Target/Biomarker selection using systems networks and decision theory

用于自动靶标和生物标志物鉴定的方法通常涉及根据适应症相关性标准对基因和蛋白质进行排序。一个典型的过程，然后涉及遍历科学文献的支持证据。然而，许多与年龄相关的西方世界疾病具有复杂异质性条件的特征，这些条件沿着疾病严重程度的谱沿着变化。它们通常涉及多个系统和途径。因此，选择池包含数百到数千个基因。从一个大的候选库中选择最佳靶点和分层生物标志物是药物发现的一个重大挑战。目前的方法充其量是半自动化的或依赖于专家意见。这意味着在这样的决策任务中很难避免偏见。决策理论是人工智能的一个分支，可以用来帮助解决这些复杂性。自动推理和论证理论是自然语言处理的两个适当分支，非常适合这项任务。该项目的目的是使用基于NLP的方法和推荐方法的组合来完全自动化目标和生物标志物选择。最终目标是从复杂的信息关系网络中生成无偏见的决策。

Methods for automated target and biomarker identification typically involve ranking genes and proteins according to indication relevance criteria. A typical process then involves traversing scientific literature for supporting evidence. However, many age-related western world diseases bear the hallmarks of complex heterogeneous conditions that vary along a spectrum of disease severity. They frequently involve multiple systems and pathways. Hence selection pools comprise many hundreds to thousands of genes. Selecting the best targets and stratification biomarkers from a large candidate pool presents a significant challenge in drug discovery. Current approaches are at best semi-automated or rely on expert opinion. This means it is difficult to avoid bias in such decision-making tasks. Decision theory is one branch of Artificial Intelligence that can be applied to help resolve these complexities. Automated reasoning and argumentation theory are two appropriate branches of natural language processing well suited to the task. The aim of this project is to fully automate target and biomarker selections using a combination of NLP-based and recommendation methods. The end goal is to generate unbiased decisions from a complex network of information relationships.