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Screening disease feature genes and analyzing correlations with immune cell infiltration in knee osteoarthritis chondrocytes based on multiple machine learning algorithms
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| Published in: | PLOS ONE |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
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| _version_ | 1868553972565934080 |
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| collection | WordPress RSS FRELIP Feed Integration |
| container_title | PLOS ONE |
| description | |
| discipline_display | Cybersecurity_journals |
| discipline_facet | Cybersecurity_journals |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:92528 |
| institution | FRELIP |
| journal_source_facet | PLOS ONE |
| last_indexed | 2026-06-20T21:45:31.151Z |
| publishDate | 2026 |
| publishDateSort | 2026 |
| record_format | rss_article |
| spellingShingle | Screening disease feature genes and analyzing correlations with immune cell infiltration in knee osteoarthritis chondrocytes based on multiple machine learning algorithms Cybersecurity_journals General Cybersecurity_journals |
| sub_discipline_display | General |
| sub_discipline_facet | General |
| subject_display | Cybersecurity_journals General Cybersecurity_journals |
| subject_facet | Cybersecurity_journals General Cybersecurity_journals |
| title | Screening disease feature genes and analyzing correlations with immune cell infiltration in knee osteoarthritis chondrocytes based on multiple machine learning algorithms |
| title_alt | Detección de genes característicos de enfermedad y análisis de correlaciones con infiltración de células inmunitarias en condrocitos de osteoartritis de rodilla basados en múltiples algoritmos de aprendizaje automático Identification des gènes caractéristiques de la maladie et analyse des corrélations avec l'infiltration de cellules immunitaires dans les chondrocytes de l'arthrose du genou basée sur plusieurs algorithmes d'apprentissage automatique Triagem de genes característicos de doenças e análise de correlações com infiltração de células imunes em condrócitos de osteoartrite do joelho com base em múltiplos algoritmos de aprendizado de máquina |
| title_auth | Screening disease feature genes and analyzing correlations with immune cell infiltration in knee osteoarthritis chondrocytes based on multiple machine learning algorithms |
| title_es_txt | Detección de genes característicos de enfermedad y análisis de correlaciones con infiltración de células inmunitarias en condrocitos de osteoartritis de rodilla basados en múltiples algoritmos de aprendizaje automático |
| title_fr_txt | Identification des gènes caractéristiques de la maladie et analyse des corrélations avec l'infiltration de cellules immunitaires dans les chondrocytes de l'arthrose du genou basée sur plusieurs algorithmes d'apprentissage automatique |
| title_full | Screening disease feature genes and analyzing correlations with immune cell infiltration in knee osteoarthritis chondrocytes based on multiple machine learning algorithms |
| title_fullStr | Screening disease feature genes and analyzing correlations with immune cell infiltration in knee osteoarthritis chondrocytes based on multiple machine learning algorithms |
| title_full_unstemmed | Screening disease feature genes and analyzing correlations with immune cell infiltration in knee osteoarthritis chondrocytes based on multiple machine learning algorithms |
| title_pt_txt | Triagem de genes característicos de doenças e análise de correlações com infiltração de células imunes em condrócitos de osteoartrite do joelho com base em múltiplos algoritmos de aprendizado de máquina |
| title_short | Screening disease feature genes and analyzing correlations with immune cell infiltration in knee osteoarthritis chondrocytes based on multiple machine learning algorithms |
| title_sort | screening disease feature genes and analyzing correlations with immune cell infiltration in knee osteoarthritis chondrocytes based on multiple machine learning algorithms |
| topic | Cybersecurity_journals General Cybersecurity_journals |
| url | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0351666 |