Machines and Networks: How Graphs Bridge Machines with Analytical Processes Towards –Omics Studies
Keywords:
Machine Learning, Graph Theory, Omics, Network-based Learning, Graph-based AlgorithmsAbstract
Lembar pernyataan
Yth Moderator RINarxiv
Bahwa saya menyatakan:
1) Sebagai penulis artikel berjudul "Machines and Networks: How Graphs Bridge Machines with Analytical Processes Towards –Omics Studies". Melalui surel ini saya menyatakan bahwa artikel ini berstatus (pilih salah satu):
B. Preprint yang belum dikirimkan ke jurnal manapun
2) bahwa artikel ini bukan merupakan karya original. Seandainya di kemudian hari ditemukan ada unsur plagiarisme (sengaja atau tidak sengaja), maka itu adalah tanggung jawab saya dan tim penulis.
Angganararas Lungidningtyas, Jeremias Ivan, Muhammad Khasib Umam, Arli Aditya Parikesit
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ABSTRACT:
Machine learning algorithm has made its appeal throughout the years as a powerful tool to analyze, develop, and predict how a specific subset of data can function and behave. By implementing other relevant algorithms, such as graph theory, it has made significant improvements, both in algorithms as well as its implementation in the biological field, computational processing, and even business and social studies. The objective of this paper is to give a brief overview of how machine learning implementations work side by side with graph-based learning algorithms to improve and resolve challenges in the mentioned fields.
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