连接器，将纵向数据进行拟合和聚类，以揭示一个新的风险分层系统。

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

机构信息

Department of Computer Science, University of Torino, Torino 10149, Italy.

Department of Mathematics G. Peano, University of Torino, Torino 10123, Italy.

出版信息

Bioinformatics. 2023 May 4;39(5). doi: 10.1093/bioinformatics/btad201.

DOI:10.1093/bioinformatics/btad201

PMID:37079732

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10159654/

Abstract

MOTIVATION

The transition from evaluating a single time point to examining the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and clustering challenging.

RESULTS

We developed CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. We give a new perspective of mechanism interpretation, specifically, we define novel model aggregations and we identify unanticipated molecular associations with response to clinically approved therapies.

AVAILABILITY AND IMPLEMENTATION

CONNECTOR is freely available under GNU GPL license at https://qbioturin.github.io/connector and https://doi.org/10.17504/protocols.io.8epv56e74g1b/v1.

摘要

动机

只有在适当的框架下，才有可能从评估单一时间点转变为考察系统的整个动态演变。动态演变的强可变性使得为数据拟合和聚类定义解释过程变得具有挑战性。

结果

我们开发了 CONNECTOR，这是一个数据驱动的框架，能够以简单直接的方式分析和检查纵向数据。当用于分析来自卵巢癌和结直肠癌的 1599 个患者来源的异种移植物生长曲线随时间的肿瘤生长动力学时，CONNECTOR 通过无监督方法将时间序列数据聚合到有信息的聚类中。我们对机制解释有了新的视角，具体来说，我们定义了新的模型聚合，并确定了与临床批准疗法反应的意外分子关联。