Improving Model Performance on the Stratification of Breast Cancer Patients by Integrating Multiscale Genomic Features.

Yingyi Hao, Li He, Yifan Zhou, Yiru Zhao, Menglong Li, Runyu Jing, Zhining Wen

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Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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2 · The registry

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5 · Who and what money

Authors and funding

7 authors.

Yingyi HaoCollege of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China.

Li HeBiogas Appliance Quality Supervision and Inspection Center, Biogas Institute of Ministry of Agriculture, Chengdu, Sichuan 610041, China.

Yifan ZhouCollege of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China.

Yiru ZhaoCollege of Computer Science, Sichuan University, Chengdu, Sichuan 610064, China.

Menglong LiCollege of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China.ORCID https://orcid.org/0000-0001-7365-0344

Runyu JingCollege of Cybersecurity, Sichuan University, Chengdu, Sichuan 610064, China.ORCID https://orcid.org/0000-0003-3375-1014

Zhining WenCollege of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China.ORCID https://orcid.org/0000-0002-0477-4906

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In clinical cancer research, it is a hot topic on how to accurately stratify patients based on genomic data. With the development of next-generation sequencing technology, more and more types of genomic features, such as mRNA expression level, can be used to distinguish cancer patients. Previous studies commonly stratified patients by using a single type of genomic features, which can only reflect one aspect of the cancer. In fact, multiscale genomic features will provide more information and may be helpful for clinical prediction. In addition, most of the conventional machine learning algorithms use a handcrafted gene set as features to construct models, which is generally selected by a statistical method with an arbitrary cut-off, e.g.,

Indexed as

Models, GeneticAlgorithmsBreast NeoplasmsCarcinoma, Renal CellDatabases, GeneticFemaleGene Expression Regulation, NeoplasticGene OntologyGenomicsHumansKidney NeoplasmsMachine LearningMicroRNAsReceptors, EstrogenRNA, MessengerThyroid NeoplasmsMicroRNAsReceptors, EstrogenRNA, Messenger

Identifiers

PMID32908867

DOI10.1155/2020/1475368

PMCPMC7471833

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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.