Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer.

We ran the computations on a Linux computer with 20 CPUs (3.1 GHz) and 252 GB of RAM. Up to 8 cores were used for parallelization. Maximum RAM usage was measured as resident set size (RSS). Estimated time in seconds per processed abstract (sec/abstract) also accounts for loading gene alias lookup tables and machine learning models.本研究的计算任务均运行于一台搭载20颗主频3.1 GHz中央处理器（Central Processing Unit, CPU）、252 GB随机存取存储器（Random Access Memory, RAM）的Linux计算机。并行计算过程中最多启用8个计算核心。内存最大使用量以常驻集大小（resident set size, RSS）作为衡量指标。每处理一篇摘要的预估耗时（单位：秒/篇摘要，sec/abstract）已包含基因别名查找表与机器学习模型的加载时长。