Evidence map›Paper›PMID 39055398›Full record

ArticleComputational and structural biotechnology journal2024

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

Hongwei Liu, Wei Zhang, Yihao Zhang, Abraham Ayodeji Adegboro, Deborah Oluwatosin Fasoranti, Luohuan Dai, Zhouyang Pan, Hongyi Liu, Yi Xiong, Wang Li and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers.

0numbers the graph read from it
0cells of the map it votes in
147citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

147 citing papers in PubMed.

  1. Article
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87 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Hongwei LiuDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Wei ZhangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Yihao ZhangDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Abraham Ayodeji AdegboroDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Deborah Oluwatosin FasorantiDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Luohuan DaiDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Zhouyang PanDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Hongyi LiuDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Yi XiongDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Wang LiDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Kang PengHunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Siyi WanggouDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Xuejun LiDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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).

Indexed as

GitHubMachine learningMimePIEZO1Prediction modelsR package

Identifiers

PMID39055398
PMCPMC11269309

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.