Cumbo Fabio, Truglia Simone, Weitschek Emanuel, Blankenberg Daniel
Center for Computational Life Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
Department of Engineering, Uninettuno University, Rome, Italy.
bioRxiv. 2024 Nov 20:2024.11.18.624180. doi: 10.1101/2024.11.18.624180.
The continuingly decreasing cost of next-generation sequencing has recently led to a significant increase in the number of microbiome-related studies, providing invaluable information for understanding host-microbiome interactions and their relation to diseases. A common approach in metagenomics consists of determining the composition of samples in terms of the amount and types of microbial species that populate them, with the goal to identify microbes whose profiles are able to differentiate samples under different conditions with advanced feature selection techniques. Here we propose a novel backward variable selection method based on the hyperdimensional computing paradigm, which takes inspiration from how the human brain works in the classification of concepts by encoding features into vectors in a high-dimensional space. We validated our method on public metagenomic samples collected from patients affected by colorectal cancer in a case/control scenario, by performing a comparative analysis with other state-of-the-art feature selection methods, obtaining promising results.
下一代测序成本的持续下降最近导致微生物组相关研究数量大幅增加，为理解宿主与微生物组的相互作用及其与疾病的关系提供了宝贵信息。宏基因组学中的一种常见方法是根据样本中微生物物种的数量和类型来确定样本的组成，目的是通过先进的特征选择技术识别出其特征能够区分不同条件下样本的微生物。在此，我们提出了一种基于超维计算范式的新型反向变量选择方法，该方法的灵感来源于人类大脑在概念分类时如何通过将特征编码到高维空间中的向量来工作。我们在病例/对照场景下，对从结直肠癌患者收集的公共宏基因组样本上验证了我们的方法，通过与其他最先进的特征选择方法进行比较分析，获得了有前景的结果。
