The potential of deep learning on the discovery of new genes implicated in differences of sex development.
Saved in:
| Title: | The potential of deep learning on the discovery of new genes implicated in differences of sex development. |
|---|---|
| Authors: | von der Decken I; Endocrinology division, Section of Medicine, University of Fribourg, Fribourg, Switzerland., Azimi H; Endocrinology division, Section of Medicine, University of Fribourg, Fribourg, Switzerland., Lauber-Biason A; Endocrinology division, Section of Medicine, University of Fribourg, Fribourg, Switzerland. |
| Source: | Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2025 Dec 29; Vol. 31, pp. 221-234. Date of Electronic Publication: 2025 Dec 29 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology Country of Publication: Netherlands NLM ID: 101585369 Publication Model: eCollection Cited Medium: Print ISSN: 2001-0370 (Print) Linking ISSN: 20010370 NLM ISO Abbreviation: Comput Struct Biotechnol J Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 41550136 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: The potential of deep learning on the discovery of new genes implicated in differences of sex development. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22von+der+Decken+I%22">von der Decken I</searchLink>; Endocrinology division, Section of Medicine, University of Fribourg, Fribourg, Switzerland.<br /><searchLink fieldCode="AU" term="%22Azimi+H%22">Azimi H</searchLink>; Endocrinology division, Section of Medicine, University of Fribourg, Fribourg, Switzerland.<br /><searchLink fieldCode="AU" term="%22Lauber-Biason+A%22">Lauber-Biason A</searchLink>; Endocrinology division, Section of Medicine, University of Fribourg, Fribourg, Switzerland. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101585369%22">Computational and structural biotechnology journal</searchLink> [Comput Struct Biotechnol J] 2025 Dec 29; Vol. 31, pp. 221-234. <i>Date of Electronic Publication: </i>2025 Dec 29 (<i>Print Publication: </i>2026). – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier+B%2EV%2E+on+behalf+of+Research+Network+of+Computational+and+Structural+Biotechnology%22">Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology </searchLink><i>Country of Publication: </i>Netherlands <i>NLM ID: </i>101585369 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Print <i>ISSN: </i>2001-0370 (Print) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220010370%22">20010370 </searchLink><i>NLM ISO Abbreviation: </i>Comput Struct Biotechnol J <i>Subsets: </i>PubMed not MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=41550136 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.csbj.2025.12.019 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 221 Titles: – TitleFull: The potential of deep learning on the discovery of new genes implicated in differences of sex development. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: von der Decken I – PersonEntity: Name: NameFull: Azimi H – PersonEntity: Name: NameFull: Lauber-Biason A IsPartOfRelationships: – BibEntity: Dates: – D: 29 M: 12 Text: 2025 Dec 29 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 2001-0370 Numbering: – Type: volume Value: 31 Titles: – TitleFull: Computational and structural biotechnology journal Type: main |
| ResultId | 1 |