Related Experiment Videos
Evaluation of the vector space representation in text-based gene clustering
P Glenisson1, P Antal, J Mathys
1Department of Electrical Engineering, ESAT-SISTA, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2003
Summary
Electronic literature enhances statistical models with scarce data. This study shows bag-of-words text representation effectively integrates genetic and free-text data for functional clustering.
Area of Science:
- Bioinformatics
- Computational Biology
- Data Science
Background:
- Electronic literature is an increasingly valuable resource for statistical modeling, especially with limited or noisy experimental data.
- Integrating domain literature knowledge with experimental data for complex modeling, particularly in clustering, is an under-explored area.
- Effective statistical text representations are needed to leverage literature information in biological data analysis.
Purpose of the Study:
- To evaluate the effectiveness of the bag-of-words (BoW) text representation for integrating genetic annotation and free-text information from databases into clustering.
- To investigate the impact of different weighting schemes and information sources on functional clustering outcomes.
- To quantitatively assess the performance of text-derived functional groupings against expert assessments.
Main Methods:
- Utilized the bag-of-words model to represent genetic annotation and free-text data from various sources.
- Applied different weighting schemes and explored various information sources to optimize text representation.
- Performed functional clustering using the text representations and compared the results with expert-defined groupings.
- Conducted a quantitative evaluation across different parameter settings.
Main Results:
- Demonstrated the successful application of the bag-of-words representation for incorporating literature-derived information into clustering.
- Showcased the influence of various weighting schemes and data sources on the quality of functional clusters.
- Provided a quantitative comparison between text-based and expert-based functional groupings.
- Linked the clustering results to specific biological interpretations.
Conclusions:
- The bag-of-words representation is a viable method for integrating electronic literature into statistical models for biological data analysis.
- Text-based data integration offers a promising approach for functional clustering, complementing experimental data.
- Further research into statistical text representations can enhance the discovery of biological insights from diverse data sources.