Welcome to Computational and Network Biology Lab
Research Topics
Network biology
From unidimensional to multidimensional biological network
Leading questions:
- What are the impacts caused by interaction rewiring in the cellular system?
- How the biological networks react as the cellular system is disturbed or attacked?
In the living system, biomolecules are continuously forming and breaking interactions to turn on or shut down biological processes inside or outside the cells. Accordingly, network or systems biology are capable of studying living systems comprehensively. We have developed a computational framework based on data science approach to investigate the biological networks and extract information from them. This approach can do top-down analysis that incorporate large scale omics data into networks to identify the critical interactions of interests; and bottom-up discovery to predict the biological processes in which the interactions of interests from small scale experiments are involved. We have successfully applied this approach to discover the potential mechanism of cancer development. In the future, we will focus on constructing multidimensional biological network and develop a framework to investigate this complicate network.
Tumor homo- and heterogeneity
Pan-cancer network and precision medicine
Leading questions:
- How to apply computational biology to study tumor homo- and heterogeneity?
- How to develop the precision medicine and pan-cancer treatment from biological networks?
With the accumulation of cancer omics data, tumor homo-/heterogeneity can be studied at large scale. The studies of tumor heterogeneity demonstrated the subtypes of cancers beyond the current cancer classification and further emphasized the importance of the precision medicine; homogeneity studies discover the shared characteristics between cancers and to identify the core of carcinogenesis. We have applied the network biology to study pan-cancer and identified a common property in immunoediting between cancers. We also found that the genes influencing cancer patient survival are highly exclusive between cancers; in pan-cancer, survival influential genes associated with poor and better survival are enriched in the biological processes of cell proliferation and energy metabolism respectively. We are now working on developing the machine learning model to discover precision medicine and identify the patient characteristics of cancer.
News
我們的研究" Regulatory feedback loops bridge the human gene regulatory network and regulate carcinogenesis "發表在Briefings in Bioinformatics
我們的研究" Regulatory feedback loops bridge the human gene regulatory network and regulate carcinogenesis "獲選推薦代表智慧計算學門參加科技部之破壞性創新論文遴選。提名就是一種肯定!!!
我們的研究" Regulatory feedback loops bridge the human gene regulatory network and regulate carcinogenesis "獲得紀念沈力揚教授講師(助理教授)級研究獎助
獲邀擔任International Conference on Biomedical Engineering and Technology (ICEBT) 之technical committee
恭喜毓庭獲得2019年Japanese Cancer Association Travel Grant
恭喜馥媺獲選2018年Japanese Cancer Association Oral presentation
恭喜馥媺獲得107年陽明大學生物醫學資訊研究所畢業生生命科學院院長獎
恭喜老師獲頒國立陽明大學107學年度良師益友導師獎
恭喜盈辰獲得2017年Japanese Cancer Association Travel Grant
恭喜盈辰獲選2017年Japanese Cancer Association Oral presentation
恭喜盈辰獲得106年陽明大學生物醫學資訊研究所畢業生所長獎
恭喜老師獲頒國立陽明大學106學年度良師益友導師獎