Ladunga I
Bioinformatics Department, SmithKline Beecham Pharmaceuticals, King of Prussia, PA 19406-0939, USA.
Curr Opin Biotechnol. 2000 Feb;11(1):13-8. doi: 10.1016/s0958-1669(99)00048-8.
Machine learning techniques have improved predictions of secretory proteins from protein, genomic and expressed sequence tag (EST) sequences. Artificial neural networks, physical sequence analysis using high-performance optimization, and hidden Markov models identify extremely variable signal peptides (the vehicles of protein transport across the endoplasmic reticulum membrane), transmembrane segments, and specific extracellular and intracellular domains as indicators of possible roles in the intercellular and intracellular chemical signaling pathways. The major role of peptide hormones, blood coagulation factors, carcinogenesis agents, and other secretory proteins in orchestrating multicellular life indicates pharmacological potential in the cure of major diseases and numerous biotechnological applications.
机器学习技术已改进了从蛋白质、基因组和表达序列标签（EST）序列预测分泌蛋白的能力。人工神经网络、使用高性能优化的物理序列分析以及隐马尔可夫模型可识别出极具变异性的信号肽（蛋白质跨内质网膜转运的载体）、跨膜区段以及特定的细胞外和细胞内结构域，以此作为在细胞间和细胞内化学信号通路中可能发挥作用的指标。肽类激素、凝血因子、致癌剂及其他分泌蛋白在协调多细胞生命过程中的主要作用表明，它们在治疗重大疾病及众多生物技术应用方面具有药理学潜力。
