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2021 Big Ten Augmented Intelligence Bowl
The Midwest, despite multiple leading Engineering and Computer Science programs and Medical schools, is not well represented in the latest AI/ML literature. As a newly launched institute at Northwestern University, the Institute for Augmented Intelligence in Medicine (I.AIM) is driven to raise the profile of both I.AIM and other Midwest schools doing great work in this area. To that end, I.AIM is hosting our 1st Annual Big 10 Augmented Intelligence Bowl. We have chosen to focus this year on AI/ML applications to address Health Disparities, a very challenging topic which we hope will bring out some creative ideas.  
This competition will be held as a two-part event and is intended to create a collaborative and supportive learning environment for the teams.  In April we will convene a multidisciplinary team from each Big 10 school.  This initial phase of the competition will evaluate the teams on a set of criteria, including quality, feasibility, scalability, and presentation. From that first phase, we will select teams to enter the next phase of the competition, provide them with resources, mentoring, and educational seminars/workshops. They will have 6 months to develop their ideas for the final round. We will then bring them back in the fall for the final competition with additional criteria of progress and execution. 
Students will be exposed to many industry and academic leaders in the course of the competition. Additionally, student teams will help build a supportive community for continuing collaborations.
Big Ten Bowl Website and Information
BTAA Team Application
Northwestern Team Application
I.AIM Seminar Series

Bringing Machine Learning Models to the Bedside at Scale

March 30th, 2021 - 12:00PM CST

Dr. Karandeep Singh

Dr. Singh is a nephrologist with a background in biomedical informatics who uses machine learning methods to model electronic health record and registry data in support of a learning health system. He directs the Machine Learning for Learning Health Systems lab which focuses on using machine learning and biomedical informatics methods to understand and improve health at scale. He also chairs the Michigan Medicine Clinical Intelligence Committee, which oversees the implementation of machine learning models across the University of Michigan's health system.

Advanced Molecular Analysis (AMA)

"We will profile and integrate personal multi-omics data (such as genomics, transcriptomics, epigenomics, etc.) to study human disease at single cell resolution."

-Feng Yue, PhD, Director, Center for Advanced Molecular Analysis

Many human diseases are associated with certain genetic signatures, either inherited from parents or acquired after birth due to environment or random events. Our goal is to use modern genomic technologies to identify such genetic variants in patients, including single-point mutations and large-scale structural variants, and study how they contribute to aberrant gene regulation and human diseases. Our state-of-the-art repertoire includes single-cell RNA-Seq, snATAC-Seq, and scHi-C, as well as different long-reads sequencing such as Nanopore-seq and optical mapping.
Our center will develop both novel technologies and computational tools to perform genome-wide analysis of patient genomes. We will generate and integrate personal multi-omics data (such as genomics, transcriptomics and epigenomics) to study human disease at single cell resolution. By combining modern genomic approach and advanced machine learning techniques, we aim to identify disease-specific biomarkers and use them to predict disease risk and clinical outcomes.

Northwestern Medicine Breakthroughs February 2021 - read more about Dr. Yue's work

Learn More About AMA
The AMA Team
Primary Members:
Benjamin Singer
Eric Neilson
Fei Li Kuang
Feng Yue
Huiping Liu
Mazhar Adli
Richard Green
Secondary Members:
Alexander Misharin
Barbara Stranger
Elizabeth McNally
Farzaneh Sorond
Fei Li Kuang
Kyubum Lee
Minoli Perera
Paul Reyfman
Raj Tuliani
Sabah Kadri
Sanjay Mehrotra
We welcome any collaboration with the goal of integrating machine learning with genomics and use the knowledge to better understand human disease. We are particularly interested in translational genomics, where the outcome of the research is directly related with patient diagnosis, stratification, and targeted treatment.
Apply for I.AIM Membership
Current Work Highlight

Using machine learning to study 3D genome organization: Recently, Dr. Yue’s group developed a supervised learning algorithm that can accurately detect chromatin interactions between essential genes and their distal regulatory elements, which frequently host disease-related genetic mutations. This random-forest based algorithm can lean unique patterns from multiple orthogonal platforms and can discover previously missed chromatin interactions. Further, this software can dramatically reduce sequencing depths by more than ten folds, and can still effectively capture the meaningful chromatin loops. This feature also means significantly reduced sequencing cost, which is one of the major bottlenecks for the study of 3D genome organization. As majority of the human diseases are related with genetic mutations in the non-protein-coding part of the genome, this work can potentially identify such elements and reveal target for personalized therapy. [Salameh et al. Nat. Comms. 2020]

Using long-reads sequencing and single techniques to study model animal system: The zebrafish has been widely used in the study of human disease, as ~70% of the protein-coding genes are conserved between the two species. However, the annotation of functional control elements in the zebrafish genome has been poor and there are still many errors in the current zebrafish genome assembly. In this work, we generated the most comprehensive map of transcriptomes, cis-regulatory elements, methylomes and 3D genome organization in zebrafish. We also performed single-cell ATAC-seq in zebrafish brain, which delineated 25 different clusters of cell types. By combining multipe long-read DNA sequencing techniques and Hi-C, we assembled the sex-determining chromosome 4 de novo. Overall, our work provides a great resource for the functional annotation of vertebrate genomes and the study of evolutionarily conserved elements. [Yang et al. Nature 2020]

Recent Publications

1. Hongbo Yang, Yu Luan, Tingting Liu, Hyung Joo Lee, Li Fang, Yanli Wang, Xiaotao Wang, Bo Zhang, Qiushi Jin, Khai Chung Ang, Xiaoyun Xing, Juan Wang, Jie Xu, Fan Song, Iyyanki Sriranga, Chachrit Khunsriraksakul, Tarik Salameh, Daofeng Li, Mayank N. K. Choudhary, Jacek Topczewski, Kai Wang, Glenn S. Gerhard, Ross C. Hardison, Ting Wang, Keith C. Cheng, Feng Yue. "A map of cis-regulatory elements and 3D genome structures in zebrafish." Nature 588, 337–343, 2020.

2. Tarik J Salameh, Xiaotao Wang, Fan Song, Bo Zhang, Sage M. Wright, Chachrit Khunsriraksakul, Feng Yue. "A supervised learning framework for chromatin loop detection in genome-wide contact maps." [Manuscript][Download predicted loops]Nature Communications 11:3428, 2020.

3. Jesse Dixon, Jie Xu, Vishnu Dileep, Ye Zhan, Fan Song, Victoria T. Le, Galip Gurkan Yardimci, Abhijit Chakraborty, Ferhat Ay, William Stafford Noble, Job Dekker, David M. Gilbert, Feng Yue. "Integrative Framework For Detecting Structural Variations In Cancer Genomes." Nature Genetics 50, 1388-1398, 2018.

4. Grant RA, Morales-Nebreda L, Markov NS, Swaminathan S, Querrey M, Guzman ER, Abbott DA, Donnelly HK, Donayre A, Goldberg IA, Klug ZM, Borkowski N, Lu Z, Kihshen H, Politanska Y, Sichizya L, Kang M, Shilatifard A, Qi C, Lomasney JW, Argento AC, Kruser JM, Malsin ES, Pickens CO, Smith SB, Walter JM, Pawlowski AE, Schneider D, Nannapaneni P, Abdala-Valencia H, Bharat A, Gottardi CJ, Budinger GRS, Misharin AV, Singer BD, Wunderink RG, NU SCRIPT Study Investigators. “Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia.” Nature. 2021 Feb; 590(7847): 635-641.

5. Wei X, Yang J, Adair SJ, Ozturk H, Kuscu C, Lee KY, Kane WJ, O'Hara PE, Liu D, Demirlenk YM, Habieb AH, Yilmaz E, Dutta A, Bauer TW, Adli M. “Targeted CRISPR screening identifies PRMT5 as synthetic lethality combinatorial target with gemcitabine in pancreatic cancer cells.” Proc. Natl. Acad. Sci. USA. 2020 Nov; 117(45): 28068-28079.

Look out for next month's spotlight Center: Bioethics and Medical Humanities with Dr. Michelson! 
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