10.2肺鳞状细胞癌.zip
<p dir="ltr">We interrogated multi-omics data from The Cancer Genome Atlas (TCGA) and other public databases including genomic sequencing, gene expression, miRNA expression protein expression and metabolite profiles of lung cancer patients. In our study, different ML algorithms inclu...
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| المؤلف الرئيسي: | |
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| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
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| _version_ | 1852026007281729536 |
|---|---|
| author | Zhiwei Xv (19835028) |
| author_facet | Zhiwei Xv (19835028) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zhiwei Xv (19835028) |
| dc.date.none.fl_str_mv | 2024-10-11T06:11:28Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.27209688.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/10_2_zip/27209688 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological network analysis machine learning antibiotic |
| dc.title.none.fl_str_mv | 10.2肺鳞状细胞癌.zip |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p dir="ltr">We interrogated multi-omics data from The Cancer Genome Atlas (TCGA) and other public databases including genomic sequencing, gene expression, miRNA expression protein expression and metabolite profiles of lung cancer patients. In our study, different ML algorithms including random forest, support vector machines, neural networks and deep learning models were used to construct predictive models for diagnosing lung cancer disease, treatment response and prognosis.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_d8d3bcbd14fbb88decc256e990987972 |
| identifier_str_mv | 10.6084/m9.figshare.27209688.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27209688 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | 10.2肺鳞状细胞癌.zipZhiwei Xv (19835028)Biological network analysismachine learning antibiotic<p dir="ltr">We interrogated multi-omics data from The Cancer Genome Atlas (TCGA) and other public databases including genomic sequencing, gene expression, miRNA expression protein expression and metabolite profiles of lung cancer patients. In our study, different ML algorithms including random forest, support vector machines, neural networks and deep learning models were used to construct predictive models for diagnosing lung cancer disease, treatment response and prognosis.</p>2024-10-11T06:11:28ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.27209688.v1https://figshare.com/articles/dataset/10_2_zip/27209688CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272096882024-10-11T06:11:28Z |
| spellingShingle | 10.2肺鳞状细胞癌.zip Zhiwei Xv (19835028) Biological network analysis machine learning antibiotic |
| status_str | publishedVersion |
| title | 10.2肺鳞状细胞癌.zip |
| title_full | 10.2肺鳞状细胞癌.zip |
| title_fullStr | 10.2肺鳞状细胞癌.zip |
| title_full_unstemmed | 10.2肺鳞状细胞癌.zip |
| title_short | 10.2肺鳞状细胞癌.zip |
| title_sort | 10.2肺鳞状细胞癌.zip |
| topic | Biological network analysis machine learning antibiotic |