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− | <strong> | + | <strong>Welcome to the U-M Big Data Summer Institute 2019 Wiki!</strong> |
+ | == Reading Material == | ||
− | + | === Machine Learning Group === | |
+ | ====Research Lecture Slides ==== | ||
+ | *[https://drive.google.com/file/d/1HF9cD0cnBEShUVn2Lq9vQYpRxT2ZZ8DJ/view?usp=sharing Introduction] | ||
+ | *[https://drive.google.com/file/d/1LdxCkiFDmWGHcBYsHUw_W54cM6RETp8T/view?usp=sharing Explore MIMIC] | ||
+ | *[https://drive.google.com/file/d/15yChJcEgzHjrn_bhYF7wqaaLh5NMCkzA/view?usp=sharing Getting x and y] | ||
+ | *[https://drive.google.com/file/d/17BCOb3-t5ZJ3eE0Yy9rjlOGn0cn36PUp/view?usp=sharing Some tips] | ||
+ | *[https://drive.google.com/file/d/1T8PEwWfi7PA8uMnuA9_kF6BSz69A6VKM/view?usp=sharing Sample Pipeline] | ||
+ | *[https://drive.google.com/file/d/1OBckV_yjuYrl36tOF1lAKiOcWJA8-q34/view?usp=sharing Training Pipleline] | ||
+ | *[https://drive.google.com/file/d/1i1nel98HYV1mSpro5SaDoMdOqMH5i5Gr/view?usp=sharing CNN] | ||
+ | *[https://drive.google.com/file/d/1y14n4kl9u4LBQ9M5AnP6knFZpfwgOUAW/view?usp=sharing LSTM] | ||
+ | *[https://drive.google.com/file/d/1XF-P43lIhYXU0HnS0pyGrIvPzF4HiB0B/view?usp=sharing Structuring] | ||
+ | *[https://drive.google.com/file/d/12HgspYuZAVVSU49c8AEs8FPqZyoF3c1E/view?usp=sharing Dataset and DataLoader] | ||
+ | *[https://drive.google.com/file/d/1vnUj-Stf5arWNyLguCPuCm1OYRwZGGr1/view?usp=sharing pytorch models] | ||
+ | *[https://drive.google.com/file/d/1-r9sC3GMH_qVL3SCsPBgkDh9upS3hfEM/view?usp=sharing Model Development] | ||
+ | *[https://drive.google.com/file/d/1n2md6F3XQ85WOgMV_20G_DgFqmmBgU3k/view?usp=sharing Population] | ||
+ | *[https://drive.google.com/file/d/1HEAWEbwP7R1o-UZ1NcrMp2C0Ub3ivAzK/view?usp=sharing Benchmark Features] | ||
+ | *[https://drive.google.com/file/d/1QpFDYzmyQBvvT9tk4Nr7EYIbh8mMmMOr/view?usp=sharing Inclusion Exclusion] | ||
+ | ====Lecture Notes==== | ||
+ | *[https://drive.google.com/file/d/1ogusN8iQqfEfmarjKUqKtFZaSX-LZw3k/view?usp=sharing Recurrent Neural Networks] | ||
+ | *[https://drive.google.com/file/d/11sJeWngMmTpX_PWcobdxX180_xEzOfV-/view?usp=sharing Convolutional Neural Networks] | ||
+ | *[https://drive.google.com/file/d/17m8Y5DcmJCeHgB1spA_b27WYJvzyhLi4/view?usp=sharing Deep Learning] | ||
+ | *[https://drive.google.com/open?id=19qcnGMIdoYZ9mvPwsU2kAvZcqArmd6oO BDSI Lecture] | ||
+ | *[https://drive.google.com/open?id=1gZtk4LjLDcdUSBEsNZ1qTJ2UHRDL--JO Flux Guide] | ||
+ | *[https://drive.google.com/open?id=1XDX7hkicATX0y7cVDb9PDCsPZo1gLdPi Python Guide] | ||
+ | *[https://drive.google.com/open?id=1kueXa-kFk6DiKVqItTEL-sFT_xmHg9Fb Python Tutorial] | ||
+ | ====Readings==== | ||
+ | *[https://drive.google.com/file/d/1dvEdmapBY-jgdLOCZLdPwDgbybTWEni9/view?usp=sharing ARF Epidemiology] | ||
+ | *[https://drive.google.com/file/d/1EzJxIVlXgkGGge20eHGC5HGtl-E7CPO_/view?usp=sharing CNN for Sentence Clarification] | ||
+ | *[https://drive.google.com/file/d/1rdUGIYh7WkI4qbaJEQjaIXxaXtYYDvQN/view?usp=sharing Learning from Heterogenous Temporal Data] | ||
+ | *[https://drive.google.com/file/d/12aIplGTH982iBvR7Kao6UBk2lY9Q3311/view?usp=sharing MIMC 3] | ||
+ | *[https://drive.google.com/file/d/1jQVuZfnbrxzu79ujr_uKYQGvJVmkoJGy/view?usp=sharing MIMIC Benchmarks and Multitask RNN] | ||
+ | *[https://drive.google.com/file/d/1uh5U_-3NIoUUM7aItA39iTGNkgwI01M7/view?usp=sharing RNNs for Multivariate Time Series] | ||
+ | *[https://drive.google.com/file/d/1r1y4gez3o46mbqkaWP3umHJv6o6uvzf-/view?usp=sharing TREWScore for Septic Shock] | ||
+ | *[https://drive.google.com/file/d/1EZnegIgxpmKNLorKwtkh8Veh5kIJu-lV/view?usp=sharing TREWScore Supplement] | ||
− | == | + | === Genomics Group === |
− | * [https:// | + | |
− | * [https://www. | + | ===== Lectures ===== |
− | * [https:// | + | *[https://drive.google.com/open?id=19jBLxGLRKtJD-5Id-H3d0AK0Vu_dfGko BDSI Genomics Intro] |
− | * [https://www. | + | *[https://drive.google.com/file/d/1nZkKKbwgRWkfEeyEGzsueJ8Dh_Zjahpv/view?usp=sharing PopGen Intro] |
− | * [https://www. | + | |
+ | ===== Intro Exercises ===== | ||
+ | *[https://drive.google.com/open?id=1Wl1JensClieYfVNIcTWzJVxN_N7Tugte Single Cell Exercise] | ||
+ | *[https://drive.google.com/open?id=18yGPNL0ZhxJR6Rya4K1IDnH1E8Yz6sjS Single Cell R Code] | ||
+ | *[https://drive.google.com/file/d/1hFlCoxcw758XGfm_LrY6T9kuH6c0R6pJ/view?usp=sharing Intro Integrative] | ||
+ | |||
+ | ===== Papers ===== | ||
+ | ====== Population Genetics ====== | ||
+ | *[https://drive.google.com/file/d/1UF6ADS9oc8zOh8R5zvuoRo72viYvMKoA/view?usp=sharing 1000 Genomes Project] | ||
+ | *[https://drive.google.com/file/d/1ppsbWyfDzwoi_zA-9F6mt05YV3B3y173/view?usp=sharing Cavalli SforzaHGDP 2005] | ||
+ | *[https://drive.google.com/file/d/1sULpGhl34Cn-TiDtctu78VnLR92oYVh6/view?usp=sharing LiHGDP 2008] | ||
+ | *[https://drive.google.com/file/d/1NACFUWFfl9ojMl_R6fGOV-YvBbnJkjlb/view?usp=sharing NovembreNature 2008] | ||
+ | ======Single Cell RNA ====== | ||
+ | *[https://drive.google.com/file/d/1Tzv1s58R0GVJBS0xYi2RiSYb4al25go_/view?usp=sharing Macosko_MouseRetinaDropSeq] | ||
+ | *[https://drive.google.com/file/d/1vg15r6G7jrXSgZXOxsnWKFwbBQnzr8iB/view?usp=sharing Single cell makes big data] | ||
+ | ======Transcriptomics ====== | ||
+ | *[https://drive.google.com/file/d/1SctF-SUft870PGg-43caqyN-B9sWxMh_/view?usp=sharing GamazonPrediXCan 2015] | ||
+ | *[https://drive.google.com/file/d/1ioY2blWyZxDKonibl9baSXHAiUO2jNEU/view?usp=sharing GusevTWAS 2006] | ||
+ | |||
+ | ==== Online videos to better understand genetics and genomics ==== | ||
+ | |||
+ | ===== ''Genetics'' ===== | ||
+ | * [https://www.youtube.com/watch?v=ubq4eu_TDFc&list=PLF9969C74FAAD2BF9 Introduction to Genetics by 23andMe (5 videos)] | ||
+ | * [https://www.youtube.com/watch?v=Mehz7tCxjSE TED-Ed : How Mendel's pea plants helped us understand genetics - Hortensia Jiménez Díaz] | ||
+ | * [https://www.youtube.com/watch?v=TU44tR0hJ8A Genetic Recombination and Gene Mapping by Bozeman Science] | ||
+ | * [https://www.youtube.com/user/UsefulGenetics/playlists Useful Genetics : A college-level comprehensive genetics course with 292 lectures offered by Rosie Redfield at UBC] | ||
+ | |||
+ | ===== ''Useful 3D Animations'' ===== | ||
+ | * [https://www.youtube.com/watch?v=gG7uCskUOrA From DNA to protein - 3D Animation] | ||
+ | * [https://www.youtube.com/watch?v=SMtWvDbfHLo DNA Transcription - 3D Animation] | ||
+ | * [https://www.youtube.com/watch?v=aVgwr0QpYNE DNA splicing - 3D Animation] | ||
+ | * [https://www.youtube.com/watch?v=TfYf_rPWUdY mRNA Translation - 3D Animation] | ||
+ | * [https://www.youtube.com/watch?v=gbSIBhFwQ4s How DNA is packaged - 3D Animation] | ||
+ | * [https://www.youtube.com/watch?v=J3HVVi2k2No The Central Dogma - 3D Animation] | ||
+ | |||
+ | ===== ''Gene Regulation and Epigenetics'' ===== | ||
+ | * [https://www.youtube.com/watch?v=kp1bZEUgqVI Epigenetics Lecture by SciShow] | ||
+ | * [https://www.youtube.com/watch?v=dES-ozV65u4 Hi-C Technique : A 3D map of the Human Genome] | ||
+ | * [https://www.youtube.com/watch?v=TwXXgEz9o4w The ENCODE Project] | ||
+ | * [https://www.youtube.com/watch?v=cK-OGB1_ELE RNAi by Nature Video] | ||
+ | |||
+ | ===== ''Sequencing Technologies'' ===== | ||
+ | * [https://www.youtube.com/watch?v=AhsIF-cmoQQ TED-Ed : The race to sequence the human genome - Tien Nguyen] | ||
+ | * [https://www.youtube.com/watch?v=vL7ptq2Dcf0 DropSeq - Droplet-based Single Cell Sequencing by McCarroll Lab] | ||
+ | |||
+ | === Data Mining on Large Complex Datasets === | ||
+ | * | ||
+ | ====Papers==== | ||
+ | * | ||
+ | == 2019 Presentations == | ||
+ | |||
+ | === <u>Week 1</u> === | ||
+ | ==== Day 1: June 17 ==== | ||
+ | * | ||
+ | ==== Day 2: June 18 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 3: June 19 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 4: June 20 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 4: June 21 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ====Day 5: June 22 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | === <u>Week 2</u> === | ||
+ | ==== Day 6: June 25 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 7: June 26 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 8: June 27 ==== | ||
+ | * | ||
+ | ==== Day 9: June 28 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 10: June 29 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | === <u>Week 3</u> === | ||
+ | ==== Day 11: July 2 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 12: July 3 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 13: July 4 (NO CLASS) ==== | ||
+ | |||
+ | ==== Day 14: July 5 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 15: July 6 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | === <u>Week 4</u> === | ||
+ | ==== Day 16: July 9 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 17: July 10 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 18: July 11 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 19: July 12 ==== | ||
+ | * | ||
+ | ==== Day 20: July 13 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | === <u>Week 5</u> === | ||
+ | ==== Day 21: July 16 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 22: July 17 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 23: July 18 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 24: July 19 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | ==== Day 25: July 20 ==== | ||
+ | * | ||
+ | =====Recorded Lectures===== | ||
+ | * | ||
+ | == Day 29: Symposium == | ||
+ | '''2019 Professor Lectures Presentations''' | ||
+ | * | ||
+ | '''2019 Student Poster Presentations''' | ||
+ | * | ||
+ | '''2018 Professor Lectures Presentations''' | ||
+ | *[https://drive.google.com/file/d/19QJ9SmqsCP66urThKu5hlSxZdobyAd7z/view?usp=sharing 2018 Symposium Flyer] | ||
+ | *[https://drive.google.com/file/d/1ukz_fd4kLGR2JaqjbAdFz6HuRmkD9BdM/view?usp=sharing Symposium Welcome Remarks] | ||
+ | *[https://drive.google.com/file/d/1yUAUsxe_aElBdfZDItsdkBFKgJs9Ig8N/view?usp=sharing Calibration Concordance] - Chen | ||
+ | *[https://drive.google.com/file/d/1U8Rt4mDPAmZuqv9RXhsPkMnL1VmoI3G0/view?usp=sharing Data Science and Predictive Health Analytics] - Dinov | ||
+ | *[https://drive.google.com/file/d/118HtI_euaMBmCl7-bibqDYJPRlablmOY/view?usp=sharing Models of Human Choice] - Feinberg | ||
+ | *[https://drive.google.com/file/d/1O3q32tp2J87Tmigz75ozVAonX-YYKwam/view?usp=sharing Linking Tumor with Personalized Medicine] - Rao | ||
+ | *[https://drive.google.com/file/d/1eFBzOkXbNyaP4s0o7kemHVSbhgxfqUmz/view?usp=sharing Humanist Approach to Data Science] - Schutt | ||
+ | *[https://drive.google.com/file/d/1kuDFENM8UwzxL_ve70jwca717RTDjzXA/view?usp=sharing Big Data in the Social Sciences] - Titunik | ||
+ | *[https://drive.google.com/file/d/19bnSFP9xPQg7i6lQ1bdO439Gj7iyf1R9/view?usp=sharing Detecting Epistasis in Large Scale Genetic Mapping] - Zhou | ||
+ | '''2018 Student Poster Presentations''' | ||
+ | *[https://drive.google.com/file/d/1TGNqkCAV9eBJ_-zHbiU-yo3N4Aowm_HB/view?usp=sharing Imaging Group Presentation] | ||
+ | *[https://drive.google.com/file/d/1S01_WV6wfJ6BE2Op-01byw0WFUZadPjR/view?usp=sharing Machine Learning Group Presentation] | ||
+ | *[https://drive.google.com/file/d/1LlZgR4TAlyYaS88bQ6qgZncWFuS0lFuC/view?usp=sharing Genetics Group Presentation] | ||
+ | *[https://drive.google.com/file/d/1HqWZFGjHgIYOoqqlpQxc0qn9y7PTa4hD/view?usp=sharing Data Mining Presentation] | ||
+ | '''2017 Student Poster Presentations''' | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-WFNPZzd5TndqZ0U/view?usp=sharing Data Mining/ Machine Learning] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-S2xrUFM0ZEI3dGM/view?usp=sharing Electronic Health Records (EHR)] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-NEZ1YnQyVW0tREE/view?usp=sharing Genomics] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-cDVJNUtIcjRjcFE/view?usp=sharing Imaging] | ||
+ | =====2017 Symposium Reference Files===== | ||
+ | *[https://drive.google.com/file/d/1zwuVp6_sIRIlKx79-i0uZRp6Gtooi6g8/view?usp=sharing 2017 Symposium Program] | ||
+ | *[https://drive.google.com/file/d/18FfpzqxwPSunJfHM3pHDxYYggAPlKgWX/view?usp=sharing 2017 Symposium Flyer] | ||
+ | ======2017 Symposium Projects====== | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-Zm5RWHByeWdUd2M/view?usp=sharing A Time-to-Event Analysis of Heart Failure via Electronic Health Records] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-UEIwRVNZaVJXRFk/view?usp=sharing Melanoma Detection by Classifying Skin Lesion Images] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-VEk4RExSSkV3NGM/view?usp=sharing Classifying Skin Lesions Images Using Adaptive Boosting] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-RDR1UmtsV3hFUVk/view?usp=sharing Machine Learning Classification of Skin Lesion Images] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-YWs0alJGdTA3UE0/view?usp=sharing Genomics: Genome Storage and Assembly] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-SU1KWFdPcWZzMEk/view?usp=sharing Predicting the Transcriptome from the Genome] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-VGhDZlZZeHAyRDQ/view?usp=sharing Classification of Cell Types from Peripheral Mononuclear Blood Cells] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-bzRTUTlQek9JRGM/view?usp=sharing EHR-Based Study of Long-Term Infectious Diseases] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-VjRsZkJfalVNbVk/view?usp=sharing Visualizing Lab and Phenotype Associations Using PheWAS and Electronic Health Records] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-NXJoemNibTFxZXc/view?usp=sharing Data Mining: Microenvironment Microarray Spot Based Approach for Cell Prediction] | ||
+ | *[https://drive.google.com/file/d/0B2ht_TCS6xC-VDdJQmtOZHR6RE0/view?usp=sharing Estimating Cell Growth with Machine Learning and Data Mining] | ||
+ | |||
+ | == Additional Resources == | ||
+ | * [[DataCamp Resources]] | ||
+ | * [[https://drive.google.com/file/d/15pPcBQrTBj9ze9VthpTZLmkEa3yRaz8T/view?usp=sharing Daily Schedule]] - Last update July 3, 2018 |
Revision as of 23:39, 3 June 2019
Welcome to the U-M Big Data Summer Institute 2019 Wiki!
Contents
- 1 Reading Material
- 2 2019 Presentations
- 3 Day 29: Symposium
- 4 Additional Resources
Reading Material
Machine Learning Group
Research Lecture Slides
- Introduction
- Explore MIMIC
- Getting x and y
- Some tips
- Sample Pipeline
- Training Pipleline
- CNN
- LSTM
- Structuring
- Dataset and DataLoader
- pytorch models
- Model Development
- Population
- Benchmark Features
- Inclusion Exclusion
Lecture Notes
- Recurrent Neural Networks
- Convolutional Neural Networks
- Deep Learning
- BDSI Lecture
- Flux Guide
- Python Guide
- Python Tutorial
Readings
- ARF Epidemiology
- CNN for Sentence Clarification
- Learning from Heterogenous Temporal Data
- MIMC 3
- MIMIC Benchmarks and Multitask RNN
- RNNs for Multivariate Time Series
- TREWScore for Septic Shock
- TREWScore Supplement
Genomics Group
Lectures
Intro Exercises
Papers
Population Genetics
Single Cell RNA
Transcriptomics
Online videos to better understand genetics and genomics
Genetics
- Introduction to Genetics by 23andMe (5 videos)
- TED-Ed : How Mendel's pea plants helped us understand genetics - Hortensia Jiménez Díaz
- Genetic Recombination and Gene Mapping by Bozeman Science
- Useful Genetics : A college-level comprehensive genetics course with 292 lectures offered by Rosie Redfield at UBC
Useful 3D Animations
- From DNA to protein - 3D Animation
- DNA Transcription - 3D Animation
- DNA splicing - 3D Animation
- mRNA Translation - 3D Animation
- How DNA is packaged - 3D Animation
- The Central Dogma - 3D Animation
Gene Regulation and Epigenetics
- Epigenetics Lecture by SciShow
- Hi-C Technique : A 3D map of the Human Genome
- The ENCODE Project
- RNAi by Nature Video
Sequencing Technologies
- TED-Ed : The race to sequence the human genome - Tien Nguyen
- DropSeq - Droplet-based Single Cell Sequencing by McCarroll Lab
Data Mining on Large Complex Datasets
Papers
2019 Presentations
Week 1
Day 1: June 17
Day 2: June 18
Recorded Lectures
Day 3: June 19
Recorded Lectures
Day 4: June 20
Recorded Lectures
Day 4: June 21
Recorded Lectures
Day 5: June 22
Recorded Lectures
Week 2
Day 6: June 25
Recorded Lectures
Day 7: June 26
Recorded Lectures
Day 8: June 27
Day 9: June 28
Recorded Lectures
Day 10: June 29
Recorded Lectures
Week 3
Day 11: July 2
Recorded Lectures
Day 12: July 3
Recorded Lectures
Day 13: July 4 (NO CLASS)
Day 14: July 5
Recorded Lectures
Day 15: July 6
Recorded Lectures
Week 4
Day 16: July 9
Recorded Lectures
Day 17: July 10
Recorded Lectures
Day 18: July 11
Recorded Lectures
Day 19: July 12
Day 20: July 13
Recorded Lectures
Week 5
Day 21: July 16
Recorded Lectures
Day 22: July 17
Recorded Lectures
Day 23: July 18
Recorded Lectures
Day 24: July 19
Recorded Lectures
Day 25: July 20
Recorded Lectures
Day 29: Symposium
2019 Professor Lectures Presentations
2019 Student Poster Presentations
2018 Professor Lectures Presentations
- 2018 Symposium Flyer
- Symposium Welcome Remarks
- Calibration Concordance - Chen
- Data Science and Predictive Health Analytics - Dinov
- Models of Human Choice - Feinberg
- Linking Tumor with Personalized Medicine - Rao
- Humanist Approach to Data Science - Schutt
- Big Data in the Social Sciences - Titunik
- Detecting Epistasis in Large Scale Genetic Mapping - Zhou
2018 Student Poster Presentations
- Imaging Group Presentation
- Machine Learning Group Presentation
- Genetics Group Presentation
- Data Mining Presentation
2017 Student Poster Presentations
2017 Symposium Reference Files
2017 Symposium Projects
- A Time-to-Event Analysis of Heart Failure via Electronic Health Records
- Melanoma Detection by Classifying Skin Lesion Images
- Classifying Skin Lesions Images Using Adaptive Boosting
- Machine Learning Classification of Skin Lesion Images
- Genomics: Genome Storage and Assembly
- Predicting the Transcriptome from the Genome
- Classification of Cell Types from Peripheral Mononuclear Blood Cells
- EHR-Based Study of Long-Term Infectious Diseases
- Visualizing Lab and Phenotype Associations Using PheWAS and Electronic Health Records
- Data Mining: Microenvironment Microarray Spot Based Approach for Cell Prediction
- Estimating Cell Growth with Machine Learning and Data Mining
Additional Resources
- DataCamp Resources
- [Daily Schedule] - Last update July 3, 2018