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Customizing a Model for Fiber Segmentation, Part I: Investigating Possible Methods

Blog: August 05, 2022

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...

Zhenglin Guo

Person: Project Manager II

How do I nominate someone for the prize?

FAQ

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

News: November 01, 2022

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

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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

Publication: Journal Article

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

Publication: Miscellaneous

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

Publication: Journal Article

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.

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation

Publication: Journal Article

Khurshid, Shaan, Samuel Friedman, Christopher Reeder, Paolo Di Achille, Nathaniel Diamant, Pulkit Singh, Lia X. Harrington, et al. 2022. “ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation”. Circulation 145 (2): 122-33.

Abstract 15044: Risk of Myocardial Infarction in Carriers of Familial Hypercholesterolemia Mutations is Modified by Common Variant Genetic Background or Adherence to a Healthy Lifestyle

Publication: Journal Article

Fahed, Akl C, Minxian Wang, Mark Chaffin, Alexander G Bick, Candace Patterson, Pradeep Natarajan, Matthew Lebo, et al. 2019. “Abstract 15044: Risk of Myocardial Infarction in Carriers of Familial Hypercholesterolemia Mutations Is Modified by Common Variant Genetic Background or Adherence to a Healthy Lifestyle”. Circulation 140 (Suppl\_1): A15044—A15044.