In recent years, there has been great interest in the possibility of using artificial limbs as an extension of the human body as well as replacement of lost limbs. In this paper, we develop a sixth finger system as an extension of the human body. We then investigate how an extra robotic thumb, that works as a sixth finger and gives somatosensory feedback to the user, modifies the body schema, and also affecting the self-perception of existing limbs. The sixth robotic finger is controlled with the thumb of the opposite hand, and contact information is conveyed via electrostimulation to the tip of the thumb controlling the movement. We conducted reaching task experiments with and without visual information to evaluate the level of embodiment of the sixth robotic finger and the modification of the self-perception of the finger controlling the system. The experimental results indicate that not only the sixth finger is incorporated into the body schema of the user, but also the body schema of the controlling finger is modified; ability of the brain to adapt to different scenarios and geometries of the body is also implied.

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The cancer cell gene expression data in general has a very large feature and requires analysis to find out which genes are strongly influencing the specific disease for diagnosis and drug discovery. In this paper several methods of supervised learning (decision tree, naïve bayes, neural network, and deep learning) are used to classify cancer cells based on the expression of the microRNA gene to obtain the best method that can be used for gene analysis. In this study there is no optimization and tuning of the algorithm to assess the fitness of algorithms. There are 1881 features of microRNA gene expresion, 22 cancer classes based on tissue location. A simple feature selection method is used to test the comparison of the algorithm. Expreriments were conducted with various scenarios to asses the accuracy of the classification. Keywords: Cancer, MicroRNA, classification, Decision Tree, Naïve Bayes, Neural Network, Deep Learning AbstrakData ekpresi gen sel kanker secara umum memiliki feature yang sangat banyak dan memerlukan analisa untuk mengetahui gen apa yang sangat berpengaruh terhadap spesifik penyakit untuk diagnosis dan juga penemuan obat. Pada tulisan ini beberapa metode supervised learning (decisien tree, naïve bayes, neural network, dan deep learning) digunakan untuk mengklasifikasi sel kanker berdasarkan ekpresi gen microRNA untuk mendapatkan metode terbaik yang dapat digunakan untuk analsisa gen. Dalam studi ini tidak ada optimasi dan tuning dari algoritma untuk menguji kemampuan algortima secara umum. Terdapat 1881 feature epresi gen microRNA pada 25 kelas kanker berdarkan lokasi tissue. Metode sederhana feature selection digunakan juga untuk menguji perbandingan algoritma tersebut. Exprerimen dilakukan dengan berbagai sekenario untuk menguji akurasi dari klasifikai.

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