CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration Article Swipe
Tarek Eissa
,
Marinus Huber
,
Barbara Obermayer‐Pietsch
,
Birgit Linkohr
,
Annette Peters
,
Frank Fleischmann
,
Mihaela Žigman
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.06.15.598503
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.06.15.598503
Molecular analytics increasingly utilize machine learning (ML) for predictive modeling based on data acquired through molecular profiling technologies. However, developing robust models that accurately capture physiological phenotypes is challenged by a multitude of factors. These include the dynamics inherent to biological systems, variability stemming from analytical procedures, and the resource-intensive nature of obtaining sufficiently representative datasets. Here, we propose and evaluate a new method: Contextual Out-of-Distribution Integration (CODI). Based on experimental observations, CODI generates synthetic data that integrate unrepresented sources of variation encountered in real-world applications into a given molecular fingerprint dataset. By augmenting a dataset with out-of-distribution variance, CODI enables an ML model to better generalize to samples beyond the initial training data. Using three independent longitudinal clinical studies and a case-control study, we demonstrate CODI’s application to several classification scenarios involving vibrational spectroscopy of human blood. We showcase our approach’s ability to enable personalized fingerprinting for multi-year longitudinal molecular monitoring and enhance the robustness of trained ML models for improved disease detection. Our comparative analyses revealed that incorporating CODI into the classification workflow consistently led to significantly improved classification accuracy while minimizing the requirement of collecting extensive experimental observations. SIGNIFICANCE STATEMENT Analyzing molecular fingerprint data is challenging due to multiple sources of biological and analytical variability. This variability hinders the capacity to collect sufficiently large and representative datasets that encompass realistic data distributions. Consequently, the development of machine learning models that generalize to unseen, independently collected samples is often compromised. Here, we introduce CODI, a versatile framework that enhances traditional classifier training methodologies. CODI is a general framework that incorporates information about possible out-of-distribution variations into a given training dataset, augmenting it with simulated samples that better capture the true distribution of the data. This allows the classification to achieve improved predictive performance on samples beyond the original distribution of the training data.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.06.15.598503
- https://www.biorxiv.org/content/biorxiv/early/2024/06/17/2024.06.15.598503.full.pdf
- OA Status
- green
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399790188
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399790188Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.06.15.598503Digital Object Identifier
- Title
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CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integrationWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-17Full publication date if available
- Authors
-
Tarek Eissa, Marinus Huber, Barbara Obermayer‐Pietsch, Birgit Linkohr, Annette Peters, Frank Fleischmann, Mihaela ŽigmanList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.06.15.598503Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2024/06/17/2024.06.15.598503.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
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2024/06/17/2024.06.15.598503.full.pdfDirect OA link when available
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Profiling (computer programming), Distribution (mathematics), Computer science, Data science, Mathematics, Operating system, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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63Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2169625443, https://openalex.org/W2087366831, https://openalex.org/W1986202285, https://openalex.org/W4386828769, https://openalex.org/W3196242974, https://openalex.org/W3036229468, https://openalex.org/W2000034029, https://openalex.org/W95925227, https://openalex.org/W3110616531, https://openalex.org/W2028138594, https://openalex.org/W3198414654, https://openalex.org/W2811374795, https://openalex.org/W2525984666, https://openalex.org/W1978694357, https://openalex.org/W3135265693, https://openalex.org/W2116650193, https://openalex.org/W2946046628, https://openalex.org/W4296039169, https://openalex.org/W2019497601, https://openalex.org/W3196850540, https://openalex.org/W4313260763, https://openalex.org/W4375956160, https://openalex.org/W4312710251, https://openalex.org/W4224797805, https://openalex.org/W2809598685, https://openalex.org/W3176923149, https://openalex.org/W4223462374, https://openalex.org/W2046671000, https://openalex.org/W3210826506, https://openalex.org/W3135749556, https://openalex.org/W4365135723, https://openalex.org/W2027762699, https://openalex.org/W2018289193, https://openalex.org/W4386334003, https://openalex.org/W2582150691, https://openalex.org/W2011055829, https://openalex.org/W2017076975, https://openalex.org/W1503912205, https://openalex.org/W2084680372, https://openalex.org/W4400109558, https://openalex.org/W3033135634, https://openalex.org/W3033083983, https://openalex.org/W4280587146, https://openalex.org/W2949012161, https://openalex.org/W2126525177, https://openalex.org/W2147562834, https://openalex.org/W3113344283, https://openalex.org/W2944477354, https://openalex.org/W3210254907, https://openalex.org/W2794521141, https://openalex.org/W3023937217, https://openalex.org/W2783914690, https://openalex.org/W1610928686, https://openalex.org/W2986650563, https://openalex.org/W3082568354, https://openalex.org/W2792384849, https://openalex.org/W2762818368, https://openalex.org/W4210522464, https://openalex.org/W3001197829, https://openalex.org/W2466989778, https://openalex.org/W3194918895, https://openalex.org/W4388834501, https://openalex.org/W3159800610 |
| referenced_works_count | 63 |
| abstract_inverted_index.a | 31, 62, 88, 95, 122, 247, 258, 269 |
| abstract_inverted_index.By | 93 |
| abstract_inverted_index.ML | 103, 159 |
| abstract_inverted_index.We | 139 |
| abstract_inverted_index.an | 102 |
| abstract_inverted_index.by | 30 |
| abstract_inverted_index.in | 84 |
| abstract_inverted_index.is | 28, 198, 240, 257 |
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| abstract_inverted_index.on | 12, 70, 296 |
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| abstract_inverted_index.we | 58, 125, 244 |
| abstract_inverted_index.Our | 165 |
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| abstract_inverted_index.due | 200 |
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| abstract_inverted_index.new | 63 |
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| abstract_inverted_index.(ML) | 7 |
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| abstract_inverted_index.with | 97, 275 |
| abstract_inverted_index.Based | 69 |
| abstract_inverted_index.CODI, | 246 |
| abstract_inverted_index.Here, | 57, 243 |
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| abstract_inverted_index.Using | 115 |
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| abstract_inverted_index.data. | 114, 286, 305 |
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| abstract_inverted_index.beyond | 110, 298 |
| abstract_inverted_index.blood. | 138 |
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| abstract_inverted_index.nature | 51 |
| abstract_inverted_index.robust | 21 |
| abstract_inverted_index.study, | 124 |
| abstract_inverted_index.(CODI). | 68 |
| abstract_inverted_index.ability | 143 |
| abstract_inverted_index.achieve | 292 |
| abstract_inverted_index.capture | 25, 280 |
| abstract_inverted_index.collect | 215 |
| abstract_inverted_index.dataset | 96 |
| abstract_inverted_index.disease | 163 |
| abstract_inverted_index.enables | 101 |
| abstract_inverted_index.enhance | 154 |
| abstract_inverted_index.general | 259 |
| abstract_inverted_index.hinders | 211 |
| abstract_inverted_index.include | 36 |
| abstract_inverted_index.initial | 112 |
| abstract_inverted_index.machine | 5, 230 |
| abstract_inverted_index.method: | 64 |
| abstract_inverted_index.propose | 59 |
| abstract_inverted_index.samples | 109, 239, 277, 297 |
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| abstract_inverted_index.sources | 80, 203 |
| abstract_inverted_index.studies | 120 |
| abstract_inverted_index.through | 15 |
| abstract_inverted_index.trained | 158 |
| abstract_inverted_index.unseen, | 236 |
| abstract_inverted_index.utilize | 4 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.CODI’s | 127 |
| abstract_inverted_index.However, | 19 |
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| abstract_inverted_index.analyses | 167 |
| abstract_inverted_index.capacity | 213 |
| abstract_inverted_index.clinical | 119 |
| abstract_inverted_index.dataset, | 272 |
| abstract_inverted_index.dataset. | 92 |
| abstract_inverted_index.datasets | 220 |
| abstract_inverted_index.dynamics | 38 |
| abstract_inverted_index.enhances | 251 |
| abstract_inverted_index.evaluate | 61 |
| abstract_inverted_index.factors. | 34 |
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| abstract_inverted_index.inherent | 39 |
| abstract_inverted_index.learning | 6, 231 |
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| abstract_inverted_index.revealed | 168 |
| abstract_inverted_index.showcase | 140 |
| abstract_inverted_index.stemming | 44 |
| abstract_inverted_index.systems, | 42 |
| abstract_inverted_index.training | 113, 254, 271, 304 |
| abstract_inverted_index.workflow | 175 |
| abstract_inverted_index.Analyzing | 194 |
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| abstract_inverted_index.analytics | 2 |
| abstract_inverted_index.collected | 238 |
| abstract_inverted_index.datasets. | 56 |
| abstract_inverted_index.encompass | 222 |
| abstract_inverted_index.extensive | 189 |
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| abstract_inverted_index.introduce | 245 |
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| abstract_inverted_index.scenarios | 132 |
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| abstract_inverted_index.synthetic | 75 |
| abstract_inverted_index.variance, | 99 |
| abstract_inverted_index.variation | 82 |
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| abstract_inverted_index.analytical | 46, 207 |
| abstract_inverted_index.augmenting | 94, 273 |
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| abstract_inverted_index.collecting | 188 |
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| abstract_inverted_index.phenotypes | 27 |
| abstract_inverted_index.predictive | 9, 294 |
| abstract_inverted_index.real-world | 85 |
| abstract_inverted_index.robustness | 156 |
| abstract_inverted_index.variations | 267 |
| abstract_inverted_index.Integration | 67 |
| abstract_inverted_index.application | 128 |
| abstract_inverted_index.challenging | 199 |
| abstract_inverted_index.comparative | 166 |
| abstract_inverted_index.demonstrate | 126 |
| abstract_inverted_index.development | 228 |
| abstract_inverted_index.encountered | 83 |
| abstract_inverted_index.fingerprint | 91, 196 |
| abstract_inverted_index.independent | 117 |
| abstract_inverted_index.information | 263 |
| abstract_inverted_index.performance | 295 |
| abstract_inverted_index.procedures, | 47 |
| abstract_inverted_index.requirement | 186 |
| abstract_inverted_index.traditional | 252 |
| abstract_inverted_index.variability | 43, 210 |
| abstract_inverted_index.vibrational | 134 |
| abstract_inverted_index.SIGNIFICANCE | 192 |
| abstract_inverted_index.applications | 86 |
| abstract_inverted_index.approach’s | 142 |
| abstract_inverted_index.case-control | 123 |
| abstract_inverted_index.compromised. | 242 |
| abstract_inverted_index.consistently | 176 |
| abstract_inverted_index.distribution | 283, 301 |
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| abstract_inverted_index.incorporates | 262 |
| abstract_inverted_index.increasingly | 3 |
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| abstract_inverted_index.personalized | 146 |
| abstract_inverted_index.spectroscopy | 135 |
| abstract_inverted_index.sufficiently | 54, 216 |
| abstract_inverted_index.variability. | 208 |
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| abstract_inverted_index.incorporating | 170 |
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5028481560 |
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| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I8204097 |
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| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.13551025 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |