Cellarity Releases Novel, Open-Source, Single-Cell Dataset and Invites the Machine Learning and Computational Biology Communities to Develop New Algorithms Capable of Learning Fundamental Rules of Cell Behavior
Cellarity has announced the release of a groundbreaking single-cell dataset aimed at advancing genetic information mapping, available for a Kaggle competition at NeurIPS 2022. This dataset, generated through collaboration with Yale University and others, includes a time course of 300,000 CD34+ stem cells from human donors, evaluated across five time points. The initiative seeks to develop machine learning algorithms to understand cell differentiation and gene regulation implications for medicine. Entries for the competition close on November 15, 2022.
- Release of a unique 300,000-cell dataset to advance genetic research.
- Collaboration with respected institutions like Yale University and Chan Zuckerberg Initiative enhances credibility.
- Competition encourages innovation in machine learning applications for single-cell analysis.
- None.
The dataset will be publicly available for a Kaggle competition presented at NeurIPS 2022 hosted by Open Problems in Single-Cell Analysis in collaboration with Chan Zuckerberg Initiative, Chan Zuckerberg Biohub,
Cells are among the most complex and dynamic systems and are regulated by the interplay of DNA, RNA, and proteins. Recent technological advances have made it possible to measure these cellular features and such data provide, for the first time, a direct and comprehensive view spanning the layers of gene regulation that drive biological systems and give rise to disease.
“Advancements in single-cell technologies now make it possible to decode genetic regulation, and we are excited to generate another first-of-its-kind dataset to support Open Problems in Single Cell Analysis,” said
To drive innovation for such data, Cellarity generated a time course profiling in vitro differentiation of blood progenitors, a dataset designed in collaboration with scientists at
“While multimodal single-cell data is increasingly available, methods to analyze these data are still scarce and often treat cells as static snapshots without modeling the underlying dynamics of cell state,” said
In 2021, Cellarity partnered with Open Problems collaborators to develop the first benchmark competition for multimodal single-cell data integration using a first-of-its-kind multi-omics benchmarking dataset (NeurIPS 2021). This dataset was the largest atlas of the human bone marrow measured across DNA, RNA, and proteins and was used to predict one modality from another and learn representations of multiple modalities measured in the same cells. The 2021 competition saw winning submissions from both computational biologists with deep single-cell expertise and machine learning practitioners for whom this competition marked their first foray into biology. This translation of knowledge across disciplines is expected to drive more powerful algorithms to learn fundamental rules of biology.
For 2022, Cellarity and Open Problems are extending the challenge to drive innovation in modeling temporal single-cell data measured in multiple modalities at multiple time points. For this year’s competition, Cellarity generated a 300,000-cell time course dataset of CD34+ hematopoietic stem and progenitor cells (HSPC) from four human donors at five time points. HSPCs are stem cells that give rise to all other cells in the blood throughout adult life, and a 10-day time course captures important biology in CD34+ HSPCs. Being able to solve the prediction problems over time is expected to yield new insights into how gene regulation influences differentiation.
Entries to the competition will be accepted until
About Open Problems in Single Cell Analysis
Open Problems in Single-Cell Analysis was founded in 2020 bringing together academic, non-profit, and for-profit institutions to accelerate innovation in single-cell algorithm development. An explosion in single-cell analysis algorithms has resulted in more than 1,200 methods published in the last five years. However, few standard benchmarks exist for single-cell biology, both making it difficult to identify top performing algorithms and hindering collaboration with the machine learning community to accelerate single-cell science. Open Problems is a first-of-its-kind international consortium developing a centralized, open-source, and continuously updated framework for benchmarking single-cell algorithms to drive innovation and alignment in the field. For more information, visit https://openproblems.bio/.
About Cellarity
Cellarity’s mission is to fundamentally transform the way medicines are created. Founded by Flagship Pioneering in 2017, Cellarity has developed unique capabilities combining high-resolution data, single cell technologies, and machine learning to encode biology, predict interventions, and purposefully design breakthrough medicines. By focusing on the cellular changes that underlie disease instead of a single target, Cellarity’s approach uncovers new biology and treatments and is applicable to a vast array of disease areas. The company currently has programs underway in metabolic disease, hematology, immuno-oncology, and respiratory disease. For more info, visit www.cellarity.com.
About Flagship Pioneering
Flagship Pioneering conceives, creates, resources, and develops first-in-category bioplatform companies to transform human health and sustainability. Since its launch in 2000, the firm has, through its
View source version on businesswire.com: https://www.businesswire.com/news/home/20220815005722/en/
Media Contact:
Source: Cellarity
FAQ
What is the new dataset announced by Cellarity?
How does the new dataset impact genetic research?
When is the competition deadline for the dataset?
What institutions collaborated on the dataset?
What is the goal of the Kaggle competition?
