Abs trakBioinformatika merupakan ilmu multidisipliner yang melibatkan berbagai bidang ilmu. Salah satu aplikasi dari bioinformatika adalah dalam proses desain obat berbantuan komputer. Dalam desain obat berbantuan komputer salah satu langkah awal yang dibutuhkan adalah mencari suatu rongga pada protein, rongga tersebut nantinya untuk melekat suatu ligan (partikel kecil) maupun protein yang merupakan partikel dari calon obat.
DNA merupakan unsur yang sangat penting dan mendasar pada setiap organisme. Sekuensing DNA dapat dimanfaatkan untuk menentukan identitas suatu organisme dengan cara membandingkan urutan DNA nya dengan DNA lain yang sudah diketahui. Integrasi Seleksi data dan Extreme Learning Machine (IDELM) ini dipilih karena data DNA merupakan data yang besar serta karakteristik datanya yang kebanyakan adalah data yang imbalance. Pada proses penelitian data yang akan diolah terlebih dahulu diuraikan fragmennya dengan simulator MetaSim, selanjutnya dilakukan ekstraksi fitur dengan menggunakan n-mers, kemudian dilakukan proses klasifikasi dengan IDELM. Hasil dari pengklasifikasian tersebut memiliki performa yang baik, karena dengan ekstraksi fitur 3-mers maupun 4-mers performanya di atas 80%.
Recently, computer-aided drug design is developing rapidly. The first step of computer-aided drug design is to find a protein-ligand binding site, which is a pocket or cleft on the surface of the protein being used to bind a ligand (drug). In this study, the binding site is defined as a binary classification problem to differ the location which can bind or cannot bind the ligand. Classification method used in this research is Extreme Learning Machine (ELM), because this method has fast learning process. In the real case, the dataset usually has imbalanced data. One of them is to predict binding site. Imbalanced data can be solved in several ways. In this study we carried out the integration of data selection and classification to overcome the inconsistency problem. The performance of integrating between data selection and Extreme Learning Machine to predict protein-ligand binding site is measured by using recall, specificity, G-mean and CPU time. The average of recall, specificity, G-mean and CPU time in this research are respectively, those are 91.8472%, 97.071%, 94.2647%, and 2.79 second.
A successful understanding on how to make computers learn would open up many new uses of computers and new levels of competence and customization. A detailed understanding on information- processing algorithms for machine learning might lead to a better understanding of human learning abilities and disabilities. There are many type of machine learning that we know, which includes Backpropagation (BP), Extreme Learning Machine (ELM), and Support Vector Machine (SVM). This research uses five data that have several characteristics. The result of this research is all the three investigated models offer comparable classification accuracies. This research has three type conclusions, the best performance in accuracy is BP, the best performance in stability is SVM and the best performance in CPU time is ELM for bioinformatics data.
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