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Fatemeh Behjati Ardakani

Showing results (1-10 of 15) with videos related to

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F1000Research|November 19, 2019
Predicting transcription factor binding using ensemble random forest modelsFatemeh Behjati Ardakani, Florian Schmidt, Marcel H Schulz
Bioinformatics (Oxford, England)|May 17, 2026
Predicting gene-specific regulation with transcriptomic and epigenetic single-cell dataLaura Rumpf, Fatemeh Behjati Ardakani, Dennis Hecker, et al.
Bioinformatics (Oxford, England)|January 28, 2023
The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell dataDennis Hecker, Fatemeh Behjati Ardakani, Alexander Karollus, et al.
Plos One|April 15, 2021
CpG content-dependent associations between transcription factors and histone modificationsJonas Fischer, Fatemeh Behjati Ardakani, Kathrin Kattler, et al.
Genome Biology|July 14, 2026
Comparing machine learning methods predicting transcriptome from epigenome with applications to association studiesFatemeh Behjati Ardakani, Shamim Ashrafiyan, Laura Rumpf, et al.
Proteomics|September 14, 2023
Computational tools for inferring transcription factor activityDennis Hecker, Michael Lauber, Fatemeh Behjati Ardakani, et al.
Gigascience|October 30, 2020
Prediction of single-cell gene expression for transcription factor analysisFatemeh Behjati Ardakani, Kathrin Kattler, Tobias Heinen, et al.
Briefings in Bioinformatics|November 12, 2025
Decoding heart failure subtypes with neural networks via differential explanation analysisMariano Ruz Jurado, David Rodriguez Morales, Elijah Genetzakis, et al.
Human Genomics|July 25, 2023
CVD-associated SNPs with regulatory potential reveal novel non-coding disease genesChaonan Zhu, Nina Baumgarten, Meiqian Wu, et al.
Biorxiv : the Preprint Server for Biology|March 3, 2025
Cell type-specific epigenetic regulatory circuitry of coronary artery disease lociDennis Hecker, Xiaoning Song, Nina Baumgarten, et al.
Pageof 2

Showing results (1-10 of 15) with videos related to

Sort By:
Pageof 2
F1000Research|November 19, 2019
Predicting transcription factor binding using ensemble random forest modelsFatemeh Behjati Ardakani, Florian Schmidt, Marcel H Schulz
Bioinformatics (Oxford, England)|May 17, 2026
Predicting gene-specific regulation with transcriptomic and epigenetic single-cell dataLaura Rumpf, Fatemeh Behjati Ardakani, Dennis Hecker, et al.
Bioinformatics (Oxford, England)|January 28, 2023
The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell dataDennis Hecker, Fatemeh Behjati Ardakani, Alexander Karollus, et al.
Plos One|April 15, 2021
CpG content-dependent associations between transcription factors and histone modificationsJonas Fischer, Fatemeh Behjati Ardakani, Kathrin Kattler, et al.
Genome Biology|July 14, 2026
Comparing machine learning methods predicting transcriptome from epigenome with applications to association studiesFatemeh Behjati Ardakani, Shamim Ashrafiyan, Laura Rumpf, et al.
Proteomics|September 14, 2023
Computational tools for inferring transcription factor activityDennis Hecker, Michael Lauber, Fatemeh Behjati Ardakani, et al.
Gigascience|October 30, 2020
Prediction of single-cell gene expression for transcription factor analysisFatemeh Behjati Ardakani, Kathrin Kattler, Tobias Heinen, et al.
Briefings in Bioinformatics|November 12, 2025
Decoding heart failure subtypes with neural networks via differential explanation analysisMariano Ruz Jurado, David Rodriguez Morales, Elijah Genetzakis, et al.
Human Genomics|July 25, 2023
CVD-associated SNPs with regulatory potential reveal novel non-coding disease genesChaonan Zhu, Nina Baumgarten, Meiqian Wu, et al.
Biorxiv : the Preprint Server for Biology|March 3, 2025
Cell type-specific epigenetic regulatory circuitry of coronary artery disease lociDennis Hecker, Xiaoning Song, Nina Baumgarten, et al.
Pageof 2