基因相似度對於預估各種生物機制上有所幫助。隨著生物技術的進步，各種生物實驗資料已經可以取得來進行基因間關係的研究。如序列相似度、功能相似度、共同表現、相似蛋白表現與共同調控。這些多樣的生物指標代表不同的類型的資料，而這些指標彼此間互相影響。
而統計上的量化有助於鑑別這些指標之間的關聯，並也助於以適當的若干指標來取代所關注卻資料不全的指標。在先前的研究中，部分指標間的關聯已經運用於使用其他的指標來驗證或取代生物資料不足的舊指標。然而，這些不同指標的關聯非常零落地被提及，並且無系統性地分析。這篇研究提供了完整基因共同表現、共同調控、功能相似、蛋白質作用與序列相似度，任兩指標間關聯的分析。這五種生物上的資料選擇是因為這些資料能非常廣泛的在各種物種都能取得，並且也非常廣泛地被使用。這些指標關聯的關聯經由均值曲線來分析，並更進而將其關聯分類為：可對應，可鑑別，弱關聯三種關係。基於這些鑑別出的關聯，合適於任意指標的預估子都將在本文呈現出來。最後，這些不同資料間的關聯再更全面性地分析，並從中發現一些生物上的機制。

Gene similarity is helpful for predicting various biological mechanisms. With the advance of biotechnologies, various types of biological data, such as sequence similarity, functional similarity, co-expression, co-protein interaction, and co-regulation are available for studying gene relations. These different types of biological data are figured by types of features and mutually affected by each other.
Statistically quantifying the linkage among different types of biological data is helpful to discover the linkages between features and select proper feature to alternate desired biological feature by inadequate data.
In the previous works, the linkage between different types of biological data is implicitly applied to validate or quantify an unknown biological data by other available data. However the linkages between different types of biological data are simply mentioned in these works without a systematic analysis.
The present work gives a comprehensive study of linkage among co-expression, co-regulation, co-function, protein-protein interaction similarity and sequence similarity. The five types of biological data were selected because (i) they are widely available in many species (availability) and (ii) there are existing gene/protein relation measures based on them (popularity).
The linkages between features are analyzed by mean-value curve, and further identified into entirely implicated, partially implicated, and obscure. Base on the identified linkages, proper predictors of each type of data are presented. Finally, these linkages among features are globally analyzed and some biological mechanisms are revealed from these globally-analyzed relations between types of data.

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