根據過去在流行病學上的研究發現，帕金森症患者患多種癌症的風險通常 較低，但患黑色素瘤的風險卻較高。而黑色素瘤患者患帕金森症的風險也比預 期還高，因此在帕金森症和黑色素瘤這兩種病症之間存在雙向關聯，此篇研究 的目的就是期望藉由最大生成樹和基因本體，分析兩種病症相關基因間的互動，以便更深入探討此兩種病症的關聯。
首先，我們從 NHGRI-EBI GWAS Catalog 中得到和帕金森症相關的基因， 從 Kyoto Encyclopedia of Genes and Genomes (KEGG)得到和黑色素生成作用相關的基因，再從 GeneMANIA 中得到這些基因間的互動關係。
接著，我們分別從基因本體和基因互動兩種不同的角度出發，來分析兩病症相關基因間的關聯。
從基因本體的角度，我們可以知道帕金森症相關基因與黑色素生成作用相關基因之間的互動可能集中在分子功能（MF）的5種功能類別和生物過程（BP）的2種功能類別上。
從基因互動的角度，我們利用最大生成樹篩選出重要的基因互動，找出哪些基因互動可能是重要的，而這些基因又是透過哪些功能產生互動。
最後，我們可視化了重要基因互動，並將其和黑色素生成路徑圖結合在一起。 透過這張圖，我們可以知道帕金森症相關基因與黑色素生成作用相關基因的互動可能集中在黑色素生成路徑上的哪些地方。

According to past epidemiological studies, patients with Parkinson's disease are usually at a lower risk of multiple cancers, but at a higher risk of melanoma. The risk of melanoma patients suffering from Parkinson’s disease is also higher than expected. Therefore, there is a bidirectional association between Parkinson’s disease and melanoma. The purpose of this research is to use Maximum Spanning Tree (MST) and Gene Ontology (GO) to analyze the interactions between genes related to the two diseases in order to further explore the relationship between the two diseases.
First, we got the genes related to Parkinson's disease from the NHGRI-EBI GWAS Catalog and the genes related to melanoma from Kyoto Encyclopedia of Genes and Genomes (KEGG), and then got the interactions between these genes from GeneMANIA.
Next, we analyzed the association between Parkinson’s disease-related genes and melanogenesis-related genes from two different perspectives, Gene Ontology and gene interactions.
From the viewpoint of Gene Ontology, we know that the interactions between Parkinson’s disease-related genes and melanogenesis-related genes may be focused on 5 functional categories of Molecular Function (MF) and 2 functional categories of Biological Process (BP).
From the viewpoint of gene interactions, we used MST to screen out important gene interactions and found that which gene interactions may be important, and through which functions these genes interact.
Finally, we visualized the combination of important gene interactions and melanogenesis pathway map. Through this map, we can know where the interactions of Parkinson's disease-related genes and melanogenesis-related genes may be concentrated on the melanogenesis pathway.

摘要----------------------------------------------------2
Abstract------------------------------------------------3
Table of Contents---------------------------------------4
List of Figures-----------------------------------------5
List of Tables------------------------------------------6
1. Introduction-----------------------------------------7
2. Methods----------------------------------------------10
2.1 Gene Identification-------------------------------10
2.2 Gene Interactions from GeneMANIA------------------11
2.3 Gene Ontology-------------------------------------11
2.4 Maximum Spanning Tree-----------------------------15
2.5 Combination of MST and GO-------------------------15
3. Discussion-------------------------------------------29
4. Conclusion-------------------------------------------33
Reference-----------------------------------------------34

1. Ye, Q.; Wen, Y.; Al-Kuwari, N.; Chen, X., Association between Parkinson’s disease and melanoma: Putting the pieces together. Frontiers in aging neuroscience 2020, 12, 60.
2. Buniello, A.; MacArthur, J. A. L.; Cerezo, M.; Harris, L. W.; Hayhurst, J.; Malangone, C.; McMahon, A.; Morales, J.; Mountjoy, E.; Sollis, E., The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic acids research 2019, 47 (D1), D1005-D1012.
3. Kanehisa, M.; Goto, S., KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research 2000, 28 (1), 27-30.
4. Warde-Farley, D.; Donaldson, S. L.; Comes, O.; Zuberi, K.; Badrawi, R.; Chao, P.; Franz, M.; Grouios, C.; Kazi, F.; Lopes, C. T., The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic acids research 2010, 38 (suppl_2), W214-W220.
5. Ashburner, M.; Ball, C. A.; Blake, J. A.; Botstein, D.; Butler, H.; Cherry, J. M.; Davis, A. P.; Dolinski, K.; Dwight, S. S.; Eppig, J. T., Gene ontology: tool for the unification of biology. Nature genetics 2000, 25 (1), 25-29.
6. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Research 2021, 49 (D1), D325-D334.
7. Yu, G.; Li, F.; Qin, Y.; Bo, X.; Wu, Y.; Wang, S., GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 2010, 26 (7), 976-978.
8. Yu, G., Gene ontology semantic similarity analysis using GOSemSim. In Stem Cell Transcriptional Networks, Springer: 2020; pp 207-215.
9. Manjang, K.; Tripathi, S.; Yli-Harja, O.; Dehmer, M.; Emmert-Streib, F., Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance. Scientific reports 2020, 10 (1), 1-16.
10. Graham, R. L.; Hell, P., On the history of the minimum spanning tree problem. Annals of the History of Computing 1985, 7 (1), 43-57.
11. Benabdallah, F. Z.; El Maliani, A. D.; Lotfi, D.; El Hassouni, M. In Analysis of the Over-Connectivity in Autistic Brains Using the Maximum Spanning Tree: Application on the Multi-Site and Heterogeneous ABIDE Dataset, 2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM), IEEE: 2020; pp 1-7.
12. Asano, T.; Bhattacharya, B.; Keil, M.; Yao, F. In Clustering algorithms based on minimum and maximum spanning trees, Proceedings of the fourth annual symposium on Computational Geometry, 1988; pp 252-257.
13. Xu, Y.; Olman, V.; Xu, D., Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees. Bioinformatics 2002, 18 (4), 536-545.
14. Wang, R.-S.; Zhang, S.; Zhang, X.-S.; Chen, L. In Identifying modules in complex networks by a graph-theoretical method and its application in protein interaction networks, International Conference on Intelligent Computing, Springer: 2007; pp 1090-1101.
15. Jeyabalan, S.; Raj, V. C., A Novel Technique for Analysis of Protein to Protein Interaction using Efficient Minimum Spanning Tree Techniques. Indian Journal of Science and Technology 2016, 9 (41), 1-5.
16. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N. S.; Wang, J. T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research 2003, 13 (11), 2498-2504.