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POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors

机译:POIM:位置低聚物重要性矩阵-了解基于支持向量机的信号检测器

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摘要

Motivation: At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences. Frequently the most accurate classifiers are obtained by training support vector machines (SVMs) with complex sequence kernels. However, a cumbersome shortcoming of SVMs is that their learned decision rules are very hard to understand for humans and cannot easily be related to biological facts.
机译:动机:生物序列的分类是许多重要的生物信息学问题的核心,例如基因发现和功能预测。通常,最复杂的分类器是通过训练具有复杂序列核的支持向量机(SVM)获得的。但是,SVM的一个麻烦缺点是其学习的决策规则很难为人类所理解,并且不易与生物学事实相关联。

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