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Related Concept Videos

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...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Related Experiment Video

Updated: Jul 7, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Interactive data analysis and clustering of genomic data.

A Ciaramella1, S Cocozza, F Iorio

  • 1Department of Applied Sciences University of Naples Parthenope, I-80143, Centro Direzionale, Naples, Italy. angelo.ciaramella@uniparthenope.it

Neural Networks : the Official Journal of the International Neural Network Society
|February 8, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel clustering approach to analyze human cell cycle gene expression data. The method enhances cluster reliability and provides significant biological insights using Gene Ontology annotations.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Time-dependent gene expression profiles are crucial for understanding the human cell cycle.
  • Existing clustering methods may require refinement for complex biological datasets.

Purpose of the Study:

  • To develop and evaluate a new, robust clustering approach for time-dependent gene expression data.
  • To compare the performance of K-means, Self-organizing Maps, and Probabilistic Principal Surfaces using this new approach.

Main Methods:

  • A multi-step clustering procedure involving preprocessing, parameter selection via data subsampling and stability measures.
  • Utilizing random initialization, similarity measures, and reliability evaluation for model selection and tuning.
  • Comparative analysis using cluster similarity, distortion values, and Gene Ontology (GO) annotations for validation.

Main Results:

  • The new approach successfully identified clusters of periodically expressed genes in the human cell cycle.
  • Comparative analysis revealed differences in performance among K-means, SOMs, and PPS.
  • GO annotations validated the biological significance of the obtained gene clusters.

Conclusions:

  • The proposed clustering methodology offers a reliable way to analyze gene expression dynamics.
  • The study provides a validated set of gene clusters related to the human cell cycle.
  • This approach can be applied to other time-series biological datasets for enhanced discovery.