Volume 3,Issue 9
基于最佳特征集肿瘤凝集素的分类研究
肿瘤凝集素与癌症关联密切，可应用于临床诊断、治疗、药物输送及癌症靶向领域。因此，提升其分类准确性对疾病研究具有重要意义，能为深入理解和攻克癌症提供关键支持。本文构建了一种基于机器学习的肿瘤凝集素计算方法（ZTL_M），该方法通过 monoDiKGap 提取特征，经F-score筛选得到最优特征集，再利用多层感知器分类器完成识别。实验采用5倍交叉验证，结果显示 ZTL_M对肿瘤凝集素的识别准确率达96.3%，使用monoDiKGap可以提高模型识别肿瘤凝集素的能力，ZTL_M方法比一些现有方法具有更好的性能。
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