Unified mRNA Subcellular Localization Predictor based on machine learning techniques
<h3>Background</h3><p dir="ltr">The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensi...
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| مؤلفون آخرون: | , , |
| منشور في: |
2024
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| _version_ | 1864513544645509120 |
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| author | Saleh Musleh (15279190) |
| author2 | Muhammad Arif (769250) Nehad M. Alajez (17930970) Tanvir Alam (638619) |
| author2_role | author author author |
| author_facet | Saleh Musleh (15279190) Muhammad Arif (769250) Nehad M. Alajez (17930970) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Saleh Musleh (15279190) Muhammad Arif (769250) Nehad M. Alajez (17930970) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2024-02-07T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s12864-024-10077-9 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Unified_mRNA_Subcellular_Localization_Predictor_based_on_machine_learning_techniques/29590343 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Information and computing sciences Machine learning Multiclass classification mRNA Subcellular Localization Machine learning |
| dc.title.none.fl_str_mv | Unified mRNA Subcellular Localization Predictor based on machine learning techniques |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost.</p><h3>Methods</h3><p dir="ltr">In this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an <i>in silico</i> strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER).</p><h3>Results</h3><p dir="ltr">The proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Genomics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12864-024-10077-9" target="_blank">https://dx.doi.org/10.1186/s12864-024-10077-9</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ff0245a30cc45f0dc93569534d4437c0 |
| identifier_str_mv | 10.1186/s12864-024-10077-9 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29590343 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Unified mRNA Subcellular Localization Predictor based on machine learning techniquesSaleh Musleh (15279190)Muhammad Arif (769250)Nehad M. Alajez (17930970)Tanvir Alam (638619)Biological sciencesBioinformatics and computational biologyInformation and computing sciencesMachine learningMulticlass classificationmRNASubcellular LocalizationMachine learning<h3>Background</h3><p dir="ltr">The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost.</p><h3>Methods</h3><p dir="ltr">In this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an <i>in silico</i> strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER).</p><h3>Results</h3><p dir="ltr">The proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Genomics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12864-024-10077-9" target="_blank">https://dx.doi.org/10.1186/s12864-024-10077-9</a></p>2024-02-07T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s12864-024-10077-9https://figshare.com/articles/journal_contribution/Unified_mRNA_Subcellular_Localization_Predictor_based_on_machine_learning_techniques/29590343CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/295903432024-02-07T03:00:00Z |
| spellingShingle | Unified mRNA Subcellular Localization Predictor based on machine learning techniques Saleh Musleh (15279190) Biological sciences Bioinformatics and computational biology Information and computing sciences Machine learning Multiclass classification mRNA Subcellular Localization Machine learning |
| status_str | publishedVersion |
| title | Unified mRNA Subcellular Localization Predictor based on machine learning techniques |
| title_full | Unified mRNA Subcellular Localization Predictor based on machine learning techniques |
| title_fullStr | Unified mRNA Subcellular Localization Predictor based on machine learning techniques |
| title_full_unstemmed | Unified mRNA Subcellular Localization Predictor based on machine learning techniques |
| title_short | Unified mRNA Subcellular Localization Predictor based on machine learning techniques |
| title_sort | Unified mRNA Subcellular Localization Predictor based on machine learning techniques |
| topic | Biological sciences Bioinformatics and computational biology Information and computing sciences Machine learning Multiclass classification mRNA Subcellular Localization Machine learning |