title: A Study of Domain Adaptation Classifiers Derived From Logistic Regression for the Task of Splice Site Prediction.
abstract: Supervised classifiers are highly dependent on abundant labeled training data. Alternatives for addressing the lack of labeled data include: labeling data (but this is costly and time consuming); training classifiers with abundant data from another domain (however, the classification accuracy usually decreases as the distance between domains increases); or complementing the limited labeled data with abundant unlabeled data from the same domain and learning semi-supervised classifiers (but the unlabeled data can mislead the classifier). A better alternative is to use both the abundant labeled data from a source domain, the limited labeled data and optionally the unlabeled data from the target domain to train classifiers in a domain adaptation setting. We propose two such classifiers, based on logistic regression, and evaluate them for the task of splice site prediction-a difficult and essential step in gene prediction. Our classifiers achieved high accuracy, with highest areas under the precision-recall curve between 50.83% and 82.61%.
authors: Nic
authors: N
authors: Herndon
authors: Doina
authors: D
authors: Caragea
mesh_terms: D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D016015:Logistic Models; D015233:Models, Statistical; D012326:RNA Splicing / Q000235:genetics*; D012372:ROC Curve; D017422:Sequence Analysis, DNA / Q000379:methods*