A hybrid approach for predicting multi-label subcellular localization of mRNA at genome scale Article Swipe
Shubham Choudhury
,
Nisha Bajiya
,
Sumeet Patiyal
,
Gajendra P. S. Raghava
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.01.17.524365
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.01.17.524365
In the past, number of methods have been developed for predicting single label subcellular localization of mRNA in a cell. Only limited methods had been built to predict multi-label subcellular localization of mRNA. Most of the existing methods are slow and cannot be implemented at transcriptome scale. In this study, a fast and reliable method had been developed for predicting multi-label subcellular localization of mRNA that can be implemented at genome scale. Firstly, deep learning method based on convolutional neural network method have been developed using one-hot encoding and attained an average AUROC - 0.584 (0.543 – 0.605). Secondly, machine learning based methods have been developed using mRNA sequence composition, our XGBoost classifier achieved an average AUROC - 0.709 (0.668 - 0.732). In addition to alignment free methods, we also developed alignment-based methods using similarity and motif search techniques. Finally, a hybrid technique has been developed that combine XGBoost models and motif-based searching and achieved an average AUROC 0.742 (0.708 - 0.816). Our method – MRSLpred, developed in this study is complementary to the existing method. One of the major advantages of our method over existing methods is its speed, it can scan all mRNA of a transcriptome in few hours. A publicly accessible webserver and a standalone tool has been developed to facilitate researchers (Webserver: https://webs.iiitd.edu.in/raghava/mrslpred/ ). Key Points Prediction of Subcellular localization of mRNA Classification of mRNA based on Motif and BLAST search Combination of alignment based and alignment free techniques A fast method for subcellular localization of mRNA A web server and standalone software
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.01.17.524365
- https://www.biorxiv.org/content/biorxiv/early/2023/01/19/2023.01.17.524365.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4317497639
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4317497639Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.01.17.524365Digital Object Identifier
- Title
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A hybrid approach for predicting multi-label subcellular localization of mRNA at genome scaleWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-19Full publication date if available
- Authors
-
Shubham Choudhury, Nisha Bajiya, Sumeet Patiyal, Gajendra P. S. RaghavaList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.01.17.524365Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2023/01/19/2023.01.17.524365.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2023/01/19/2023.01.17.524365.full.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Subcellular localization, Classifier (UML), Computational biology, Convolutional neural network, Genome, Pattern recognition (psychology), Data mining, Biology, Gene, GeneticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.has | 144, 210 |
| abstract_inverted_index.its | 189 |
| abstract_inverted_index.our | 111, 183 |
| abstract_inverted_index.the | 2, 36, 174, 179 |
| abstract_inverted_index.web | 253 |
| abstract_inverted_index.– | 97, 165 |
| abstract_inverted_index.Most | 34 |
| abstract_inverted_index.Only | 21 |
| abstract_inverted_index.also | 130 |
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| abstract_inverted_index.deep | 74 |
| abstract_inverted_index.fast | 52, 245 |
| abstract_inverted_index.free | 127, 242 |
| abstract_inverted_index.have | 7, 83, 104 |
| abstract_inverted_index.mRNA | 17, 65, 108, 195, 226, 229, 251 |
| abstract_inverted_index.over | 185 |
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| abstract_inverted_index.slow | 40 |
| abstract_inverted_index.that | 66, 147 |
| abstract_inverted_index.this | 49, 169 |
| abstract_inverted_index.tool | 209 |
| abstract_inverted_index.0.584 | 95 |
| abstract_inverted_index.0.709 | 119 |
| abstract_inverted_index.0.742 | 159 |
| abstract_inverted_index.AUROC | 93, 117, 158 |
| abstract_inverted_index.BLAST | 234 |
| abstract_inverted_index.Motif | 232 |
| abstract_inverted_index.based | 77, 102, 230, 239 |
| abstract_inverted_index.built | 26 |
| abstract_inverted_index.cell. | 20 |
| abstract_inverted_index.label | 13 |
| abstract_inverted_index.mRNA. | 33 |
| abstract_inverted_index.major | 180 |
| abstract_inverted_index.motif | 137 |
| abstract_inverted_index.past, | 3 |
| abstract_inverted_index.study | 170 |
| abstract_inverted_index.using | 86, 107, 134 |
| abstract_inverted_index.(0.543 | 96 |
| abstract_inverted_index.(0.668 | 120 |
| abstract_inverted_index.(0.708 | 160 |
| abstract_inverted_index.Points | 220 |
| abstract_inverted_index.cannot | 42 |
| abstract_inverted_index.genome | 71 |
| abstract_inverted_index.hours. | 201 |
| abstract_inverted_index.hybrid | 142 |
| abstract_inverted_index.method | 55, 76, 82, 164, 184, 246 |
| abstract_inverted_index.models | 150 |
| abstract_inverted_index.neural | 80 |
| abstract_inverted_index.number | 4 |
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| abstract_inverted_index.search | 138, 235 |
| abstract_inverted_index.server | 254 |
| abstract_inverted_index.single | 12 |
| abstract_inverted_index.speed, | 190 |
| abstract_inverted_index.study, | 50 |
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| abstract_inverted_index.0.732). | 122 |
| abstract_inverted_index.0.816). | 162 |
| abstract_inverted_index.XGBoost | 112, 149 |
| abstract_inverted_index.average | 92, 116, 157 |
| abstract_inverted_index.combine | 148 |
| abstract_inverted_index.limited | 22 |
| abstract_inverted_index.machine | 100 |
| abstract_inverted_index.method. | 176 |
| abstract_inverted_index.methods | 6, 23, 38, 103, 133, 187 |
| abstract_inverted_index.network | 81 |
| abstract_inverted_index.one-hot | 87 |
| abstract_inverted_index.predict | 28 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Finally, | 140 |
| abstract_inverted_index.Firstly, | 73 |
| abstract_inverted_index.achieved | 114, 155 |
| abstract_inverted_index.addition | 124 |
| abstract_inverted_index.attained | 90 |
| abstract_inverted_index.encoding | 88 |
| abstract_inverted_index.existing | 37, 175, 186 |
| abstract_inverted_index.learning | 75, 101 |
| abstract_inverted_index.methods, | 128 |
| abstract_inverted_index.publicly | 203 |
| abstract_inverted_index.reliable | 54 |
| abstract_inverted_index.sequence | 109 |
| abstract_inverted_index.software | 257 |
| abstract_inverted_index.MRSLpred, | 166 |
| abstract_inverted_index.Secondly, | 99 |
| abstract_inverted_index.alignment | 126, 238, 241 |
| abstract_inverted_index.developed | 9, 58, 85, 106, 131, 146, 167, 212 |
| abstract_inverted_index.searching | 153 |
| abstract_inverted_index.technique | 143 |
| abstract_inverted_index.webserver | 205 |
| abstract_inverted_index.Prediction | 221 |
| abstract_inverted_index.accessible | 204 |
| abstract_inverted_index.advantages | 181 |
| abstract_inverted_index.classifier | 113 |
| abstract_inverted_index.facilitate | 214 |
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| abstract_inverted_index.techniques | 243 |
| abstract_inverted_index.(Webserver: | 216 |
| abstract_inverted_index.Combination | 236 |
| abstract_inverted_index.Subcellular | 223 |
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| abstract_inverted_index.motif-based | 152 |
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| abstract_inverted_index.researchers | 215 |
| abstract_inverted_index.subcellular | 14, 30, 62, 248 |
| abstract_inverted_index.techniques. | 139 |
| abstract_inverted_index.composition, | 110 |
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| abstract_inverted_index.Classification | 227 |
| abstract_inverted_index.alignment-based | 132 |
| abstract_inverted_index.https://webs.iiitd.edu.in/raghava/mrslpred/ | 217 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5051938893 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I119939252 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
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| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.55999854 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |