Elucidation of the Mechanism of Indonesian Traditional Medicine (Jamu) Based on Case Studies of Type 2 Diabetes Networks

  • Vitri Aprilla Handayani
  • Eduward Hottua Hutabarat
Keywords: Fuzzy Clustering, Graph Tri-Partite, Jamu, Network, Type 2 Diabetes

Abstract

Plant medicine is a kind of plant that can be used to solve various problems in the human body either due to illness or other disorders. Jamu is an Indonesian traditional medicine. It is essentially herbal medicine that made from natural materials taken from several parts of medicinal plants which contain some substances and compounds that important and beneficial for the body. So far, the efficacy for some type of jamu has been proven empirically. In this paper, to fill this gap, we intend to elucidate the mechanism of jamu using computational base approach. This research focus to a jamu for Type 2 Diabetes (T2D) which prescription consist of four plants: Ginger (Zingiber officinale), Bratawali (Tinospora crispa), Sembung (Blumea balsamifera), and Bitter Melon (Momordica charantia). The framework of analysis starts with generating the network with 3 components: active compounds, proteins target and gene ontology. After that, will implement clustering to those components using concept of tri-partite graphs fuzzy clustering. The main ingredients of 15 active compounds have high score probability which divided in different cluster by the pair of active compounds that have high synergic effect. T2D is not solely caused by protein abnormalities from insulin secretion (isoform insulin-degrading enzyme 1), but also caused by other proteins that involved in the inhibition of insulin in the pancreas. These proteins are Alpha-2C adrenergic receptors, beta-1 adrenergic receptor, and peroxisome proliferator-activated receptor delta, which have high probability in the same group.

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Published
2020-02-28
How to Cite
Vitri Aprilla Handayani, & Eduward Hottua Hutabarat. (2020). Elucidation of the Mechanism of Indonesian Traditional Medicine (Jamu) Based on Case Studies of Type 2 Diabetes Networks. International Journal of Engineering and Management Research, 10(1), 87-91. https://doi.org/10.31033/ijemr.10.1.16