MATSUDA, Yukari SHIGEMOTO-MOGAMI, Kayoko FUJISHITA, Shogo OZAWA, et al. "Test of Significant Differences with a priori Probability in Microarray Experiments." Analytical Sciences 19, no. 11 (2003): 1529–35. http://dx.doi.org/10.2116/analsci.19.1529.

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6

Kegel, Jessica U., Delphine Guillebault, and Linda K. Medlin. "Application of microarrays (phylochips) for analysis of community diversity by species identification." Perspectives in Phycology 3, no. 2 (September 9, 2016): 93–106. http://dx.doi.org/10.1127/pip/2016/0048.

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7

Nurlaily, Diana, Farida Nur Hayati, and Elly Pusporani. "Membandingkan Seleksi variabel Pada Data Microarray Menggunakan Important Variable Value dan Genetic Algorithm (Studi Kasus Lung Cancer Dataset dan Prostate Cancer Dataset)." J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika 14, no. 1 (July 31, 2021): 38–43. http://dx.doi.org/10.36456/jstat.vol14.no1.a3853.

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Анотація:

Teknologi DNA microarray menarik minat yang luar biasa baik di kalangan komunitas ilmiah maupun kalangan industri. Meskipun data microarray telah diterapkan dalam berbagai bidang, penanganan volume data besar yang dihasilkan bukanlah perkara yang mudah. Ukuran sampel kecil dengan dimensi tinggi adalah tantangan utama analisis menggunakan data microarray. Oleh karena itu perlu dilakukan analisis lebih lanjut untuk mengatasi hal ini. Banyak penelitian yang telah dirancang berkaitan dengan data microarray misalnya untuk menyelidiki mekanisme genetik kanker, dan untuk mengklasifikasikan berbagai jenis kanker atau membedakan antara jaringan kanker dan non-kanker. Semua penelitian ini bertujuan untuk menghasilkan kesimpulan dan interpretasi yang bermanfaat dari kumpulan data yang kompleks. Dalam penelitian ini, data yang digunakan adalah data kanker paru-paru sebanyak 24257 Variabel dan data kanker prostat sebanyak 12626 Variabel. Data tersebut kemudian akan dianalisis dengan beberapa metode feature selection yaitu important variable value dan genetic algorithm untuk memilih dimensi atau variabel data sehingga dapat meningkatkan akurasi klasifikasi data. Berdasarkan hasil analisis feature selection menggunakan data kanker paru-paru, didapatkan jumlah variabel terpilih sebanyak 112 variabel dengan metode feature selection important. Sedangkan metode genetic algorithm didapatkan jumlah variabel terpilihnya sebanyak 12266 variabel. Pada data kanker prostat, didapatkan jumlah variabel terpilih sebanyak 299 variabel dengan metode feature selection important. Sedangkan metode genetic algorithm didapatkan jumlah variabel terpilihnya sebanyak 6359 variabel.

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8

Hamim, Mohammed, Ismail El Mouden, Mounir Ouzir, Hicham Moutachaouik, and Mustapha Hain. "A NOVEL DIMENSIONALITY REDUCTION APPROACH TO IMPROVE MICROARRAY DATA CLASSIFICATION." IIUM Engineering Journal 22, no. 1 (January 4, 2021): 1–22. http://dx.doi.org/10.31436/iiumej.v22i1.1447.

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Cancer tumor prediction and diagnosis at an early stage has become a necessity in cancer research, as it provides an increase in the treatment success chances. Recently, DNA microarray technology became a powerful tool for cancer identification, that can analyze the expression level of a different and huge number of genes simultaneously. In microarray data, the large genes number versus a few records may affect the prediction performance. In order to handle this "curse of dimensionality” constraint of microarray dataset while improving the cancer identification performance, a dimensional reduction phase is necessary. In this paper, we proposed a framework that combines dimensional reduction methods and machine learning algorithms in order to achieve the best cancer prediction performance using different microarray datasets. In the dimensional reduction phase, a combination of feature selection and feature extraction techniques was proposed. Pearson and Ant Colony Optimization was used to select the most important genes. Principal Component Analysis and Kernel Principal Component Analysis were used to linearly and non-linearly transform the selected genes to a new reduced space. In the cancer identification phase, we proposed four algorithms C5.0, Logistic Regression, Artificial Neural Network, and Support Vector Machine. Experimental results demonstrated that the framework performs effectively and competitively compared to state-of-the-art methods. ABSTRAK: Ramalan tumor kanser dan diagnosis pada peringkat awal telah menjadi keperluan dalam kajian kanser, kerana ia membuka peluang peningkatan kejayaan dalam rawatan. Kebelakangan ini, teknologi mikrotatasusunan DNA menjadi alat berkuasa bagi mengenal pasti kanser, di mana ia mampu menganalisa level ekspresi yang pelbagai dan gen-gen yang banyak secara serentak. Dalam data mikrotatasusunan, gen-gen yang banyak ini bakal menentukan ramalan prestasi berbanding analisa melalui rekod-rekod yang sebilangan. Fasa pengurangan dimensi adalah perlu bagi mengawal kakangan “penentuan kedimensian” dataset mikrotatasusunan, sementara itu ia memantapkan lagi keberkesanan kenal pasti kanser. Kajian ini mencadangkan rangka kombinasi kaedah pengurangan dimensi dan algoritma pembelajaran mesin bagi mencapai prestasi ramalan kanser terbaik dengan menggunakan pelbagai dataset mikrotatasusunan. Dalam fasa pengurangan dimensi, kombinasi pemilihan ciri dan teknik pengekstrakan ciri telah dicadangkan, Pengoptimuman Pearson dan Koloni Semut bagi memilih gen yang paling penting, Analisis Komponen Prinsipal dan Analisis Komponen Prinsipal Kernel, bagi menukar gen terpilih yang linear dan tak linear kepada ruang baru yang dikurangkan. Dalam menentukan fasa mengenal pasti kanser, kajian ini mencadangkan empat algoritma iaitu C5.0, Regresi Logistik, Rangkaian Neural Buatan dan Mesin Vektor Sokongan. Dapatan kajian menunjukkan rangka ini adalah berkesan dan kompetitif berbanding kaedah semasa.

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9

Lykhenko, O. "СONSECUTIVE INTEGRATION OF AVAILABLE MICROARRAY DATA FOR ANALYSIS OF DIFFERENTIAL GENE EXPRESSION IN HUMAN PLACENTA". Biotechnologia Acta 14, № 1 (лютий 2021): 38–45. http://dx.doi.org/10.15407/biotech14.01.38.

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The purpose of the study was to provide the pipeline for processing of publicly available unprocessed data on gene expression via integration and differential gene expression analysis. Data collection from open gene expression databases, normalization and integration into a single expression matrix in accordance with metadata and determination of differentially expressed genes were fulfilled. To demonstrate all stages of data processing and integrative analysis, there were used the data from gene expression in the human placenta from the first and second trimesters of normal pregnancy. The source code for the integrative analysis was written in the R programming language and publicly available as a repository on GitHub. Four clusters of functionally enriched differentially expressed genes were identified for the human placenta in the interval between the first and second trimester of pregnancy. Immune processes, developmental processes, vasculogenesis and angiogenesis, signaling and the processes associated with zinc ions varied in the considered interval between the first and second trimester of placental development. The proposed sequence of actions for integrative analysis could be applied to any data obtained by microarray technology.

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10

SHIGAKI, Syuhei, Takayuki YAMAJI, Xiaoming HAN, Go YAMANOUCHI, Tatsuhiko SONODA, Osamu OKITSU, Takeshi MORI, Takuro NIIDOME, and Yoshiki KATAYAMA. "A Peptide Microarray for the Detection of Protein Kinase Activity in Cell Lysate." Analytical Sciences 23, no. 3 (2007): 271–75. http://dx.doi.org/10.2116/analsci.23.271.

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11

Mangalik, Yanche Kurniawan, Triando Hamonangan Saragih, Dodon Turianto Nugrahadi, Muliadi Muliadi, and Muhammad Itqan Mazdadi. "Analisis Seleksi Fitur Binary PSO Pada Klasifikasi Kanker Berdasarkan Data Microarray Menggunakan DWKNN." Jurnal Informatika Polinema 9, no. 2 (February 27, 2023): 133–42. http://dx.doi.org/10.33795/jip.v9i2.1128.

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Анотація:

Salah satu penyakit mematikan penyebab kematian terbesar secara global adalah kanker. Kematian akibat kanker dapat diredam melalui deteksi dini terhadap kanker dengan memanfaatkan teknologi microarray. Namun teknologi ini memiliki kekurangan, yaitu jumlah gen (fitur) yang terlalu banyak. Kekurangan tersebut dapat diatasi dengan melakukan seleksi fitur terhadap data microarray. Salah satu algoritma seleksi fitur yang dapat digunakan adalah Binary Particle Swarm Optimizationi (BPSO). Pada penelitian ini, dilakukan seleksi fitur dengan BPSO pada data microarray dan klasifikasi menggunakan Distance Weighted KNN (DWKNN). Kemudian akan dilihat perbandingan hasil akurasi, presisi, recall, dan f1-score antara DWKNN dan BPSO-DWKNN. Seleksi fitur dan klasifikasi (BPSO-DWKNN) pada dataset Leukemia menghasilkan akurasi, presisi, recall, dan f1-score tertinggi beturut-turut sebesar 93,12%, 94,39%, 95,92%, dan 94,8%. Pada dataset Lung Cancer diperoleh akurasi, presisi, recall, dan f1-score tertinggi beturut-turut sebesar 98,36%, 98,77%, 99,35%, dan 99,03%. Pada dataset Prostate Cancer diperoleh akurasi, presisi, recall, dan f1-score tertinggi beturut-turut sebesar 86,81%, 89,13%, 88,04%, dan 88,07%. Pada dataset Diffuse Large B-Cell Lymphome diperoleh akurasi, presisi, recall, dan f1-score tertinggi beturut-turut sebesar 85,8%, 93,21%, 88,1%, dan 89,76%. Hasil perbandingan menunjukkan peningkatan akurasi, presisi, recall, dan f1-score pada algoritma DWKNN dengan seleksi fitur BPSO dibandingkan dengan algoritma DWKNN tanpa seleksi fitur BPSO.

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12

LI, Yongjin. "Establishment and Application of a Visual DNA Microarray for the Detection of Food-borne Pathogens." Analytical Sciences 32, no. 2 (2016): 215–18. http://dx.doi.org/10.2116/analsci.32.215.

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13

IKEDA, Hiromu, Yoshihiro YAYAMA, Akito HATA, Jumpei KAMIMOTO, Tatsuhiro YAMAMOTO, Takeshi MORI, and Yoshiki KATAYAMA. "PNA-tagged Peptide Microarrays for Ratiometric Activity Detection of Cellular Protein Kinases." Analytical Sciences 30, no. 6 (2014): 631–35. http://dx.doi.org/10.2116/analsci.30.631.

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14

LEE, SangWook, Jong Hyun LEE, Hyuck Gi KWON, Thomas LAURELL, Ok Chan JEONG, and Soyoun KIM. "A Sol-gel Integrated Dual-readout Microarray Platform for Quantification and Identification of Prostate-specific Antigen." Analytical Sciences 34, no. 3 (2018): 317–21. http://dx.doi.org/10.2116/analsci.34.317.

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15

DONHAUSER, Simon C., Reinhard NIESSNER, and Michael SEIDEL. "Quantification of E. coli DNA on a Flow-through Chemiluminescence Microarray Readout System after PCR Amplification." Analytical Sciences 25, no. 5 (2009): 669–74. http://dx.doi.org/10.2116/analsci.25.669.

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16

Diani, Rima. "Analisis Pengaruh Kernel Support Vector Machine (SVM) pada Klasifikasi Data Microarray untuk Deteksi Kanker." Indonesian Journal on Computing (Indo-JC) 2, no. 1 (September 14, 2017): 109. http://dx.doi.org/10.21108/indojc.2017.2.1.169.

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<p>Berdasarkan data dari Pusat Data dan Informasi Kementrian Kesehatan RI, di tahun 2012 sekitar 8,2 juta kasus kematian disebabkan oleh kanker. Perkembangan terakhir menunjukan bahwa teknologi DNA <em>microarray</em> mampu menangani masalah deteksi kanker sejak dini, namun kelemahan utama dari <em>microarray</em> adalah masalah <em>curse of dimensionality.</em></p>Analysis of Variance (ANOVA) merupakan salah satu metode seleksi fitur yang dapat mengatasi kelemahan <em>microarray</em>. ANOVA dapat menemukan pasangan gen informatif yang dapat membantu dalam proses pengklasifikasian yang dilakukan oleh Support Vector Machine (SVM). Dalam SVM, kernel <em>trick</em> saat <em>learning</em> model sangat membantu dalam mengatasi masalah <em>feature space</em>. Pemilihan kernel berpengaruh terhadap akurasi yang dihasilkan. Melalui serangkaian proses seperti perhitungan korelasi, seleksi fitur dan pengklasifikasian menggunakan SVM, didapatkan akurasi dari empat <em>dataset</em> yang digunakan. Untuk <em>dataset</em> leukimia dan <em>ovarian cancer</em>, akurasi terbesar dihasilkan oleh kernel polynomial yaitu sebesar 100% dan 97,54%. Sedangkan untuk <em>dataset</em> <em>lung cancer</em> akurasi terbesar diperoleh dari kernel linear yaitu sebesar 100% dan untuk <em>dataset</em> <em>colon tumor</em> akurasi terbesar diperoleh dari kernel RBF sebesar 85,15%. Perbedaan kernel yang menghasilkan akurasi tertinggi pada setiap <em>dataset</em> sangat bergantung kepada karakteristik <em>dataset </em>kanker itu sendiri.

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17

RAMADHANI, PUTRI TSATSABILA. "Deteksi Kanker berdasarkan Klasifikasi Data Microarray menggunakan Functional Link Neural Network dengan Seleksi Fitur Genetic Algorithm." Indonesian Journal on Computing (Indo-JC) 2, no. 2 (November 20, 2017): 11. http://dx.doi.org/10.21108/indojc.2017.2.2.173.

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<p>Di beberapa tahun terakhir, pemanfaatan teknologi microarray memiliki pengaruh besar dalam menentukan gen informatif yang menyebabkan kanker. Micorarray mampu menentukan ekspresi ribuan gen dan secara simultan memantau proses bilogis yang sedang berlangsung. Dengan melakukan analisa terhadap data micorarray, selanjutnya ekspresi dari ribuan gen yang merepresentasikan suatu jaringan pada manusia, akan diklasifikasikan sebagai jaringan kanker atau bukan. Dalam penulisan penelitian penelitian, penulis meng-implementasikan Functional Link Neural Network dengan fungsi basis Legendre Polynomial untuk klasifikasi data yang akurat dan menggunakan Genetic Algorithm sebagai seleksi fitur untuk mereduksi data berdimensi tinggi yang sering ditemukan pada data microarray. Dengan serangkaian proses yang telah dilakukan, maka diperoleh kinerja tertinggi terhadap klasifikasi data microarray Colon Tumor sebesar 92.3% dan Leukemia sebesar 87.5%. Perbedaan kinerja yang diperoleh disebabkan oleh perbedaan karakteristik masing-masing data.</p>

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18

Scholtens, Denise, Alexander Miron, Faisal M. Merchant, Arden Miller, Penelope L. Miron, J. Dirk Iglehart, and Robert Gentleman. "Analyzing factorial designed microarray experiments." Journal of Multivariate Analysis 90, no. 1 (July 2004): 19–43. http://dx.doi.org/10.1016/j.jmva.2004.02.004.

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19

Dettling, Marcel, and Peter Bühlmann. "Finding predictive gene groups from microarray data." Journal of Multivariate Analysis 90, no. 1 (July 2004): 106–31. http://dx.doi.org/10.1016/j.jmva.2004.02.012.

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20

Chen, Liuyuan, Jie Yang, Juntao Li, and Xiaoyu Wang. "Multinomial Regression with Elastic Net Penalty and Its Grouping Effect in Gene Selection." Abstract and Applied Analysis 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/569501.

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Анотація:

For the multiclass classification problem of microarray data, a new optimization model named multinomial regression with the elastic net penalty was proposed in this paper. By combining the multinomial likeliyhood loss and the multiclass elastic net penalty, the optimization model was constructed, which was proved to encourage a grouping effect in gene selection for multiclass classification.

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21

Rempala, Grzegorz A., and Iwona Pawlikowska. "Limit theorems for hybridization reactions on oligonucleotide microarrays." Journal of Multivariate Analysis 99, no. 9 (October 2008): 2082–95. http://dx.doi.org/10.1016/j.jmva.2008.02.014.

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22

Ristevski, Blagoj. "A survey of models for inference of gene regulatory networks." Nonlinear Analysis: Modelling and Control 18, no. 4 (October 25, 2013): 444–65. http://dx.doi.org/10.15388/na.18.4.13972.

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In this article, I present the biological backgrounds of microarray, ChIP-chip and ChIPSeq technologies and the application of computational methods in reverse engineering of gene regulatory networks (GRNs). The most commonly used GRNs models based on Boolean networks, Bayesian networks, relevance networks, differential and difference equations are described. A novel model for integration of prior biological knowledge in the GRNs inference is presented, too. The advantages and disadvantages of the described models are compared. The GRNs validation criteria are depicted. Current trends and further directions for GRNs inference using prior knowledge are given at the end of the paper.

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23

Feng, Xiaobing, and Miun Yoon. "Numerical Methods for Genetic Regulatory Network Identification Based on a Variational Approach." Computational Methods in Applied Mathematics 16, no. 1 (January 1, 2016): 77–103. http://dx.doi.org/10.1515/cmam-2015-0019.

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AbstractThis paper studies differential equation-based mathematical models and their numerical solutions for genetic regulatory network identification. The primary objectives are to design, analyze, and test a general variational framework and numerical methods for seeking its approximate solutions for reverse engineering genetic regulatory networks from microarray datasets. In the proposed variational framework, no structure assumption on the genetic network is presumed, instead, the network is solely determined by the microarray profile of the network components and is identified through a well chosen variational principle which minimizes an energy functional. The variational principle serves not only as a selection criterion to pick up the right solution of the underlying differential equation model but also provides an effective mathematical characterization of the small-world property of genetic regulatory networks which has been observed in lab experiments. Five specific models within the variational framework and efficient numerical methods and algorithms for computing their solutions are proposed and analyzed. Model validations using both synthetic network datasets and subnetwork datasets of Saccharomyces cerevisiae (yeast) and E. coli are performed on all five proposed variational models and a performance comparison versus some existing genetic regulatory network identification methods is also provided.

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24

Wardani, Retno Sulistyo, and Endang Mangunkusumo. "Gangguan transpor ion pada polip hidung." Oto Rhino Laryngologica Indonesiana 41, no. 2 (December 1, 2011): 78. http://dx.doi.org/10.32637/orli.v41i2.43.

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Background: Nasal polyps is an inflammation process effecting in epithelial natrium hiperabsorptionand decreased of chloride ion secretion. Purpose: To find out the role of nasal seroprotein in increasingthe hydraulic effect and improving cellular integrity in balancing the natrium hyperasborption in nasalpolyps patients following protocol treatment of endoscopic simple polypectomy and 6 weeks intranasalglucocorticoid. Method: Twenty-nine patients with naive bilateral nasal polyps were undergone protocoltreatment of endoscopic simple polypectomy followed by 6 weeks intranasal glucocorticoid. There were16 responder subjects and 13 non-responder subjects. Increased expresions of statherin (STATH) and prolactin-induced-protein (PIP) were obtained by microarray examination on the best five responder of paired samples pre and post treatment, and validated by real-time RT-PCR for 22 pairs samples (44nasal polyps tissue). Result: Increasing expression (foldchange) of STATH and PIP based on microarray were 115.33 (FDR 8.81) dan 26.45 (FDR 12.20) and the validation by real-time RT PCR demonstratedthe foldchange expression of 186.59 (95% CI 6.22–1024.97) in STATH expression and 17.64 (95% CI3.37–32.75) in PIP expression. Responder group showed higher transcription activity in gen STATH300.42 (95% CI 1.34–1257.32) compared to non-responder group of 72.76 (CI 95% 21.81–1285.91),while PIP in responder group showed 19.56 (CI 95% 1.75–130.70) and in non-responder group of 15.71(CI 95% 3.84–29.79). Conclusion: Gene expression comparison analysis by microarray, real-time RTPCR from the result of this study showed that STATH and PIP had a function for the improvement innasal polyps treatment protocol. Keywords: epithelial ion transport, nasal polyps, prolactin-induced-protein, statherin Abstrak : Latar belakang: Polip hidung adalah proses inflamasi yang mengakibatkan terjadinya hiperabsorpsiion natrium dan berkurangnya sekresi ion klorida. Tujuan: Untuk mengetahui peran nasal seroproteinyang meningkatkan efek hidraulik dan memperbaiki integritas sel untuk mengatasi hiperabsorpsi ionnatrium, akan dilakukan penelitian pada pasien polip hidung sebelum dan sesudah protokol pengobatandengan polipektomi sederhana endoskopik dan glukokortikoid intranasal selama enam minggu. Dua puluh sembilan pasien polip hidung bilateral yang dapat dievaluasi, dikelompokkan berdasarkankriteria klinis menjadi 16 subjek responder dan 13 subjek non-responder. Peningkatan ekspresi statherin(STATH) dan prolactin-induced-protein (PIP) diperoleh melalui pemeriksaan microarray pada 5 sampeldengan respons terapi terbaik. Selanjutnya 22 pasang sampel (44 jaringan) menjalani pemeriksaauntuk mengetahui ekspresi gen STATH dan PIP pada tingkat mRNA dengan pemeriksaan real-time RTPCR. Hasil: Penelitian ini mendapatkan peningkatan ekspresi (foldchange) STATH dan PIPberdasarkanpemeriksaan microarray 115,33 (FDR 8,81) dan 26,45 (FDR 12,20) dan setelah divalidasi ulang denganpemeriksaan real-time RT PCR didapatkan peningkatan ekspresi 186,59 (IK 95% 6,22–1024,97) (IK 95% 3,37–32,75). Kelompok responder menunjukkan aktivitas transkripsi yang lebih tinggi ermakna pada gen STATH sebesar 300,42 (IK 95% 1,34–1257,32) dibandingkan dengan kelomresponder 72,76 (IK 95% 21,81–1285,91) sedangkan PIP kelompok responder 19,56 (IK 95% 1,75–130,70)dan kelompok non-responder 15,71 (IK 95% 3,84–29,79). Kesimpulan: Analisis perbandingan gen berdasarkan pemeriksaan microarray, real-time RT PCR dari hasil penelitian ini menunjukkan bahwaSTATH dan PIP mempunyai peran untuk respons kesembuhan dalam protokol pengobatan polip hidung. Kata kunci: transpor ion epitel, polip nasi, statherin, prolactin-induced-protein

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Masrie, Marshaly Safira, and Jonas Nara Baringbing. "AMNIOSENTESIS: TINJAUAN MENYELURUH." Damianus: Journal of Medicine 19, no. 2 (November 27, 2020): 161–66. http://dx.doi.org/10.25170/djm.v19i2.1276.

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Pendahuluan: Amniosentesis adalah suatu prosedur invasif pengambilan cairan amnion untuk mendapatkan sel-sel janin dalam rangka pemeriksaan kromosom, salah satu teknik dan prosedur diagnosis prenatal yang diperkenalkan selama 10 tahun terakhir. Hal ini penting diperkenalkan karena besarnya insiden kelainan kromosom pada bayi yaitu 90 kejadian per 10.000 kelahiran.Tujuan: Artikel ini digunakan sebagai bahan pembelajaran untuk meningkatkan wawasan mengenai amniosentesis yang merupakan metode diagnostik prenatal invasif yang paling sering digunakan dengan tingkat keberhasilan yang cukup tinggi serta membantu agar dapat melakukan pencegahan dan deteksi dini penyakit keturunan dan kelainan bawaan pada janin yang belum lahir.Metode: Penulisan artikel ini menggunakan metode tinjauan naratif sebagai bagian dari studi literatureDiskusi: Amniosentesis memiliki tujuan lainnya seperti menilai tingkat pematangan paru janin dan mengetahui apakah terdapat infeksi pada janin. Prosedur amniosentesis biasanya dilakukan pada usia kehamilan 15 – 20 minggu. Jika prosedur amniosentesis dilakukan di bawah usia kehamilan 15 minggu, dapat meningkatkan kejadian keguguran. Analisis fosfolipid cairan amnion dapat menentukan tingkat pematangan paru janin. Cairan amnion juga dapat digunakan untuk analisis biokimia, studi molekuler, dan microarray chromosome analysis (CMA).

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Pramana, Setia, Dan Lin, Philippe Haldermans, Ziv Shkedy, Tobias Verbeke, Hinrich Göhlmann, An,De Bondt, Willem Talloen, and Luc Bijnens. "IsoGene: An R Package for Analyzing Dose-response Studies in Microarray Experiments." R Journal 2, no. 1 (2010): 5. http://dx.doi.org/10.32614/rj-2010-001.

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Otava, Martin, Rudradev Sengupta, Ziv Shkedy, Dan Lin, Setia Pramana, Tobias Verbeke, Philippe Haldermans, et al. "IsoGeneGUI: Multiple Approaches for Dose-Response Analysis of Microarray Data Using R." R Journal 9, no. 1 (2017): 14. http://dx.doi.org/10.32614/rj-2017-002.

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Ciaramella, Angelo, and Antonino Staiano. "On the Role of Clustering and Visualization Techniques in Gene Microarray Data." Algorithms 12, no. 6 (June 18, 2019): 123. http://dx.doi.org/10.3390/a12060123.

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As of today, bioinformatics is one of the most exciting fields of scientific research. There is a wide-ranging list of challenging problems to face, i.e., pairwise and multiple alignments, motif detection/discrimination/classification, phylogenetic tree reconstruction, protein secondary and tertiary structure prediction, protein function prediction, DNA microarray analysis, gene regulation/regulatory networks, just to mention a few, and an army of researchers, coming from several scientific backgrounds, focus their efforts on developing models to properly address these problems. In this paper, we aim to briefly review some of the huge amount of machine learning methods, developed in the last two decades, suited for the analysis of gene microarray data that have a strong impact on molecular biology. In particular, we focus on the wide-ranging list of data clustering and visualization techniques able to find homogeneous data groupings, and also provide the possibility to discover its connections in terms of structure, function and evolution.

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Shao, Jun, and Shein-Chung Chow. "Variable screening in predicting clinical outcome with high-dimensional microarrays." Journal of Multivariate Analysis 98, no. 8 (September 2007): 1529–38. http://dx.doi.org/10.1016/j.jmva.2004.12.004.

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Conlon, Erin M., Patrick Eichenberger, and Jun S. Liu. "Determining and analyzing differentially expressed genes from cDNA microarray experiments with complementary designs." Journal of Multivariate Analysis 90, no. 1 (July 2004): 1–18. http://dx.doi.org/10.1016/j.jmva.2004.02.007.

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Friedland, Shmuel, Amir Niknejad, and Laura Chihara. "A simultaneous reconstruction of missing data in DNA microarrays." Linear Algebra and its Applications 416, no. 1 (July 2006): 8–28. http://dx.doi.org/10.1016/j.laa.2005.05.009.

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Orr, Megan, and Peng Liu. "Sample Size Estimation while Controlling False Discovery Rate for Microarray Experiments Using the ssize.fdr Package." R Journal 1, no. 1 (2009): 47. http://dx.doi.org/10.32614/rj-2009-019.

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Bottomly, Daniel, Beth Wilmot, and Shannon,K McWeeney. "oligoMask: A Framework for Assessing and Removing the Effect of Genetic Variants on Microarray Probes." R Journal 6, no. 1 (2014): 159. http://dx.doi.org/10.32614/rj-2014-018.

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Zaninović, Luca, Ana Katušić Bojanac, and Marko Bašković. "Metode molekularne dijagnostike u prenatalnoj medicini." Medicina Fluminensis 58, no. 3 (September 1, 2022): 224–37. http://dx.doi.org/10.21860/medflum2022_280997.

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Prenatalna medicina u smislu probira kromosomskih abnormalnosti fetusa nastala je 70-ih godina prošlog stoljeća. U 21. stoljeću, s razvojem tehnologija napredne i brze analize genoma, kao što su kromosomalni microarray te sekvenciranje genoma sljedeće generacije, prenatalna dijagnostika proširila se s najčešćih aneuploidija na detekciju i brojnih drugih strukturalnih kromosomskih poremećaja (povećanje broja kopija gena, delecije, duplikacije), kao i monogenskih bolesti. Osim klasičnih invazivnih tehnika (biopsija korionskih resica, amniocenteza) kojima se prikupljaju stanice za citogenetičku i genomsku analizu, danas je moguće neinvazivno analizirati genom fetusa putem analize slobodne deoksiribonukleinske kiseline (DNK) (engl. cell-free deoxyribonucleic acid) izolirane iz krvi majke. S obzirom na svoju relativno veliku točnost, jednostavnost i mogućnost rane primjene, izgledno je da će ovakvo neinvazivno testiranje zamijeniti klasični probir u prvom tromjesečju koji je kombinirao biokemijske i fetalne ultrazvučne parametre. Nema sumnje da će neinvazivni probir analizom cfDNA, uz korištenje modernih tehnologija sekvenciranja, sve više postati dijagnostički iskoristiv u prenatalnoj medicini. Ipak, problem možda leži u analizi genomskih podataka gdje se katkad detektiraju promjene u slijedu nukleotida za koje je klinička signifikantnost nepoznata i još ne postoji jasno definiran postupnik kliničkog djelovanja nakon takvih podataka. Cilj ovog preglednog rada je iz različitih izvora, kliničkih podataka, preglednih članaka te metaanaliza izložiti koherentan pregled suvremenih probirnih i dijagnostičkih molekularnih metoda u prenatalnoj medicini. Istaknute su prednosti i mane korištenja metoda probira te analitičke mogućnosti pojedinih molekularnih metoda, s krajnjim kritičkim osvrtom i konceptualizacijom budućnosti ovakvih postupaka.

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Nguyen, Quynh-Thi Thuy, Tsai-Lien Huang, and Hao-Jen Huang. "Identification of Genes Related to Arsenic Detoxification in Rice Roots Using Microarray Analysis." International Journal of Bioscience, Biochemistry and Bioinformatics, 2014, 22–27. http://dx.doi.org/10.7763/ijbbb.2014.v4.304.

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Siakotou, Panagiota. "The Usual Suspects in Melanoma Pathogenesis and the Role of Tissue Microarrays Analysis in Melanoma Research." International Journal of Surgery & Surgical Techniques 2, no. 1 (2018). http://dx.doi.org/10.23880/ijsst-16000113.

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Samosir, Sefty M., Angghea Rachmiawaty, Ita Fatati, and Alamsyah Aziz. "Multiple Congenital Anomalies: Meningoencephalocele, Labiopalatoschisis and Clubfoot with Normal Chromosomal Analysis." Indonesian Journal of Obstetrics and Gynecology, January 28, 2022, 49–53. http://dx.doi.org/10.32771/inajog.v10i1.1366.

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Objective : To perform chromosomal microarray when similar case was found.Methods: Case reportCase : G1P0A0, 20 years-old, 23-24 weeks gestation, normal BMI, was diagnosed by ultrasonography with multiple congenital anomaly consisted by meningoencephalocele, labiopalatoschisis, and clubfoot. Amniocentesis was performed to manage karyotyping analysis and a result of 46 XY was obtained. Neonate was delivered with exact condition according to prenatal diagnosis and was demised 6 hours postnatal. Patient had no history of smoking or DM, and no familial congenital deformity. Patient was a worker in textile manufactory and inadequacy folic acid intake during pregnancy was known.Early suspicion of aneuploidy as cause of multiple congenital anomalies in this case was not proven otherwise. Serology test also found no congenital infection. Literature research indicated tendency of MTHFR polymorphisms. Genetic analysis such as chromosomal microarray to establish involvement of MTHFR polymorphism is needed.Conclusion : This case should behold as clinicians’ consideration to perform additional examination and patients counseling when similar anomaly was found during prenatal ultrasonography examination.Keyword : chromosomal microarray,karyotyping; MTHFR polymorphism, mutiple congenital anomaly. Abstrak Tujuan : Untuk melakukan pemeriksaan chromosomal microarray pada kasus yang sama.Metode: Laporan kasus.Kasus : G1P0A0, usia 20 tahun, hamil 23-24 minggu, didiagnosis secara ultrasonografi dengan kelainan kongenital multipel berupa meningoensefalokel, celah bibir-palatum, dan club foot. Amniosentesis dilakukan pada pasien untuk analisis kromosom dan didapatkan hasil 46 XY. Bayi lahir dengan kondisi yang sama dengan diagnosis prenatal tersebut dan meninggal 6 jam pasca salin. Dari hasil anamnesis dan pemeriksaan kami dapatkan pasien bukan perokok, tidak ada keluarga dengan cacat bawaan, indeks masa tubuh pasien normal. Pasien merupakan karyawan pabrik tekstil dan pasien tidak mengkonsumsi asam folat adekuat selama kehamilan. Dua data terakhir yang kemungkinan berkontribusi terjadinya kelainan kongenital multipel pada kasus ini, yaitu kontak dengan teratogen dan defisiensi asam folat. Dugaan awal kelainan kromosom sebagai penyebab kelainan kongenital multipel pada kasus ini tidak terbukti. Sayangnya, investigasi-investigasi lebih lanjut berupa analisis genetik, seperti pemeriksaan apakah terdapat polimorfisme MTHFR yang berhubungan dengan defisiensi asam folat pada kasus ini tidak dilakukan karena keluarga pasien menolak. Kesimpulan : Kasus ini sebaiknya menjadi bahan pertimbangan klinisi untuk melakukan pemeriksaan tambahan dan edukasi ke pasien pada saat menemukan kelainan yang sama pada pemeriksaan ultrasonografi prenatal.Keyword: chromosomal microarray; karyotyping; multipel kongenital anomali; polimorfisme MTHFR

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KARADAĞ, Aynur. "Akut ve Kronik Miyeloid Lösemili Hastalarda Prognostik miRNA İmzasının Biyoinformatik Analiz ile Karşılaştırılması." Medical Records, July 26, 2022. http://dx.doi.org/10.37990/medr.1118392.

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Aim: In this study, differentially expressed miRNA profiles were determined using high-throughput expression data from samples of AML and CML patients to identify miRNAs involved in the therapeutic response. Material and Methods: miRNA microarray datasets GSE142699 and GSE90773 were downloaded via the GEO database and analysis was performed with the online analysis tool GEO2R. Data no. GSE142699 was made with 24 control and 24 newly diagnosed AML patients, data no. GSE90773 was made with 8 control and 10 newly diagnosed CML patients. After the analysis, they were grouped according to fold change (FC) values and p

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Dubey, Aditya, and Akhtar Rasool. "Usage of Clustering and Weighted Nearest Neighbors for Efficient Missing Data Imputation of Microarray Gene Expression Dataset." Advanced Theory and Simulations, August 25, 2022, 2200460. http://dx.doi.org/10.1002/adts.202200460.

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"Differential Gene Screening and Bioinformatics Analysis of Epidermal Stem Cells and Dermal Fibroblasts During Skin Aging." International Journal of Diabetes & Metabolic Disorders 7, no. 2 (January 31, 2022). http://dx.doi.org/10.33140/ijdmd.07.01.09.

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Objective: To explore the differentially expressed genes (DEGs) and potential therapeutic targets of skin aging in GEO database by bioinformatics methods. Methods: Dermal fibroblasts and skin aging related data sets GSE110978 and GSE117763 were downloaded from GEO database, and epidermal stem cells and skin aging related data sets GSE137176 were downloaded. GEO2R was used to screen DEGs of candidate samples from the three microarrays, GO function analysis and KEGG pathway analysis were performed. Protein interaction network was constructed using String database, and hub gene was obtained by Cytoscape. NetworkAnalys was used to analyze the coregulatory network of DEGs and MicroRNA (miRNA), interaction with TF, and protein-chemical interactions of DEGs. Finally, DSigDB was used to determine candidate drugs for DEGs. Results: Six DEGs were obtained. It mainly involves the cytological processes such as response to metal ion, and is enriched in mineral absorption and other signal pathways. Ten genes were screened by PPI analysis. Gene-miRNA coregulatory network found that Peg3 and mmu-miR-1931 in DEGs were related to each other, and Cybrd1 was related to mmu-miR-290a-5p and mmu-miR-3082-5p. TF-gene interactions found that the transcription factor UBTF co-regulated two genes, Arhgap24 and Mpzl1. Protein-chemical Interactionsa analysis and identification of candidate drugs show results for candidate drugs. Conclusion: Try to explore the mechanism of hub gene action in skin aging progression, and to discover the key signaling pathways leading to skin aging, which may be a high risk of skin aging.

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