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A Study of Domain Adaptation Classifiers Derived From Logistic Regression for the Task of Splice Site Prediction
| Content Provider | Semantic Scholar |
|---|---|
| Author | Herndon, Nic Caragea, Doina |
| Copyright Year | 2016 |
| 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%. |
| Starting Page | 75 |
| Ending Page | 83 |
| Page Count | 9 |
| File Format | PDF HTM / HTML |
| DOI | 10.1109/TNB.2016.2522400 |
| Alternate Webpage(s) | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4894847/pdf/nihms787617.pdf |
| PubMed reference number | 26849871 |
| Alternate Webpage(s) | https://doi.org/10.1109/TNB.2016.2522400 |
| Journal | Medline |
| Volume Number | 15 |
| Journal | IEEE Transactions on NanoBioscience |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
