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Evolutionary Relationships through Genome Comparisons

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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Related Experiment Video

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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

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Published on: May 27, 2021

Finding complex biological relationships in recent PubMed articles using Bio-LDA.

Huijun Wang¹, Ying Ding, Jie Tang

¹School of Informatics and Computing, Indiana University, Bloomington, Indiana, United States of America.

|March 31, 2011

Summary

Bio-LDA is a novel algorithm that identifies biological topics and relationships from scientific literature. It aids in knowledge discovery for drug repurposing and target identification.

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Area of Science:

Life Sciences
Bioinformatics
Computational Biology

Background:

The vast volume of life science literature presents challenges for researchers staying current.
Scholarly articles are a rich source for discovering information on genes, diseases, compounds, and their interactions.

Purpose of the Study:

To introduce Bio-LDA, an algorithm for automated topic identification and relation discovery from biological texts.
To demonstrate the utility of Bio-LDA in enhancing knowledge discovery within the life sciences.

Main Methods:

Bio-LDA extracts biological terminology to identify latent topics.
It employs measures to uncover relationships between topics and bio-terms.
Identified relationships are integrated with existing life science datasets.

Main Results:

The Bio-LDA model successfully identified latent topics and putative relationships.
Case studies showcased its effectiveness in association prediction, search, and connectivity map generation.
Integration with existing datasets provided additional biological insights.

Conclusions:

Bio-LDA offers a powerful approach for knowledge discovery in life sciences.
The model supports applications such as target identification, lead hopping, and drug repurposing.
This method facilitates navigating and extracting value from extensive biological literature.