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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Phylogenetic Trees03:21

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
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Related Experiment Video

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Published on: July 29, 2022

Finding explained groups of time-course gene expression profiles with predictive clustering trees.

Ivica Slavkov1, Valentin Gjorgjioski, Jan Struyf

  • 1Dept. of Knowledge Technologies, Jozef Stefan Institute, Slovenia. Ivica.Slavkov@ijs.si

Molecular Biosystems
|March 19, 2010
PubMed
Summary

This study introduces predictive clustering trees (PCTs) to analyze biological time series data. PCTs efficiently cluster gene expression profiles and provide descriptions in one step, improving upon traditional two-step methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Analyzing biological time series data typically involves a two-step process: clustering temporal profiles and then annotating these clusters.
  • Existing methods require expert knowledge for post-clustering annotation, such as Gene Ontology enrichment.

Purpose of the Study:

  • To investigate the application of predictive clustering trees (PCTs) for analyzing biological time series data.
  • To demonstrate PCTs' ability to unify clustering and prediction for temporal profile analysis.

Main Methods:

  • Utilized predictive clustering trees (PCTs), a method that integrates clustering and prediction.
  • Applied PCTs to multiple yeast microarray time series datasets measuring gene expression changes under varying environmental conditions.

Main Results:

  • PCTs successfully clustered genes with similar temporal expression profiles.
  • The approach yielded a predictive model for gene temporal profiles using cluster prototypes.
  • PCTs provided symbolic descriptions for the identified clusters in a single, integrated step.

Conclusions:

  • Predictive clustering trees offer a unified, single-step approach for analyzing biological time series data.
  • PCTs effectively cluster temporal profiles, generate predictive models, and provide cluster descriptions, enhancing traditional methods.