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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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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Identifying relevant data for a biological database: handcrafted rules versus machine learning.

Aditya Kumar Sehgal1, Sanmay Das, Keith Noto

  • 1Core Technologies Group, Parity Computing, San Diego, CA 92121, USA. a.sehgal@paritycomputing.com

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|March 12, 2011
PubMed
Summary
This summary is machine-generated.

Machine learning methods automatically identify relevant biological data for specialized databases like TCDB, outperforming human-created rules. This research aids curators in updating biological databases efficiently.

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Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Machine Learning
  • Database Curation

Background:

  • Over 1,000 specialized biological databases exist, necessitating efficient data identification.
  • Automatic data curation is crucial for maintaining and expanding these vital resources.

Purpose of the Study:

  • To develop and evaluate machine learning approaches for identifying relevant biological data.
  • To incorporate identified data into the Transport Protein Integrated Database (TCDB).

Main Methods:

  • Applied machine learning algorithms to MEDLINE documents and Swiss-Prot/TrEMBL protein records.
  • Compared machine learning performance against expert-defined rules for data selection.

Main Results:

  • Machine learning approaches demonstrated superior performance compared to manually created rules.
  • Successfully identified and incorporated novel records into the TCDB database.

Conclusions:

  • Machine learning offers a practical and effective solution for updating specialized biological databases.
  • The findings provide valuable insights for database curators managing large-scale biological data.