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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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Gene function prediction using labeled and unlabeled data.

Xing-Ming Zhao¹, Yong Wang, Luonan Chen

¹ERATO Aihara Complexity Modelling Project, JST, 4-6-1 Komaba, Meguro, Tokyo, Japan. xmzhao@aihara.jst.go.jp

BMC Bioinformatics

|January 29, 2008

Summary

This summary is machine-generated.

This study introduces Annotating Genes with Positive Samples (AGPS), a novel method for defining negative samples in gene function prediction. AGPS accurately predicts gene functions using only positive samples, overcoming data imbalance issues.

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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Area of Science:

Bioinformatics
Computational Biology
Machine Learning in Genomics

Background:

Gene function prediction is typically a classification task requiring both positive and negative samples.
A significant challenge in gene function prediction is the scarcity of negative samples, as only positive gene annotations are readily available.
Treating all unannotated genes as negative samples leads to imbalanced datasets and potential classifier degradation due to false negatives.

Purpose of the Study:

To present a novel technique for defining negative samples in gene function prediction.
To enable reliable and accurate gene function prediction using only positive sample information.
To address the issue of imbalanced datasets in gene function prediction.

Main Methods:

Development of the Annotating Genes with Positive Samples (AGPS) technique for negative sample definition.
Integration of diverse data sources for enhanced prediction accuracy.
Utilizing one-class and two-class Support Vector Machines as the core machine learning algorithms.

Main Results:

The AGPS algorithm effectively defines negative samples, facilitating straightforward gene function prediction.
AGPS demonstrates good performance in predicting functions for yeast genes.
The method integrates multiple data sources for reliable and accurate gene function predictions.

Conclusions:

A new method for defining negative samples in gene function prediction has been proposed.
Experimental results on yeast genes confirm the effectiveness and good performance of AGPS on training and test sets.
The overlap between AGPS predictions and Gene Ontology (GO) annotations for unknown genes validates the proposed method's utility.