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Academic Journal

CSGDN: contrastive signed graph diffusion network for predicting crop gene–phenotype associations.

Authors : Pan, Yiru¹ (AUTHOR); Ji, Xingyu¹ (AUTHOR); You, Jiaqi¹ (AUTHOR)

Subjects: GRAPH neural networks; EMMER wheat; BIPARTITE graphs

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-16. 16p.

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Academic Journal

Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.

Authors : Zhai, Weiqi¹ (AUTHOR); Huang, Xiaodi² (AUTHOR); Shen, Nan³ (AUTHOR)

Subjects: HUMAN phenotype; RARE diseases; GENES

Source: Briefings in Bioinformatics. Jul2023, Vol. 24 Issue 4, p1-10. 10p.

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Academic Journal

scPAS: single-cell phenotype-associated subpopulation identifier.

Authors : Xie, Aimin¹ (AUTHOR); Wang, Hao¹ (AUTHOR); Zhao, Jiaxu¹ (AUTHOR)

Subjects: TRANSCRIPTOMES; DATA integration; PHENOTYPES

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-12. 12p.

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Academic Journal

Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.

Authors : Guo, Yang¹ (AUTHOR) ; Xiao, Zhiqiang¹ (AUTHOR)

Subjects: *TIME series analysis; GENE expression; GENETIC transcription regulation

Source: Briefings in Bioinformatics. Nov2024, Vol. 25 Issue 6, p1-12. 12p.

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Academic Journal

Learning genotype–phenotype associations from gaps in multi-species sequence alignments.

Authors : Islam, Uwaise Ibna¹ (AUTHOR); Santos, Andre Luiz Campelo dos¹ (AUTHOR); Kanjilal, Ria¹ (AUTHOR)

Subjects: VITAMIN C; PHENOTYPES; SEQUENCE alignment

Source: Briefings in Bioinformatics. Jan2025, Vol. 26 Issue 1, p1-11. 11p.

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Academic Journal

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.

Authors : Yuan, Xiao^1,2,3 (AUTHOR); Wang, Jing¹ (AUTHOR); Dai, Bing¹ (AUTHOR)

Subjects: COMPUTER-aided diagnosis; HUMAN phenotype; GENES

Source: Briefings in Bioinformatics. Mar2022, Vol. 23 Issue 2, p1-12. 12p.

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Academic Journal

Molecular group and correlation guided structural learning for multi-phenotype prediction.

Authors : Zhou, Xueping¹ (AUTHOR); Cai, Manqi¹ (AUTHOR); Yue, Molin¹ (AUTHOR)

Subjects: SUPERVISED learning; FEATURE selection; GENE expression profiling

Source: Briefings in Bioinformatics. Nov2024, Vol. 25 Issue 6, p1-10. 10p.

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Academic Journal

BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data.

Authors : Zhang, Shunjie¹ (AUTHOR); Li, Pan² (AUTHOR); Wang, Shenghan² (AUTHOR)

Subjects: MACHINE learning; INDEPENDENT variables; DNA methylation

Source: Briefings in Bioinformatics. Sep2024, Vol. 25 Issue 5, p1-13. 13p.

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Academic Journal

Open tools for quantitative anonymization of tabular phenotype data: literature review.

Authors : Haber, Anna C¹ (AUTHOR); Sax, Ulrich^2,3 (AUTHOR); Prasser, Fabian¹ (AUTHOR)

Subjects: PHENOTYPES; MOLECULAR biology; LIBRARY software

Source: Briefings in Bioinformatics. Nov2022, Vol. 23 Issue 6, p1-10. 10p.

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Academic Journal

Applying a Rasch model to the Broad Autism Phenotype Questionnaire: item severity analysis and differential test functioning of the English and Spanish versions.

Authors : Godoy-Giménez, Marta¹ (AUTHOR); González-Rodríguez, Antonio¹ (AUTHOR); Estévez, Angeles F.^1,2 (AUTHOR)

Subjects: RASCH models; ENGLISH language; CHILDREN with autism spectrum disorders

Source: Current Psychology. Jul2023, Vol. 42 Issue 21, p17700-17714. 15p.

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Your Filters

CSGDN: contrastive signed graph diffusion network for predicting crop gene–phenotype associations.

Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.

scPAS: single-cell phenotype-associated subpopulation identifier.

Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.

Learning genotype–phenotype associations from gaps in multi-species sequence alignments.

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.

Molecular group and correlation guided structural learning for multi-phenotype prediction.

BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data.

Open tools for quantitative anonymization of tabular phenotype data: literature review.

Applying a Rasch model to the Broad Autism Phenotype Questionnaire: item severity analysis and differential test functioning of the English and Spanish versions.

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