蛋白質結構分類問題在資訊生物學上始終是個很熱門的話題。到目前為止，從蛋白質的一級結構來分類蛋白質已經有很高的準確率。然而，對於更進一步的蛋白質分類，卻由於資料量過於龐大而成為一個挑戰。近期的研究已能達到65 % 的正確率對於將蛋白質分成不同的27個folds 之中。在此篇研究中，我們結合了資料融合技術和階層式機器學習架構將之應用在Ding and Dubchak 所提出的蛋白質分類資料上。我們將準確率提昇至69.6%。我們驗證了資料融合技術是個簡單且相當有用的方法並且可以應用在多個不同領域。

The classification of protein structures is essential for their function determination in bioinformatics. At present time, one can achieve high prediction accuracy easily from primary amino acid sequences. However, for further classification into various folding categories, presents a challenge to large number of folds. Recently study yielded high prediction accuracy of 65% on an independent set of 27 most populated folds. In this work, we combine data fusion scheme and a hierarchical learning architecture (HLA) and apply it on the data set gathered by Ding and Dubchak[12]. We are able to achieve an overall accuracy of 69.6%. We demonstrate that data fusion is a simple and useful scheme and could be applied to various fields.

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