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Related Concept Videos

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Evolutionary Relationships through Genome Comparisons

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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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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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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. 
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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Related Experiment Video

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Network-based visualisation of frequent sequences.

László Bántay1, János Abonyi1

  • 1HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, Hungary.

Plos One
|May 9, 2024
PubMed
Summary

This study introduces novel network-based visualizations to improve frequent sequential pattern mining. These methods offer intuitive interpretations of complex event data, aiding system analysis.

Area of Science:

  • Data Mining and Pattern Recognition
  • Information Visualization
  • Complex Systems Analysis

Background:

  • Frequent sequential pattern mining is crucial for discovering patterns in event chains within complex systems.
  • Unlabeled events from parallel processes complicate pattern identification.
  • Existing visualization techniques often yield too many sequences, hindering interpretation.

Purpose of the Study:

  • To propose intuitive and interactive network-based visualization methods for sequential pattern mining results.
  • To reduce cognitive load and improve the understanding of event scenarios.
  • To enhance the interpretation of complex system mechanisms.

Main Methods:

  • Developed three novel network-based sequence visualization techniques.

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  • Method 1: Weighted network using transition confidence values.
  • Method 2: Enriched adjacency matrix with node similarities for Multidimensional Scaling (MDS) projection.
  • Method 3: Similarity measurement based on event occurrence overlap.

    • Main Results:

    • Proposed visualizations offer richer, more understandable interpretations than traditional text-based outputs.
    • Demonstrated applicability in industrial alarm management and website clickstream analysis.
    • Implementation in Python environment confirmed high applicability for interactive processing.

    Conclusions:

    • The novel visualization methods significantly enhance the interactive processing of frequent sequences.
    • These techniques facilitate faster and better understanding of complex system event scenarios.
    • The approach supports deeper exploration of the inner mechanisms of complex systems.