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Journal Article Epublish 132 citations

PubMed: 39055398 DOI: 10.1016/j.csbj.2024.06.035

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection.

Comput Struct Biotechnol J 23:2798-2810

Authors: Hongwei Liu, Wei Zhang, Yihao Zhang, Abraham Ayodeji Adegboro, Deborah Oluwatosin Fasoranti, Luohuan Dai, Zhouyang Pan, Hongyi Liu, Yi Xiong, Wang Li, Kang Peng, Siyi Wanggou, Xuejun Li

Affiliations

  • Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
  • Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
  • Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China. Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
  • Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China. Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.

Abstract

The widespread use of high-throughput sequencing technologies has revolutionized the understanding of biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome and clinical response. However, an open-source R package covering state-of-the-art machine-learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct a machine learning-based integration model with elegant performance (Mime). Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting the outcomes of patients compared with other published models. Furthermore, the PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from the PIEZO1-associated signatures demonstrated high potential as a glioma target. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields. The Mime package is available on GitHub (https://github.com/l-magnificence/Mime).

MeSH

Algorithms, Machine Learning, Learning

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