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[¥Í¸êºÓ¤h¯Z] | 1052[¥ÍÂå¸ê±M°Q(ºÓ¯Z¥Í¸êI)] |
Instructors | ªL®¶¼y¦Ñ®v¡B¤ý¬ê¶W¦Ñ®v |
Location | ¹Ï¸ê¤j¼Ó403±Ð«Ç (Rm. 403, Library, Information, and Research Building) |
Time | Monday,13:20-15:10 |
Website | http://video.ym.edu.tw/course/view.php?id=161 |
1052[¥ÍÂå¸ê±M°Q(ºÓ¯Z¥Í¸êI)]-³W©w |
Date | Speaker | Topic | Note |
---|---|---|---|
02¤ë13¤é | - | ¶}¾Ç²Ä¤@¶g | |
02¤ë20¤é | - | ||
02¤ë27¤é | - | ¤G¤G¤K¼u©Ê©ñ°² | |
03¤ë06¤é | A | Mapping the effects of drugs on the immune system | paper |
03¤ë13¤é | B | Mapping the effects of drugs on the immune system | discussion |
03¤ë20¤é | B | A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy | paper |
03¤ë27¤é | A | A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy | discussion |
04¤ë03¤é | - | ²M©ú¸`¼u©Ê©ñ°² | |
04¤ë10¤é | - | ´Á¤¤¦Ò¶g | |
04¤ë17¤é | - | ½Ð¦P¾Ç°Ñ¥[ºÓ¤GÁ`µû¶q | |
04¤ë24¤é | B | Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis | paper |
05¤ë01¤é | A | Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis | discussion |
05¤ë08¤é | - | ||
05¤ë15¤é | A | Early and multiple origins of metastatic lineages within primary tumors | paper |
05¤ë22¤é | - | ||
05¤ë29¤é | - | ºÝ¤È¸`¼u©Ê©ñ°² | |
06¤ë05¤é | B | Early and multiple origins of metastatic lineages within primary tumors | discussion |
06¤ë12¤é | - | ´Á¥½¦Ò¶g |
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[¥Í¸êºÓ¤h¯Z] | 1052[¥ÍÂå¸ê±M°Q(ºÓ¯Z¥Í¸êII)] |
Instructors | ¶À«ÛµØ¦Ñ®v¡B·¨¥Ã¥¿¦Ñ®v¡BÁéÖö¤è¦Ñ®v |
Location | ¹Ï¸ê¤j¼Ó411±Ð«Ç (Rm. 411, Library, Information, and Research Building) |
Time | Monday,13:20-15:10 |
Website | http://video.ym.edu.tw/course/view.php?id=162 |
1052[¥ÍÂå¸ê±M°Q(ºÓ¯Z¥Í¸êII)]-³W©w |
Date | Speaker | Topic | Note |
---|---|---|---|
02¤ë13¤é | - | ¶}¾Ç²Ä¤@¶g | |
02¤ë20¤é | - | - | |
02¤ë27¤é | - | ¤G¤G¤K¼u©Ê©ñ°² | |
03¤ë06¤é | ¬Iµq°a | A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data | |
03¤ë13¤é | ªL¨È¬X | Dual RNA-seq reveals viral infections in asthmatic children without respiratory illness which are associated with changes in the airway transcriptome | |
03¤ë20¤é | ÁÂÃLÜö | An analytical workflow for accurate variant discovery in highly divergent regions | |
03¤ë27¤é | - | - | |
04¤ë03¤é | - | ²M©ú¸`¼u©Ê©ñ°² | |
04¤ë10¤é | - | ´Á¤¤¦Ò¶g | |
04¤ë17¤é | - | ½Ð¦P¾Ç°Ñ¥[ºÓ¤GÁ`µû¶q | |
04¤ë24¤é | ¾G³ÕÀç | Prediction of drugs having opposite effects on disease genes in a directed network | |
05¤ë01¤é | ±i§µY | Pancancer modelling predicts the context-specific impact of somatic mutations on transcriptional programs | |
05¤ë08¤é | ¤ýµYµ¾ | Fast motif matching revisited high-order PWMs, SNPs and indels | |
05¤ë15¤é | ¬Iµq°a | Building a genetic risk model for bipolar disorder from genome-wide association data with random forest algorithm | |
ªL¨È¬X | Huntington¡¦s disease blood and brain show a common gene expression pattern and share an immune signature with Alzheimer¡¦s disease | ||
05¤ë22¤é | - | - | |
05¤ë29¤é | - | ºÝ¤È¸`¼u©Ê©ñ°² | |
06¤ë05¤é | ÁÂÃLÜö | Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers | |
¾G³ÕÀç | Disentangling genetic and environmental risk factors for individual diseases from multiplex comorbidity networks | ||
06¤ë12¤é | ±i§µY | ||
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[Âå¸êºÓ¤h¯Z] | 1052[¥ÍÂå¸ê±M°Q(Âå¸êºÓ³Õ)] |
Instructors | ±i³Õ½×¦Ñ®v¡B§Å©[«~¦Ñ®v |
Location | ¹Ï¸ê¤j¼Ó402±Ð«Ç (Rm. 402, Library, Information, and Research Building) |
Time | Monday,13:20-15:10 |
Website/ Information |
Seminar on Big Data for Health Special Issue of JBHI http://jbhi.embs.org/2015/07/08/special-issue-big-data-for-health/ Articles in this Special Issue
Grading:Participation 40%, Presentation 40%, Term Project (<5 pages, by IMRDC Review structure) 20%. |
Date | Speaker | Topic | Note |
---|---|---|---|
02¤ë13¤é | ½Òµ{²Ä¤@¤Ñ¤W½Ò¡A¤½§GÂå¸ê²ÕºÓ³Õ±M°Q³ø§i¤è¦¡¤Î¶¶§Ç | ||
02¤ë20¤é | - | ||
02¤ë27¤é | - | ¤G¤G¤K¼u©Ê©ñ°² | |
03¤ë06¤é | ¨HªYª´ ¶À°Ò§D ¾G±Ò®ä |
Predicting Days in Hospital Using Health Insurance Claims ¡V Xie et al | |
03¤ë13¤é | - | ||
03¤ë20¤é | - | Á{®É°±½Ò | |
03¤ë27¤é | - | ||
04¤ë03¤é | - | ²M©ú¸`¼u©Ê©ñ°² | |
04¤ë10¤é | ¾G¬f§u ¦¿»ñ ±i¤S¤É |
Predicting Asthma-Related Emergency Department Visits Using Big Data ¡V Ram et al | |
04¤ë17¤é | - | ½Ð¦P¾Ç°Ñ¥[ºÓ¤GÁ`µû¶q | |
04¤ë24¤é | ¸ªYæP ¸µqÝ ¤ý¨|À¦ |
Hierarchical Classification of Large-Scale Patient Records for Automatic Treatment Stratification ¡V Mei et al | |
05¤ë01¤é | - | ||
05¤ë08¤é | §õ«l½n ²¸s®¦ ¶À¥°¼Ý |
We Feel: Mapping emotion on Twitter ¡V Larsen et al | |
05¤ë15¤é | ¬I¥à¬v ±iµú»Í ½²¬FÀM |
Symmetrical Compression Distance for Arrhythmia Discrimination in Cloud-based Big Data Services ¡V Lillo-Castellano et al | ¸É½Ò¡]¸É3/20¡^ |
05¤ë22¤é | - | ||
05¤ë29¤é | - | ºÝ¤È¸`¼u©Ê©ñ°²¡A6/3(¤»)¸É½Ò | |
06¤ë03¤é | ªô«Ûº_ ªL©v¼Ý ¼ï¬L¦t |
Optimal Drug Prediction from Personal Genomics Profiles ¡V Sheng et al | ¸É½Ò¡]¸É6/5¡A¤@¦¸¤W¨â°ó¡^ |
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Toward Non-invasive Quantification of Brain Radioligand Binding by Combining Electronic Health Records and Dynamic PET Imaging Data ¡V Mikhno et al | ||
06¤ë05¤é | - | °±½Ò | |
06¤ë12¤é | - | ´Á¤¤¦Ò¶g |