FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAs Article Swipe
Yanyu Li
,
Fengcui Qian
,
Guorui Zhang
,
Xuecang Li
,
Li‐Wei Zhou
,
Zhengmin Yu
,
Wei Liu
,
Qiuyu Wang
,
Chunquan Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbae623
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbae623
Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in molecular and cellular biology. Although many algorithms have been developed to reveal their associations with complex diseases by using downstream targets, the upstream (epi)genetic regulatory information has not been sufficiently leveraged to predict the function of lncRNAs in various biological processes. Therefore, we present FunlncModel, a machine learning–based interpretable computational framework, which aims to screen out functional lncRNAs by integrating a large number of (epi)genetic features and functional genomic features from their upstream/downstream multi-omic regulatory networks. We adopted the random forest method to mine nearly 60 features in three categories from >2000 datasets across 11 data types, including transcription factors (TFs), histone modifications, typical enhancers, super-enhancers, methylation sites, and mRNAs. FunlncModel outperformed alternative methods for classification performance in human embryonic stem cell (hESC) (0.95 Area Under Curve (AUROC) and 0.97 Area Under the Precision-Recall Curve (AUPRC)). It could not only infer the most known lncRNAs that influence the states of stem cells, but also discover novel high-confidence functional lncRNAs. We extensively validated FunlncModel’s efficacy by up to 27 cancer-related functional prediction tasks, which involved multiple cancer cell growth processes and cancer hallmarks. Meanwhile, we have also found that (epi)genetic regulatory features, such as TFs and histone modifications, serve as strong predictors for revealing the function of lncRNAs. Overall, FunlncModel is a strong and stable prediction model for identifying functional lncRNAs in specific cellular contexts. FunlncModel is available as a web server at https://bio.liclab.net/FunlncModel/.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbae623
- OA Status
- gold
- Cited By
- 1
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404789735
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4404789735Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/bib/bbae623Digital Object Identifier
- Title
-
FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-22Full publication date if available
- Authors
-
Yanyu Li, Fengcui Qian, Guorui Zhang, Xuecang Li, Li‐Wei Zhou, Zhengmin Yu, Wei Liu, Qiuyu Wang, Chunquan LiList of authors in order
- Landing page
-
https://doi.org/10.1093/bib/bbae623Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1093/bib/bbae623Direct OA link when available
- Concepts
-
Computational biology, Biology, Upstream (networking), Enhancer, Function (biology), Upstream and downstream (DNA), Computer science, Epigenetics, Machine learning, Transcription factor, Genetics, Gene, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
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82Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.found | 199 |
| abstract_inverted_index.human | 131 |
| abstract_inverted_index.infer | 153 |
| abstract_inverted_index.known | 156 |
| abstract_inverted_index.large | 74 |
| abstract_inverted_index.model | 228 |
| abstract_inverted_index.novel | 168 |
| abstract_inverted_index.roles | 11 |
| abstract_inverted_index.serve | 210 |
| abstract_inverted_index.their | 25, 84 |
| abstract_inverted_index.three | 101 |
| abstract_inverted_index.using | 31 |
| abstract_inverted_index.which | 64, 185 |
| abstract_inverted_index.(TFs), | 113 |
| abstract_inverted_index.(hESC) | 135 |
| abstract_inverted_index.across | 106 |
| abstract_inverted_index.cancer | 188, 193 |
| abstract_inverted_index.cells, | 164 |
| abstract_inverted_index.forest | 93 |
| abstract_inverted_index.growth | 190 |
| abstract_inverted_index.mRNAs. | 122 |
| abstract_inverted_index.method | 94 |
| abstract_inverted_index.nearly | 97 |
| abstract_inverted_index.number | 75 |
| abstract_inverted_index.random | 92 |
| abstract_inverted_index.reveal | 24 |
| abstract_inverted_index.screen | 67 |
| abstract_inverted_index.server | 243 |
| abstract_inverted_index.sites, | 120 |
| abstract_inverted_index.stable | 226 |
| abstract_inverted_index.states | 161 |
| abstract_inverted_index.strong | 212, 224 |
| abstract_inverted_index.tasks, | 184 |
| abstract_inverted_index.types, | 109 |
| abstract_inverted_index.(AUROC) | 140 |
| abstract_inverted_index.adopted | 90 |
| abstract_inverted_index.complex | 28 |
| abstract_inverted_index.factors | 112 |
| abstract_inverted_index.genomic | 81 |
| abstract_inverted_index.histone | 114, 208 |
| abstract_inverted_index.lncRNAs | 49, 70, 157, 232 |
| abstract_inverted_index.machine | 59 |
| abstract_inverted_index.methods | 126 |
| abstract_inverted_index.predict | 45 |
| abstract_inverted_index.present | 56 |
| abstract_inverted_index.typical | 116 |
| abstract_inverted_index.various | 51 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Although | 17 |
| abstract_inverted_index.Overall, | 220 |
| abstract_inverted_index.biology. | 16 |
| abstract_inverted_index.cellular | 15, 235 |
| abstract_inverted_index.datasets | 105 |
| abstract_inverted_index.discover | 167 |
| abstract_inverted_index.diseases | 29 |
| abstract_inverted_index.efficacy | 176 |
| abstract_inverted_index.evidence | 2 |
| abstract_inverted_index.features | 78, 82, 99 |
| abstract_inverted_index.function | 47, 217 |
| abstract_inverted_index.involved | 186 |
| abstract_inverted_index.lncRNAs. | 171, 219 |
| abstract_inverted_index.multiple | 187 |
| abstract_inverted_index.specific | 234 |
| abstract_inverted_index.targets, | 33 |
| abstract_inverted_index.upstream | 35 |
| abstract_inverted_index.(AUPRC)). | 148 |
| abstract_inverted_index.(lncRNAs) | 8 |
| abstract_inverted_index.available | 239 |
| abstract_inverted_index.contexts. | 236 |
| abstract_inverted_index.developed | 22 |
| abstract_inverted_index.embryonic | 132 |
| abstract_inverted_index.features, | 203 |
| abstract_inverted_index.important | 10 |
| abstract_inverted_index.including | 110 |
| abstract_inverted_index.indicates | 3 |
| abstract_inverted_index.influence | 159 |
| abstract_inverted_index.leveraged | 43 |
| abstract_inverted_index.molecular | 13 |
| abstract_inverted_index.networks. | 88 |
| abstract_inverted_index.noncoding | 6 |
| abstract_inverted_index.processes | 191 |
| abstract_inverted_index.revealing | 215 |
| abstract_inverted_index.validated | 174 |
| abstract_inverted_index.Meanwhile, | 195 |
| abstract_inverted_index.Therefore, | 54 |
| abstract_inverted_index.algorithms | 19 |
| abstract_inverted_index.biological | 52 |
| abstract_inverted_index.categories | 102 |
| abstract_inverted_index.downstream | 32 |
| abstract_inverted_index.enhancers, | 117 |
| abstract_inverted_index.framework, | 63 |
| abstract_inverted_index.functional | 69, 80, 170, 182, 231 |
| abstract_inverted_index.hallmarks. | 194 |
| abstract_inverted_index.multi-omic | 86 |
| abstract_inverted_index.prediction | 183, 227 |
| abstract_inverted_index.predictors | 213 |
| abstract_inverted_index.processes. | 53 |
| abstract_inverted_index.regulatory | 37, 87, 202 |
| abstract_inverted_index.FunlncModel | 123, 221, 237 |
| abstract_inverted_index.alternative | 125 |
| abstract_inverted_index.extensively | 173 |
| abstract_inverted_index.identifying | 230 |
| abstract_inverted_index.information | 38 |
| abstract_inverted_index.integrating | 72 |
| abstract_inverted_index.methylation | 119 |
| abstract_inverted_index.performance | 129 |
| abstract_inverted_index.>2000 | 104 |
| abstract_inverted_index.(epi)genetic | 36, 77, 201 |
| abstract_inverted_index.Accumulating | 1 |
| abstract_inverted_index.FunlncModel, | 57 |
| abstract_inverted_index.associations | 26 |
| abstract_inverted_index.outperformed | 124 |
| abstract_inverted_index.sufficiently | 42 |
| abstract_inverted_index.computational | 62 |
| abstract_inverted_index.interpretable | 61 |
| abstract_inverted_index.transcription | 111 |
| abstract_inverted_index.cancer-related | 181 |
| abstract_inverted_index.classification | 128 |
| abstract_inverted_index.modifications, | 115, 209 |
| abstract_inverted_index.FunlncModel’s | 175 |
| abstract_inverted_index.high-confidence | 169 |
| abstract_inverted_index.Precision-Recall | 146 |
| abstract_inverted_index.learning–based | 60 |
| abstract_inverted_index.super-enhancers, | 118 |
| abstract_inverted_index.upstream/downstream | 85 |
| abstract_inverted_index.https://bio.liclab.net/FunlncModel/. | 245 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5100728506 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I91935597 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.67707956 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |