Lightning talks in Harvard, 3rd round in 2018
2018년 봄학기의 마지막 Lightning Talk!
학과장님이신 Xihong교수님, 하버드 Biostat 박사과정 dean이신 Paige 교수님 등 엄청난 교수님들의 화려한 라인업이었다. 이름값 만큼이나 Lightning talk는 5분의 talk라기보다는 lecture같았다. 녹화된 talk는 여기서 : Facebook Live link
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1. Franziska Michor
- Title: DNA methylation profiling of DNA for noninvasive early cancer detection.
(cf) DNA profiling of cancer-causing mutations
- Prognostic test, blood를 통해서
- Extraembroyonic tissues have a cancer-like methylome.
-> "Embroyonic" vs "Extra embryonic"
- Mouse genome -> Human genome
- "Methylation status"
- Exe-Hyper-CGIs can predict the cell type of origin.
- blood test를 하면 mutation에 따라 수많은 cancer의 risk를 estimate할 수 있지만, specific한 암에 대한 risk를 찾는 거는 또 다른 문제이다...
TED 에서 발표하시는 교수님
-------------------------------------------------------
2. Michael Hughs: AIDS 연구를 이끌어 가는 cBAR의 대장님!
- Title: Two study design question in infectious disease (Clinical Design)
(1) Convalescent plasma
- Hospital patients -> Randomize -> High dose/Low dose 하는 상황
-> What to do in a trial of a seasonal infection when sample size is not achieved at the end of the season?
-> If continue for another season, sample size would increase. Then, can we relax efficacy? How about the alpha level?
(2) Cluster randomized prevention trial
- High risk cluster에서 벗어나는 것
- Test a random sample of contros, or test everyone in a random. Sample of clusters?
---------------------------------------------------------
3. Xihong Lin: 하버드 Biostat 대장님!
- Title: Scalable statistical inference of massive genome exposome and phenome data.
- Whole genome sequencing: exposure data, electronic medical records를 통해서
- 인간의 3 billion basepairs, ACTG: whole genome sequencing
=> Current status in TOPMed: 40 projects, p>500M genetic variants
=> GSP (Genome Sequencing Program)
(1) WGS Analysis
- Signal detection: rare & weak --- which is sparse. (*) Jun Liu교수님의 2017년 Final 문제.. signal profiling -_-..
- High dimensional dense & spare alternatives
- Multiple phenotype (Multiple outcomes)
- Risk prediction
(2) Integrative analysis of different types and source of data
- High-dimensional causal meditation analysis: 이거 Giovanni 교수님도 관심있어 하시는 건데...
(3) Analysis of biobank data
- Biobank: Genome + EMR + Epidemiology + Imaging data + Wearable devices data
- Biobank는 현재 UK가 가장 잘 되어있다.
-----------------------------------------------------------
4. Paige Williams
- Title: Reproductive outcomes and ART
(1) Endoctrine disrupting chemicals(EDCs) and outcomes of Assisted Reproductive Technology(ART)
(2) Antiretroviral treatment(ART (?)) and adverse pregnancy outcomes among woman with HIV
- Gestational period is so~~ important
- Analysis of the IVF data
- Combinations of drugs
----------------------------------------------------------
- 생물 공부를 더 해야겠다. 생물통계학과에서 살아남으려면... 난 왜 생물통계학과를 왔지 ㅠㅠ 경영대학에서 수학과에서 생물통계학과라니... 너무 멀리 왔다.
학과장님이신 Xihong교수님, 하버드 Biostat 박사과정 dean이신 Paige 교수님 등 엄청난 교수님들의 화려한 라인업이었다. 이름값 만큼이나 Lightning talk는 5분의 talk라기보다는 lecture같았다. 녹화된 talk는 여기서 : Facebook Live link
-------------------------------------------------
1. Franziska Michor
- Title: DNA methylation profiling of DNA for noninvasive early cancer detection.
(cf) DNA profiling of cancer-causing mutations
- Prognostic test, blood를 통해서
- Extraembroyonic tissues have a cancer-like methylome.
-> "Embroyonic" vs "Extra embryonic"
- Mouse genome -> Human genome
- "Methylation status"
- Exe-Hyper-CGIs can predict the cell type of origin.
- blood test를 하면 mutation에 따라 수많은 cancer의 risk를 estimate할 수 있지만, specific한 암에 대한 risk를 찾는 거는 또 다른 문제이다...
TED 에서 발표하시는 교수님
-------------------------------------------------------
2. Michael Hughs: AIDS 연구를 이끌어 가는 cBAR의 대장님!
- Title: Two study design question in infectious disease (Clinical Design)
(1) Convalescent plasma
- Hospital patients -> Randomize -> High dose/Low dose 하는 상황
-> What to do in a trial of a seasonal infection when sample size is not achieved at the end of the season?
-> If continue for another season, sample size would increase. Then, can we relax efficacy? How about the alpha level?
(2) Cluster randomized prevention trial
- High risk cluster에서 벗어나는 것
- Test a random sample of contros, or test everyone in a random. Sample of clusters?
---------------------------------------------------------
3. Xihong Lin: 하버드 Biostat 대장님!
- Title: Scalable statistical inference of massive genome exposome and phenome data.
- Whole genome sequencing: exposure data, electronic medical records를 통해서
- 인간의 3 billion basepairs, ACTG: whole genome sequencing
=> Current status in TOPMed: 40 projects, p>500M genetic variants
=> GSP (Genome Sequencing Program)
(1) WGS Analysis
- Signal detection: rare & weak --- which is sparse. (*) Jun Liu교수님의 2017년 Final 문제.. signal profiling -_-..
- High dimensional dense & spare alternatives
- Multiple phenotype (Multiple outcomes)
- Risk prediction
(2) Integrative analysis of different types and source of data
- High-dimensional causal meditation analysis: 이거 Giovanni 교수님도 관심있어 하시는 건데...
(3) Analysis of biobank data
- Biobank: Genome + EMR + Epidemiology + Imaging data + Wearable devices data
- Biobank는 현재 UK가 가장 잘 되어있다.
-----------------------------------------------------------
4. Paige Williams
- Title: Reproductive outcomes and ART
(1) Endoctrine disrupting chemicals(EDCs) and outcomes of Assisted Reproductive Technology(ART)
(2) Antiretroviral treatment(ART (?)) and adverse pregnancy outcomes among woman with HIV
- Gestational period is so~~ important
- Analysis of the IVF data
- Combinations of drugs
----------------------------------------------------------
- 생물 공부를 더 해야겠다. 생물통계학과에서 살아남으려면... 난 왜 생물통계학과를 왔지 ㅠㅠ 경영대학에서 수학과에서 생물통계학과라니... 너무 멀리 왔다.
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