近年來，關於藥物、疾病表現型及蛋白質的資訊快速累積，愈來愈多科學家投入於利用計算方法來推論出藥物與疾病之關聯。為了有效的整合利用目前擁有的資訊與知識，並且系統化且快速的了解藥物與疾病的關聯性，因此利用資訊的方式做全面性的整合是很重要的。我們提出一種異質性網路傳遞的方法實行在藥物、基因、疾病表現型的三個互連網路，並利用可取得之實驗資料及知識來加強網路上連結的權重，進而推論藥物與所詢問疾病之兩者關聯性。我們使用攝護腺癌及結腸直腸癌做為實驗測試之疾病，我們利用文獻擷取為主的Comparative Toxicogenomics Database作為我們的測試標準。依據實驗結果，我們提出的方法有著高的特異度及敏感度，並且明顯地勝過之前的研究方法。我們成功的證明結合藥物、基因、疾病表現型異質性資訊，也證明了使用網路為基礎的方析方式其可行性及優勢性。使用我們的方法所推論出潛在的藥物與疾病之關聯已吸引到生物學家之注意並能提供毒性基因體研究及藥物重新定位研究新的觀點。

During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been draw attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue. We combine three weighted networks of drug, genomic and disease phenotype data from available experimental data and knowledge then infer drug-disease associations by a hetero-network propagation approach. In the experiments, we adopt prostate cancer and colorectal cancer as our test data. We select the manually curated associations from comparative toxicogenomics database as our benchmark. The ranked results show that our proposed method obtains high specificity and sensitivity and clearly outperforms previous methods. We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations which were inferred by our method drew the biologists’ attention and provide new perspectives for toxicogenomics and drug reposition evaluation.

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