Constructing and Visualizing Models using Mime-based Machine-learning Framework

Hongwei Liu; Wei Zhang; Yihao Zhang; Xuejun Li; Siyi Wanggou

doi:10.3791/68553

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Method Article

Constructing and Visualizing Models using Mime-based Machine-learning Framework

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DOI:

10.3791/68553

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July 22nd, 2025

Hongwei Liu^*¹^,²^,³^,⁴ , Wei Zhang^*¹^,²^,³^,⁴ , Yihao Zhang^*¹^,²^,³^,⁴ , Xuejun Li¹^,²^,³^,⁴ , Siyi Wanggou¹^,²^,³^,⁴

¹Department of Neurosurgery, Xiangya Hospital, Central South University, ²National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, ³Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, ⁴Furong Laboratory

^* These authors contributed equally

Summary

Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.

Abstract

The widespread high-throughput sequencing technology has significantly enhanced our understanding of biology and cancer heterogeneity. Machine learning algorithms on transcriptional data have become vital for predicting patient prognosis and clinical responses. Despite advancements in machine learning algorithms, an open-source platform that incorporates the most sophisticated machine learning algorithms on transcriptional data remains absent. To address this gap, we developed Mime, a versatile machine-learning framework to enhance the construction and visualization of predictive models for clinical characteristics and gene signatures. By integrating diverse datasets and employing the most advanced feature selection techniques, Mime addresses critical challenges in clinical predictions. It provides three main functions, including model construction, feature selection, and data visualization. Model construction encompasses a range of machine learning algorithms, including but not limited to decision trees, support vector machines, and ensemble methods, allowing researchers to select the best-fitted approach for their specific analysis. Feature selection utilizes advanced algorithms such as Recursive Feature Elimination and LASSO regression to streamline the dataset and focus on the most informative features. The framework supports customizable parameter tuning through cross-validation methods, optimizing model performance while mitigating overfitting risks. Visualization tools integrated within Mime enable researchers to interpret model outcomes effectively, providing graphical representations of feature importance and predictive performance metrics. In this manuscript, we provide a detailed tutorial on the stepwise procedures of this versatile machine-learning framework.

Introduction

The widespread adoption of high-throughput sequencing technologies has significantly influenced our understanding of biology and cancer heterogeneity¹. This groundbreaking advancement in biotechnology has not only deepened our scientific knowledge but has also revolutionized the field of medical research. By enabling scientists to sequence large amounts of genetic material quickly and accurately, high-throughput sequencing has accelerated the discovery of new genes, mutations, and biological pathways. A growing body of research has delineated specific molecular signatures associated with disease progression, patient prognosis, and therapeutic r....