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Journal : Narra J

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A comparative assessment on gene expression classification methods of RNA-seq data generated using next-generation sequencing (NGS) Setia Pramana; I Komang Y. Hardiyanta; Farhan Y. Hidayat; Siti Mariyah
Narra J Vol. 2 No. 1 (2022): April 2022
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v2i1.60

Abstract

Next-generation sequencing or massively parallel sequencing have revolutionized genomic research. RNA sequencing (RNA-Seq) can profile the gene-expression used for molecular diagnosis, disease classification and providing potential markers of diseases. For classification of gene expressions, several methods that have been proposed are based on microarray data which is a continuous scale or require a normal distribution assumption. As the RNA-Seq data do not meet those requirements, these methods cannot be applied directly. In this study, we compare several classifiers including Logistic Regression, Support Vector Machine, Classification and Regression Trees and Random Forest. A simulation study with different parameters such as over dispersion, differential expression rate is conducted and the results are compared with two mRNA experimental datasets. To measure predictive accuracy six performance indicators are used: Percentage Correctly Classified, Area Under Receiver Operating Characteristic (ROC) Curve, Kolmogorov Smirnov Statistics, Partial Gini Index, H-measure and Brier Score. The result shows that Random Forest outperforms the other classification algorithms.
A comparative assessment on gene expression classification methods of RNA-seq data generated using next-generation sequencing (NGS) Pramana, Setia; Hardiyanta, I Komang Y.; Hidayat, Farhan Y.; Mariyah, Siti
Narra J Vol. 2 No. 1 (2022): April 2022
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v2i1.60

Abstract

Next-generation sequencing or massively parallel sequencing have revolutionized genomic research. RNA sequencing (RNA-Seq) can profile the gene-expression used for molecular diagnosis, disease classification and providing potential markers of diseases. For classification of gene expressions, several methods that have been proposed are based on microarray data which is a continuous scale or require a normal distribution assumption. As the RNA-Seq data do not meet those requirements, these methods cannot be applied directly. In this study, we compare several classifiers including Logistic Regression, Support Vector Machine, Classification and Regression Trees and Random Forest. A simulation study with different parameters such as over dispersion, differential expression rate is conducted and the results are compared with two mRNA experimental datasets. To measure predictive accuracy six performance indicators are used: Percentage Correctly Classified, Area Under Receiver Operating Characteristic (ROC) Curve, Kolmogorov Smirnov Statistics, Partial Gini Index, H-measure and Brier Score. The result shows that Random Forest outperforms the other classification algorithms.
Co-Authors ., Yunofri Achmad Fauzi Bagus Firmansyah Addin Maulana Aditama, Farhan Satria Aini Izzati, Fitri Alifatri, La Ode Alistin, Zharifah Dhiya Ayu Amnur, Muh. Alfian Ana Lailatul Fitriyani Ana Lailatul Fitriyani Anang Kurnia Arie Wahyu Wijayanto Arif Handoyo Marsuhandi Ariya Jalaksana, Faruq Arkandana, M. Tharif Astrinariswari Rahmadian Prasetyo Astuti, Erni Tri Bintang Yuliani Manalu, Jernita Busaina, Ladisa Cahyono, Bintang Dwitya Charvia Ismi Zahrani Cholifa Fitri Annisa Dandy Adetiar Al Rizki Dede Yoga Paramartha Dede Yoga Paramartha Deli, Nensi Fitria Dewi Krismawati Dewi Krismawati Dhiar Niken Larasati Diory Paulus Pamanik Erni Tri Astuti Erwin Tanur Fadila Utami, Nurul Fajar Fathur Rachman Fajar Fatur Rachman Farakh Khoirotun Nasida Farhan Y. Hidayat Fitriyani, Ana Lailatul Fitriyyah, Nur Retno Geri Yesa Ermawan Gilang Hidayat, Muhammad Hady Suryono Hanafi, Zulfaning Tyas Hardiyanta, I Komang Y. Hendrawan, Daffa Hidayat, Farhan Y. Hizir Sofyan Hulliyyatus Suadaa, Lya I Komang Y. Hardiyanta I Nyoman Setiawan Imam Habib Pamungkas Jane, Giani Jovita Khairani, Fitri Krisela Fabrianne, Elisse Krismawati, Dewi Ladisa Busaina Linta Ifada Linta Ifada Maftukhatul Qomariyah Virati Magfirah, Deanty Fatihatul Mariel, Wahyu Calvin Frans Maulana Faris Median Ramadhan, Alif Muhammad Farhan Muhammad Gazali, La Ode Muhammad Nur Aidi Muhammad Tharif Arkandana Mumtaz Siregar, Amir Munaf, Alfatihah Reno Maulani Nuryaningsih Soekri Putri Nasiya Alifah Utami Nazuli, Muhammad Fachry Nensi Fitria Deli Nisa Rahayu Ananda Suwendra, Made Nora Dzulvawan Novandra, Rio Nur Retno Fitriyyah Nurmalasari, Mieke Nurtia Nurtia Nurwijayanti Oktari, Rina S. Panuntun, Satria Bagus Paramartha, Dede Yoga Putro, Dimas Hutomo Rahman, Dimas Haafizh Rahmaniar, Masna Novita Rifqi Ramadhan Rimadeni, Yeni Rina S. Oktari Rini Rahani Rutba, Sita Aliya Safrizal Rahman Safrizal Rahman, Safrizal Salim Satriajati Salwa Rizqina Putri Satria Bagus Panuntun Satria Bagus Panuntun Satria Bagus Panuntun Satria Bagus Panuntun Silalahi, Agatha Siswantining, Titin SITI MARIYAH Siti Mariyah Soemarso, Ditoprasetyo Rusharsono Suadaa, Lya Hulliyyatus Sugiri Suhendra Widi Prayoga Takdir Tasriah, Etjih Thosan Girisona Suganda Thosan Girisona Suganda Tigor Nirman Simanjuntak Titin Siswantining Usman Bustaman Usman Bustaman Utami, Nandya Rezky Wahyu Calvin Frans Mariel Wirata Raja Panjaitan, Eurorea Wiwin Srimulyani Yuniarti Yuniarti Yuniarto, Budi Zen, Rizqi Annisa