Computational Representation of Cellular Lines: A Text Mining Approach
In the rapidly evolving landscape of cancer drug research, cellular lines serve as invaluable tools for understanding drug-sensitive and drug-resistant tumors. The computational representation of cellular lines is usually based on genomic profiling, even though this method cannot be applied in a lar...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/47/1/13 |
| _version_ | 1797241196215533568 |
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| author | Ivan Carrera Henry Guanoluisa Alexis Miranda |
| author_facet | Ivan Carrera Henry Guanoluisa Alexis Miranda |
| author_sort | Ivan Carrera |
| collection | DOAJ |
| description | In the rapidly evolving landscape of cancer drug research, cellular lines serve as invaluable tools for understanding drug-sensitive and drug-resistant tumors. The computational representation of cellular lines is usually based on genomic profiling, even though this method cannot be applied in a large scale. This study introduces a novel approach to the computational representation of cellular lines using text mining techniques. By meticulously extracting and analyzing textual data from the scientific literature, we developed a computational representation of these cellular lines. Our methodology encompassed advanced Natural Language Processing (NLP) for text extraction and machine learning models for predictive analysis. We achieved a comprehensive description of each cellular line. To validate our findings, we generated a distance matrix for all cellular lines, leading to the construction of a dendrogram representing cellular line relationships. This dendrogram shows a resemblance with the established cell line ontology from CLO. Our results bridge the gap between cellular line representation and text mining, offering a robust computational model that can significantly impact cancer drug research. |
| first_indexed | 2024-04-24T18:19:29Z |
| format | Article |
| id | doaj.art-bc9412cd4ee34f0696fa78a089bdb32e |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-04-24T18:19:29Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-bc9412cd4ee34f0696fa78a089bdb32e2024-03-27T13:36:38ZengMDPI AGEngineering Proceedings2673-45912023-12-014711310.3390/engproc2023047013Computational Representation of Cellular Lines: A Text Mining ApproachIvan Carrera0Henry Guanoluisa1Alexis Miranda2Departamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito 170525, EcuadorDepartamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito 170525, EcuadorDepartamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito 170525, EcuadorIn the rapidly evolving landscape of cancer drug research, cellular lines serve as invaluable tools for understanding drug-sensitive and drug-resistant tumors. The computational representation of cellular lines is usually based on genomic profiling, even though this method cannot be applied in a large scale. This study introduces a novel approach to the computational representation of cellular lines using text mining techniques. By meticulously extracting and analyzing textual data from the scientific literature, we developed a computational representation of these cellular lines. Our methodology encompassed advanced Natural Language Processing (NLP) for text extraction and machine learning models for predictive analysis. We achieved a comprehensive description of each cellular line. To validate our findings, we generated a distance matrix for all cellular lines, leading to the construction of a dendrogram representing cellular line relationships. This dendrogram shows a resemblance with the established cell line ontology from CLO. Our results bridge the gap between cellular line representation and text mining, offering a robust computational model that can significantly impact cancer drug research.https://www.mdpi.com/2673-4591/47/1/13text miningnatural language processingpredictive modelingmachine learningcellular line representationdrug response prediction |
| spellingShingle | Ivan Carrera Henry Guanoluisa Alexis Miranda Computational Representation of Cellular Lines: A Text Mining Approach Engineering Proceedings text mining natural language processing predictive modeling machine learning cellular line representation drug response prediction |
| title | Computational Representation of Cellular Lines: A Text Mining Approach |
| title_full | Computational Representation of Cellular Lines: A Text Mining Approach |
| title_fullStr | Computational Representation of Cellular Lines: A Text Mining Approach |
| title_full_unstemmed | Computational Representation of Cellular Lines: A Text Mining Approach |
| title_short | Computational Representation of Cellular Lines: A Text Mining Approach |
| title_sort | computational representation of cellular lines a text mining approach |
| topic | text mining natural language processing predictive modeling machine learning cellular line representation drug response prediction |
| url | https://www.mdpi.com/2673-4591/47/1/13 |
| work_keys_str_mv | AT ivancarrera computationalrepresentationofcellularlinesatextminingapproach AT henryguanoluisa computationalrepresentationofcellularlinesatextminingapproach AT alexismiranda computationalrepresentationofcellularlinesatextminingapproach |