Creating innovative bio-convergent technologies for better human life

bioeng_admin 2012-07-16 14:52:10
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download : ICC_global_lecture_2012_schedule28ProfLuonanChen29.pdf

 

학과 교수님 및 학생분들께.

8월6-9 4일간 개최되는 상하이생명과학연구소 첸박사님 글로벌렉처가 아래와 같이 개최되오니

관심있는 분들의 많은 참여 바랍니다.

                                                         = 아  래 =

Lecture schedule: 6~9 August 2012 / 10:00~12:00, 13:00~15:00 (4 hours/day * 4 days)

 

Venue: KAIST Main Campus, Computer Science Building (E3-1), Ohsangsoo Seminar Room (#4443)

 

 

 

 

 

(Mon) August 6, 2012

 

 

- Basic concepts in biomolecular networks and high throughput technologies

 

 

- Modeling gene regulatory networks and metabolic networks

 

 

- Reconstructing transcriptional regulatory networks

 

 

- Inferring protein interaction networks

 

 

 

 

 

(Tue) August 7, 2012

 

 

- Coexpression networks of complex diseases based on expression data

 

 

- Development and progression of liver cancer (chronic hepatitis B and C)

 

 

- Identifying network biomarkers for chronic hepatitis B and C hepatic lesion and revealing their disease

 

 

progression to hepatocellular carcinoma

 

 

 

 

 

(Wed) August 8, 2012

 

 

- Sudden deterioration and phase shift for complex diseases

 

 

- Dynamical network biomarkers for complex diseases

 

 

- Detecting early-warning signals for sudden deterioration of complex diseases (early diagnosis on

 

 

complex diseases)

 

 

- Network Ontology analysis and its application to complex diseases

 

 

 

 

 

(Thu) August 9, 2012

 

 

- Identifying master regulator candidates for diabetes progression by Network Screening

 

 

- TF activity network and its applications to type-2 diabetes

 

 

- Identifying dysfunctional modules and disease genes in congenital heart disease by a network-based

 

 

approach

 

 

- Identifying disease genes and module biomarkers with differential interactions for gastric cancer

 

 

- Revealing causal network modules of complex diseases with application to colorectal cancer by

 

 

integrating heterogeneous data sources