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I2K Conference Details and Sign-Ups
May 6th, 9th and 10th - Virtual Conference Check the I2K 2022 conference page for further details. Presenters, Workshops and Demos! If you would like to be a presenter or hold a workshop/demo at I2K 2022 please submit a Request to Present form.
Customizing a Model for Fiber Segmentation, Part I: Investigating Possible Methods
Melissa Gillis Segmenting fibers using traditional cell segmentation software can be challenging. Because most biological cell segmentation tools were designed for cells, most programs have difficulty identifying and distinguishing longer and thinner objects such as fibers. Also, unlike cells...
How do I nominate someone for the prize?
Nominations may be submitted online. To begin a nomination, click here. LINK THROUGH TO APPLICATION SITE
JUMP-Cell Painting Consortium begins data release! Ardigen Supporting Partner added
Thank you to all involved! As this three year project comes to a close, the whole project team wants to extend a warm thank you to all involved: more than a hundred scientists worked in nine workstreams to design, execute, and share this dataset. It took expertise across cell biology, chemistry...
Research
We seek to understand the genetic basis of common diseases and to translate genetic association data into biological insights. Most of our research involves the development of new statistical methods and the application of these methods to large-scale genetic datasets. Genetic architecture of common...
Cohort Design and Natural Language Processing to Reduce Bias in Electronic Health Records Research: The Community Care Cohort Project
Khurshid, Shaan, Christopher Reeder, Lia X. Harrington, Pulkit Singh, Gopal Sarma, Samuel F. Friedman, Paolo Di Achille, et al. 2021. “Cohort Design and Natural Language Processing to Reduce Bias in Electronic Health Records Research: The Community Care Cohort Project”. MedRxiv, 2021.05.26.21257872.
Using Machine Learning to Elucidate the Spatial and Genetic Complexity of the Ascending Aorta
Nekoui, Mahan, James P. Pirruccello, Paolo Di Achille, Seung Hoan Choi, Samuel N. Friedman, Victor Nauffal, Kenney Ng, et al. 2021. “Using Machine Learning to Elucidate the Spatial and Genetic Complexity of the Ascending Aorta.”
Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction
Agrawal, Saaket, Marcus D.R. Klarqvist, Connor Emdin, Aniruddh P. Patel, Manish D. Paranjpe, Patrick T. Ellinor, Anthony Philippakis, Kenney Ng, Puneet Batra, and Amit V. Khera. 2021. “Selection of 51 Predictors from 13,782 Candidate Multimodal Features Using Machine Learning Improves Coronary Artery Disease Prediction”. Patterns, 100364.