Bioinformatics Program, Boston University, Boston, Massachusetts, United States of America.
Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America.
PLoS Biol. 2020 Nov 30;18(11):e3000999. doi: 10.1371/journal.pbio.3000999. eCollection 2020 Nov.
How do we scale biological science to the demand of next generation biology and medicine to keep track of the facts, predictions, and hypotheses? These days, enormous amounts of DNA sequence and other omics data are generated. Since these data contain the blueprint for life, it is imperative that we interpret it accurately. The abundance of DNA is only one part of the challenge. Artificial Intelligence (AI) and network methods routinely build on large screens, single cell technologies, proteomics, and other modalities to infer or predict biological functions and phenotypes associated with proteins, pathways, and organisms. As a first step, how do we systematically trace the provenance of knowledge from experimental ground truth to gene function predictions and annotations? Here, we review the main challenges in tracking the evolution of biological knowledge and propose several specific solutions to provenance and computational tracing of evidence in functional linkage networks.
我们如何将生物学科学扩展到下一代生物学和医学的需求，以跟踪事实、预测和假设？如今，大量的 DNA 序列和其他组学数据正在生成。由于这些数据包含生命的蓝图，因此我们必须准确地解释它。DNA 的丰富度只是挑战的一部分。人工智能 (AI) 和网络方法通常基于大屏幕、单细胞技术、蛋白质组学和其他模式来推断或预测与蛋白质、途径和生物体相关的生物功能和表型。作为第一步，我们如何系统地跟踪从实验事实到基因功能预测和注释的知识来源？在这里，我们回顾了跟踪生物知识演变的主要挑战，并提出了几种用于功能链接网络中证据的来源和计算跟踪的具体解决方案。
