Computing Laboratory, University of Kent, Canterbury, UK.
IEEE/ACM Trans Comput Biol Bioinform. 2010 Jan-Mar;7(1):172-82. doi: 10.1109/TCBB.2008.47.
The literature on protein function prediction is currently dominated by works aimed at maximizing predictive accuracy, ignoring the important issues of validation and interpretation of discovered knowledge, which can lead to new insights and hypotheses that are biologically meaningful and advance the understanding of protein functions by biologists. The overall goal of this paper is to critically evaluate this approach, offering a refreshing new perspective on this issue, focusing not only on predictive accuracy but also on the comprehensibility of the induced protein function prediction models. More specifically, this paper aims to offer two main contributions to the area of protein function prediction. First, it presents the case for discovering comprehensible protein function prediction models from data, discussing in detail the advantages of such models, namely, increasing the confidence of the biologist in the system's predictions, leading to new insights about the data and the formulation of new biological hypotheses, and detecting errors in the data. Second, it presents a critical review of the pros and cons of several different knowledge representations that can be used in order to support the discovery of comprehensible protein function prediction models.
目前，蛋白质功能预测的文献主要集中在最大限度地提高预测准确性的工作上，而忽略了验证和解释发现的知识的重要问题，这可能会导致新的有生物学意义的见解和假设，并促进生物学家对蛋白质功能的理解。本文的总体目标是批判性地评估这种方法，为这个问题提供一个新的视角，不仅关注预测准确性，还关注所诱导的蛋白质功能预测模型的可理解性。更具体地说，本文旨在为蛋白质功能预测领域做出两个主要贡献。首先，它从数据中提出了发现可理解的蛋白质功能预测模型的案例，详细讨论了此类模型的优势，即提高生物学家对系统预测的信心，导致对数据的新见解和提出新的生物学假设，并检测数据中的错误。其次，它对可用于支持发现可理解的蛋白质功能预测模型的几种不同知识表示形式的优缺点进行了批判性回顾。
