The most effective active substances for subfertility treatment.
<p>The most effective active substances for subfertility treatment.</p>
שמור ב:
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2025
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| _version_ | 1849927629679886336 |
|---|---|
| author | Uğur Ejder (22683228) |
| author2 | Pınar Uskaner Hepsağ (22683231) |
| author2_role | author |
| author_facet | Uğur Ejder (22683228) Pınar Uskaner Hepsağ (22683231) |
| author_role | author |
| dc.creator.none.fl_str_mv | Uğur Ejder (22683228) Pınar Uskaner Hepsağ (22683231) |
| dc.date.none.fl_str_mv | 2025-11-25T18:24:02Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0336846.g007 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_most_effective_active_substances_for_subfertility_treatment_/30713271 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors |
| dc.title.none.fl_str_mv | The most effective active substances for subfertility treatment. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The most effective active substances for subfertility treatment.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_98e707d76d6609261cdaa6427ee4f799 |
| identifier_str_mv | 10.1371/journal.pone.0336846.g007 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30713271 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The most effective active substances for subfertility treatment.Uğur Ejder (22683228)Pınar Uskaner Hepsağ (22683231)BiotechnologyEcologyCancerBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsmall sample sizelr &# 8211evaluated using 5assisted reproductive technologiesartificial bee colonyart ), yetaddress class imbalancesupport vector machinepharmaceutical supplement useenhance predictive performanceabc hybrids outperformedabc hybrid counterpartslocal interpretable modelabc hybrid modelmodel performancesupplement variablesproducing interpretabledietician supportvitro fertilizationsynthetic minoritystudies relysampling techniqueretrospective datasetregression treerandom forestobserved improvementsnearest neighborslimited optimizationinfluential featuresindividual predictionsimproving predictionimplemented alongsidefuture studiesfour algorithmsfolic acidfold crossexploratory ratherdietary dataconventional algorithmsconcept studyclinically directivebinary representationbaseline modelsalgorithm modelsagnostic explanationsaccuracy ).21 predictors<p>The most effective active substances for subfertility treatment.</p>2025-11-25T18:24:02ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0336846.g007https://figshare.com/articles/figure/The_most_effective_active_substances_for_subfertility_treatment_/30713271CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307132712025-11-25T18:24:02Z |
| spellingShingle | The most effective active substances for subfertility treatment. Uğur Ejder (22683228) Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors |
| status_str | publishedVersion |
| title | The most effective active substances for subfertility treatment. |
| title_full | The most effective active substances for subfertility treatment. |
| title_fullStr | The most effective active substances for subfertility treatment. |
| title_full_unstemmed | The most effective active substances for subfertility treatment. |
| title_short | The most effective active substances for subfertility treatment. |
| title_sort | The most effective active substances for subfertility treatment. |
| topic | Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors |