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Diagnosing un-occurred diseases by dynamic network biomarkers @ NIG International symposium 2017
NIG International symposium 2017, Commemorating the 30th Anniversary of DDBJ was held in Mishima Citizens Cultual Hall in shizuoka, Japan. On the third day of the symposium (29 May), oral sessions were held.
In this talk, Luonan Chen makes a presentation entitled "Diagnosing un-occurred diseases by dynamic network biomarkers". (33:24)
All presentations are listed in the YouTube list.
Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt, and may cause a critical transition from one state to another at a tipping point. Here, we develop a model-free method to detect early-warning signals of such critical transitions (or un-occurred diseases), even with only a small number of samples. Specifically, we theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent sudden deterioration before the critical transition occurs. Based on theoretical analyses, we show that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample, e.g., high-throughput data. We employ gene expression data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by related experimental data (e.g., liver cancer, lung injury, influenza, type-2 diabetes) and functional analysis. DNB can also be used for the analysis of nonlinear biological processes, e.g., cell differentiation process.
In this talk, Luonan Chen makes a presentation entitled "Diagnosing un-occurred diseases by dynamic network biomarkers". (33:24)
All presentations are listed in the YouTube list.
Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt, and may cause a critical transition from one state to another at a tipping point. Here, we develop a model-free method to detect early-warning signals of such critical transitions (or un-occurred diseases), even with only a small number of samples. Specifically, we theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent sudden deterioration before the critical transition occurs. Based on theoretical analyses, we show that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample, e.g., high-throughput data. We employ gene expression data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by related experimental data (e.g., liver cancer, lung injury, influenza, type-2 diabetes) and functional analysis. DNB can also be used for the analysis of nonlinear biological processes, e.g., cell differentiation process.
見どころダイジェスト
- 00:19 1. Once disease occurs, difficult to be cured～
- 01:57 2. Disease progression～
- 04:48 3. Can we make early diagnosis?～
- 06:42 4. Bifurcation～
- 07:10 5. System～
- 07:51 6. Mathematical Scheme～
- 09:51 7. Potential Energy Function (1-dim) ～
- 11:31 8. Main Theorem (n-dim)～
- 15:06 9. Three Diseases～
- 16:00 10. Dynamical changes of whole mouse network (3452 genes and 9238 links) including DNB during disease progression for lung injury～
- 16:51 11. Dynamical changes in whose PPI network (2291 genes) during disease progression for HCC～
- 18:36 12. Human HBV-Induce Liver Cancer (Dynamical Network Biomarker)～
- 19:12 13. Clinic symptomatic or asymptomatic subjects in influenza dataset (H3N2/Wisconsin)～
- 20:43 14. Clinical data～
- 20:59 15. Predict the pre-disease state before the disease～
- 21:37 16. Diabetes Rats (microarray data)～
- 22:34 17. DNB of GK rats (two critical states)～
- 22:48 18. Tow critical states during the progression～
- 23:14 19. DNB and DEG～
- 24:54 20. Nonlinear Phenomena～
- 27:29 21. Herding behavior, Connectivity Avalanche, Critical Slowing-Down～
- 22. signal states from the oligotrophic state to the eutrophic state～ (28m01)
- 28:34 23. Financial bubbles～
- 29:02 24. DNB in Biology and Medicine～
- 29:12 25. Molecular Validation of DNB～
- 30:23 26. DNB in Medicine～
- 30:46 27. Hunt for cancer ‘tipping point’ heats up - Nature～
- 32:07 28. Conclusion～
動画ファイルのダウンロード
170705_NIGsympo_201_chen.mov