标题： 一种使用多视图特征选择方法和集成分类器的泛癌分类模型 显示英文标题

标题： A Pan-cancer Classification Model using Multi-view Feature Selection Method and Ensemble Classifier

摘要： 准确识别癌症样本对于精确诊断和有效的患者治疗至关重要。 传统方法在高维数据和高特征与样本数量比的情况下表现不佳，这对于分类癌症样本至关重要。 本研究旨在开发一种针对转录组数据的新型特征选择框架，并提出两种集成分类器。 在特征选择方面，我们根据特征类型对转录组数据集进行垂直划分。 然后在每个划分上应用Boruta特征选择过程，合并结果，并在合并结果上再次应用Boruta。 我们使用不同的Boruta参数重复该过程，并准备最终的特征集。 最后，我们基于LR、SVM和XGBoost分类器构建了两个集成机器学习模型，采用最大投票和概率平均方法。 我们使用10折交叉验证来确保分类性能的稳健性和可靠性。 我们的方法在33种癌症分类中表现出色，准确率为97.11%，AUC值为0.9996，优于现有的最先进方法。 一组12种类型的癌症由于其来源组织的相似性，传统上难以相互区分。 我们的方法准确识别了这些12种癌症类型中超过90%的样本，这优于现有文献中所有已知的方法。 基因集富集分析显示，我们框架所选择的特征丰富了与癌症高度相关的通路。 本研究开发了一种特征选择框架，以选择与癌症发展高度相关的特征，并提高了识别不同类型癌症样本的准确性。 显示英文摘要

摘要： Accurately identifying cancer samples is crucial for precise diagnosis and effective patient treatment. Traditional methods falter with high-dimensional and high feature-to-sample count ratios, which are critical for classifying cancer samples. This study aims to develop a novel feature selection framework specifically for transcriptome data and propose two ensemble classifiers. For feature selection, we partition the transcriptome dataset vertically based on feature types. Then apply the Boruta feature selection process on each of the partitions, combine the results, and apply Boruta again on the combined result. We repeat the process with different parameters of Boruta and prepare the final feature set. Finally, we constructed two ensemble ML models based on LR, SVM and XGBoost classifiers with max voting and averaging probability approach. We used 10-fold cross-validation to ensure robust and reliable classification performance. With 97.11\% accuracy and 0.9996 AUC value, our approach performs better compared to existing state-of-the-art methods to classify 33 types of cancers. A set of 12 types of cancer is traditionally challenging to differentiate between each other due to their similarity in tissue of origin. Our method accurately identifies over 90\% of samples from these 12 types of cancers, which outperforms all known methods presented in existing literature. The gene set enrichment analysis reveals that our framework's selected features have enriched the pathways highly related to cancers. This study develops a feature selection framework to select features highly related to cancer development and leads to identifying different types of cancer samples with higher accuracy.