CLONEID: A Framework for Monitoring and Steering Subclonal Dynamics
Veith, T.; Beck, R. J.; Tagal, V.; Li, T.; Alahmari, S.; Cole, J.; Hannaby, D.; Yu, X.; Maksin, K.; Schultz, A.; Lee, H.; El Naqa, I.; Eschrich, S. A.; Lupo, J.; Ji, H.; Diaz, A.; Andor, N.
Show abstract
Understanding how genetic and phenotypic diversity emerges and evolves within cancer cell populations is a fundamental challenge in cancer biology. CLONEID is a novel framework designed to organize and analyze clone-specific measures as structured time-series data. By integrating and monitoring genotypic and phenotypic experimental data over time, CLONEID facilitates hypothesis-driven and hypothesis-generating research in cancer biology. This article outlines the development, utility, and applications of CLONEID, emphasizing its role in overcoming challenges in data reproducibility, mathematical modeling, and multi-modal data integration. A webportal to the CLONEID database is available at dev.cloneid.org.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data 96%
- CELLector: Genomics Guided Selection of Cancer in vitro Models 94%
- Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states 94%
Similar papers in this journal
- Sfaira accelerates data and model reuse in single cell genomics 95%
- Cross-species imputation and comparison of single-cell transcriptomic profiles 95%
- scCross: A Deep Generative Model for Unifying Single-cell Multi-omics with Seamless Integration, Cross-modal Generation, and In-silico Exploration 95%
Similar papers in this journal
- CelLink: integrating single-cell multi-omics data with weak feature linkage and imbalanced cell populations 95%
- Single-Cell Trajectory Inference for Detecting Transient Events in Biological Processes 94%
- Single-Cell Signature Explorer for comprehensive visualization of single cell signatures across scRNA-seq data sets 94%
Similar papers in this journal
- Single-cell DNA replication dynamics in genomically unstable cancers 95%
- Characterizing cell-type spatial relationships across length scales in spatially resolved omics data 94%
- FastCCC: A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.