Precision medicine means giving patients the right treatment at the right dose at the right time with minimum ill consequences and maximum efficacy. It is medicine personalized to the individual’s genes, environment, and lifestyle and, ultimately, its widespread use will require a deep understanding of the genomic variations that create predispositions or resistances to various diseases. Some of the links between genes and diseases are already known, and more are being discovered every day. Similarly, much is known about which drugs are efficacious for treating which diseases, but there is still more to learn. The issue now is how to extract this information from the biomedical literature in way that can keep pace with today’s rapid discoveries in medical research. Efforts to assemble an organized database of such knowledge to data have focused on mathematical statistic methods, computer-aided methods, etc. Success has been mixed as previous methods usually result in false positive or depend on training sample sets, lacking of generality in different research fields, which have choked advancements in precision medicine. To break through this bottleneck, we need novel methods that can extract and leverage the valuable information locked within the constraints of the data we have. Hence, in this paper, we present a new text-based computational framework for extracting full three-way drug-disease-gene triplet information related to colorectal cancer from biomedical texts. The framework consists of two main steps. The first is to construct an integrated drug-disease-gene network by extracting pair-wise associations between diseases, drugs, and genes, and then store unique drug-disease-gene triplets for further analysis. Since the constructed network is highly likely to be too sparse, the next step is to complete the incomplete links in the network, i.e., to predict novel links from genes to diseases to drugs. To validate our framework, we conducted a case study on colorectal cancer, mining the literature for drug-disease and disease-gene associations. An analysis of the subsequent inferences drawn between the two shows that this approach can help to inform novel research hypotheses and identify new knowledge triplets about various diseases, both of which are significant for the advancement and implementation of precision medicine.

精准医学意味着在正确的时间以正确的剂量为患者提供正确的治疗，以最大程度地减少疾病的后果和最大的疗效。它是针对个体的基因，环境和生活方式而个性化的药物，最终，其广泛使用将需要对造成易患病或对各种疾病产生抗性的基因组变异有深刻的了解。基因与疾病之间的某些联系已经为人所知，并且每天都在被发现。类似地，对于哪种药物可有效治疗哪些疾病的了解很多，但是还有很多东西要学习。现在的问题是如何从生物医学文献中提取信息，以与当今医学研究的迅速发现保持同步。将此类知识的有组织的数据库组装成数据的工作集中在数学统计方法，计算机辅助方法等上。成功与否混合在一起，因为先前的方法通常会导致误报或依赖于训练样本集，而在不同的研究中缺乏通用性领域，在精密医学领域取得了进步。为了克服这一瓶颈，我们需要新颖的方法来提取和利用锁定在我们数据约束内的有价值的信息。因此，在本文中，我们提出了一个新的基于文本的计算框架，用于从生物医学文本中提取与结直肠癌有关的完整的三向药物-疾病-基因三联体信息。该框架包括两个主要步骤。首先是通过提取疾病，药物和基因之间的成对关联来构建一个整合的药物-疾病-基因网络，然后存储独特的药物-疾病-基因三联体用于进一步分析。由于构建的网络极有可能过于稀疏，因此下一步是完成网络中的不完整链接，即预测从基因到疾病再到药物的新颖链接。为了验证我们的框架，我们对大肠癌进行了案例研究，挖掘了有关药物疾病和疾病基因关联的文献。对两者之间随后得出的推论进行的分析表明，这种方法可以帮助为新颖的研究假设提供信息，并确定有关各种疾病的新知识三胞胎，这两者对于精确医学的发展和实施都具有重要意义。药物和基因，然后存储独特的药物疾病基因三联体以进行进一步分析。由于构建的网络极有可能过于稀疏，因此下一步是完成网络中的不完整链接，即预测从基因到疾病再到药物的新颖链接。为了验证我们的框架，我们对大肠癌进行了案例研究，挖掘了有关药物疾病和疾病基因关联的文献。对两者之间随后得出的推论进行的分析表明，这种方法可以帮助为新颖的研究假设提供信息，并确定有关各种疾病的新知识三胞胎，这两者对于精确医学的发展和实施都具有重要意义。药物和基因，然后存储独特的药物疾病基因三联体以进行进一步分析。由于构建的网络极有可能过于稀疏，因此下一步是完成网络中的不完整链接，即预测从基因到疾病再到药物的新颖链接。为了验证我们的框架，我们对大肠癌进行了案例研究，挖掘了有关药物疾病和疾病基因关联的文献。对两者之间随后得出的推论进行的分析表明，这种方法可以帮助为新颖的研究假设提供信息，并确定有关各种疾病的新知识三胞胎，这两者对于精确医学的发展和实施都具有重要意义。下一步是完成网络中的不完整链接，即预测从基因到疾病再到药物的新颖链接。为了验证我们的框架，我们对大肠癌进行了案例研究，挖掘了有关药物疾病和疾病基因关联的文献。对两者之间随后得出的推论进行的分析表明，这种方法可以帮助为新颖的研究假设提供信息，并确定有关各种疾病的新知识三胞胎，这两者对于精确医学的发展和实施都具有重要意义。下一步是完成网络中的不完整链接，即预测从基因到疾病再到药物的新颖链接。为了验证我们的框架，我们对大肠癌进行了案例研究，挖掘了有关药物疾病和疾病基因关联的文献。对两者之间随后得出的推论进行的分析表明，这种方法可以帮助为新颖的研究假设提供信息，并确定有关各种疾病的新知识三胞胎，这两者对于精确医学的发展和实施都具有重要意义。