蛋白質結構分類在生物資訊學中是一個相當重要的議題。在本篇論文裡，我們使用簡單的連通圖來表示蛋白質的三維結構，其中，每個節點用來表示一個氨基酸，每個邊表示兩個氨基酸之間的接觸距離。在本篇論文中，我們使用B-factor（原子位移參數）減少每個圖中的節點和邊的數量。我們使用圖形探勘演算法找出家族中的關鍵子圖來對蛋白質結構進行家族分類。
實驗的部分，我們利用序列比對結果所生成的e -value (e-value = 1-e^-10，e=2.718)。將所提出來的演算法與BLAST，BLAT 和DALI 進行比較，並利用SCOP資料庫來驗證分類的結果。實驗結果也顯示該方法可以準確且有效地分類蛋白質結構。

Protein structural classification is vital in bioinformatics. In this study, a simple
and connected graph was used to represent a three-dimensional protein structure in
which each node represented an amino acid and each edge represented a contact distance
between two amino acids. A B-factor (an atomic displacement parameters) was
then used to substantially reduce the number of nodes and edges in each graph representation.
A graph mining approach was applied to determine the critical subgraphs
among these graphs, which can be used to classify protein structural families. The
proposed algorithm was compared with BLAST, BLAT and DALI. The alignment results
generated e-value, based on e-value = 1 − e^−10 (e=2.718), and the classification
result were examined accordingly. Moreover, an experiment was conducted, and in
this experiment, characteristic substructural patterns were identified in several protein
families in the SCOP database. The experimental results demonstrated that the proposed
method can classify the protein structures accurately and effciently.

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