Preprint / Version 1

Machines and Networks: How Graphs Bridge Machines with Analytical Processes Towards –Omics Studies


Machine Learning, Graph Theory, Omics, Network-based Learning, Graph-based Algorithms


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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


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.  


Abu-Aisheh, Z., Gaüzere, B., Bougleux, S., Ramel, J., Brun, L., & Raveaux, R. et al. (2017). Graph edit distance contest: Results and future challenges. Pattern Recognition Letters, 100, 96-103. doi: 10.1016/j.patrec.2017.10.007

Bowd, C., Weinreb, R., Balasubramanian, M., Lee I., Jang, G., & Yousefi, S. et al. (2014). Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers. Plos ONE, 9(1), e85941. doi:10.1371/journal.pone.0085941

Cai, J., Luo, J., Wang, S., & Yang, S. (2018). Feature selection in machine learning: A new perspective. Neurocomputing, 300, 70-79. doi: 10.1016/j.neucom.2017.11.077

Chasman, D., Fotuhi Siahpirani, A., & Roy, S. (2018). Network-based approaches for analysis of complex biological systems.

Colwell, L. (2018). Statistical and machine learning approaches to predicting protein–ligand interactions. Current Opinion In Structural Biology, 49, 123-128. doi: 10.1016/

Cuperlovic-Culf, M. (2018). Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling. Metabolites, 8(1), 4.

Guégan, D., & Hassani, B. (2018). Regulatory learning: How to supervise machine learning models? An application to credit scoring. The Journal Of Finance And Data Science. doi: 10.1016/j.jfds.2018.04.001

Guo, H., Zhang, F., Chen, J., Xu, Y., & Xiang, J. (2017). Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer’s Disease. Frontiers in Neuroscience, 11, 615.

Li, J., Yu, Y., Yang, Z., & Tang, L. (2011). Breast Tissue Image Classification Based on Semi-supervised Locality Discriminant Projection with Kernels. Journal Of Medical Systems, 36(5), 2779-2786. doi:10.1007/s10916-011-9754-6

Mijalkov, M., Kakaei, E., Pereira, J. B., Westman, E., Volpe, G., & for the Alzheimer’s Disease Neuroimaging Initiative. (2017). BRAPH: A graph theory software for the analysis of brain connectivity. PLoS ONE, 12(8), e0178798.

Nahid, A.-A., & Kong, Y. (2017). Involvement of Machine Learning for Breast Cancer Image Classification: A Survey. Computational and Mathematical Methods in Medicine, 2017, 3781951.

Pavlopoulos, G. A., Secrier, M., Moschopoulos, C. N., Soldatos, T. G., Kossida, S., Aerts, J., … Bagos, P. G. (2011). Using graph theory to analyze biological networks. BioData Mining, 4, 10.

Ribeiro de Paula, D., Ziegler, E., Abeyasinghe, P. M., Das, T. K., Cavaliere, C., Aiello, M., … Soddu, A. (2017). A method for independent component graph analysis of resting?state fMRI. Brain and Behavior, 7(3), e00626.

Rost, B., Radivojac, P., & Bromberg, Y. (2016). Protein function in precision medicine: deep understanding with machine learning. FEBS Letters, 590(15), 2327–2341.

Sas, K. M., Karnovsky, A., Michailidis, G., & Pennathur, S. (2015). Metabolomics and Diabetes: Analytical and Computational Approaches. Diabetes, 64(3), 718–732.

Schrider, D., & Kern, A. (2018). Supervised Machine Learning for Population Genetics: A New Paradigm. Trends In Genetics, 34(4), 301-312. doi: 10.1016/j.tig.2017.12.005

Shen, R., & Guda, C. (2014). Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks. BioMed Research International, 2014, 439476.

Stavrakas, V., Melas, I. N., Sakellaropoulos, T., & Alexopoulos, L. G. (2015). Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory. PLoS ONE, 10(5), e0128411.

Tsai, C., Hsu, Y., Lin, C., & Lin, W. (2009). Intrusion detection by machine learning: A review. Expert Systems With Applications, 36(10), 11994-12000. doi: 10.1016/j.eswa.2009.05.029

Wang, J., Bensmail, H., Yao, N., & Gao, X. (2013). Discriminative sparse coding on multi-manifolds. Knowledge-Based Systems, 54, 199-206. doi: 10.1016/j.knosys.2013.09.004

Wang, M., & Fernandez-Gonzalez, R. (2017). (Machine-)Learning to analyze in vivo microscopy: Support vector machines. Biochimica Et Biophysica Acta (BBA) - Proteins And Proteomics, 1865(11), 1719-1727. doi: 10.1016/j.bbapap.2017.09.013

Xie, J., Douglas, P. K., Wu, Y. N., Brody, A. L., & Anderson, A. E. (2017). Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms. Journal of Neuroscience Methods, 282, 81–94.

Yin, W., Garimalla, S., Moreno, A., Galinski, M. R., & Styczynski, M. P. (2015). A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems. BMC Systems Biology, 9, 49.

Yu, Y., Pan, Z., Hu, G., & Ren, H. (2017). Graph classification based on sparse graph feature selection and extreme learning machine. Neurocomputing, 261, 20-27. doi:10.1016/j.neucom.2016.03.110

Zhang, W., Chien, J., Yong, J., & Kuang, R. (2017). Network-based machine learning and graph theory algorithms for precision oncology. NPJ Precision Oncology, 1(1), 25.