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[국내논문] Deep Learning Framework for Predicting Alzheimer's Disease using Multi-omics Data
멀티오믹스 데이터를 활용한 알츠하이머 질병 예측 딥러닝 모델 개발 연구

韓國情報技術學會論文誌 = Journal of Korean institute of information technology, v.20 no.7, 2022년, pp.29 - 37  

Ha, Jihwan ,  Kong, Kyeongbo ,  Park, Donggun

초록이 없습니다.

참고문헌 (22)

  1. Kumar, Anil, Singh, Arti, Ekavali,. A review on Alzheimer's disease pathophysiology and its management: an update. Pharmacological reports : Pr, vol.67, no.2, 195-203.

  2. Vetrivel, Kulandaivelu S., Thinakaran, Gopal. Amyloidogenic processing of β-amyloid precursor protein in intracellular compartments. Neurology, vol.66, no.2,

  3. Lee, Virginia M.-Y., Trojanowski, John Q.. Mechanisms of Parkinson's Disease Linked to Pathological α-Synuclein: New Targets for Drug Discovery. Neuron, vol.52, no.1, 33-38.

  4. Roberson, M.R, Kolasa, K, Parsons, D.S, Harrell, L.E. Cholinergic denervation and sympathetic ingrowth result in persistent changes in hippocampal muscarinic receptors. Neuroscience, vol.80, no.2, 413-418.

  5. Lin, Eugene, Lin, Chieh-Hsin, Lane, Hsien-Yuan. Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease. International journal of molecular sciences, vol.22, no.15, 7911-.

  6. Park, Chihyun, Ha, Jihwan, Park, Sanghyun. Prediction of Alzheimer's disease based on deep neural network by integrating gene expression and DNA methylation dataset. Expert systems with applications, vol.140, 112873-.

  7. Nawaz, Hina, Maqsood, Muazzam, Afzal, Sitara, Aadil, Farhan, Mehmood, Irfan, Rho, Seungmin. A deep feature-based real-time system for Alzheimer disease stage detection. Multimedia tools and applications, vol.80, no.28, 35789-35807.

  8. Raza, M., Awais, M., Ellahi, W., Aslam, N., Nguyen, H.X., Le-Minh, H.. Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques. Expert systems with applications, vol.136, 353-364.

  9. Basheer, Shakila, Bhatia, Surbhi, Sakri, Sapiah Binti. Computational Modeling of Dementia Prediction Using Deep Neural Network: Analysis on OASIS Dataset. IEEE access : practical research, open solutions, vol.9, 42449-42462.

  10. Kaur, Swapandeep, Gupta, Sheifali, Singh, Swati, Gupta, Isha. Detection of Alzheimer’s Disease Using Deep Convolutional Neural Network. International journal of image and graphics, vol.22, no.3, 2140012-.

  11. Ha, Jihwan, Park, Chihyun. MLMD: Metric Learning for Predicting MiRNA-Disease Associations. IEEE access : practical research, open solutions, vol.9, 78847-78858.

  12. Tanveer, M., Richhariya, B., Khan, R. U., Rashid, A. H., Khanna, P., Prasad, M., Lin, C. T.. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease : A Review. ACM transactions on multimedia computing communications and applications, vol.16, no.1, 1-35.

  13. Ha, Jihwan, Park, Chihyun, Park, Chanyoung, Park, Sanghyun. IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization. Journal of biomedical informatics, vol.102, 103358-.

  14. Khan, Protima, Kader, Md. Fazlul, Islam, S. M. Riazul, Rahman, Aisha B., Kamal, Md. Shahriar, Toha, Masbah Uddin, Kwak, Kyung-Sup. Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances. IEEE access : practical research, open solutions, vol.9, 37622-37655.

  15. Ha, Jihwan, Park, Chihyun, Park, Chanyoung, Park, Sanghyun. Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization. Cells, vol.9, no.4, 881-.

  16. Mørk, Søren, Pletscher-Frankild, Sune, Palleja Caro, Albert, Gorodkin, Jan, Jensen, Lars Juhl. Protein-driven inference of miRNA–disease associations. Bioinformatics, vol.30, no.3, 392-397.

  17. Ha, Jihwan, Park, Chihyun, Park, Sanghyun. PMAMCA: prediction of microRNA-disease association utilizing a matrix completion approach. BMC systems biology, vol.13, no.1, 33-.

  18. Ha, Jihwan. MDMF: Predicting miRNA–Disease Association Based on Matrix Factorization with Disease Similarity Constraint. Journal of personalized medicine, vol.12, no.6, 885-.

  19. Correction: Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score. PLoS medicine, vol.14, no.3, e1002289-.

  20. Park, C., Yoon, Y., Oh, M., Yu, S.J., Ahn, J.. Systematic identification of differential gene network to elucidate Alzheimer's disease. Expert systems with applications, vol.85, 249-260.

  21. Munteanu, C.R., Fernandez-Lozano, C., Mato Abad, V., Pita Fernandez, S., Alvarez-Linera, J., Hernandez-Tamames, J.A., Pazos, A.. Classification of mild cognitive impairment and Alzheimer's Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data. Expert systems with applications, vol.42, no.15, 6205-6214.

  22. 10.1145/2939672.2939754 

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