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Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data.
Transfac: a database on transcription factors and their dna binding sites
Edgar Wingender, Peter Dietze, Holger Karas, and Rainer Knüppel · 1996
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The tetrad project: Constraint based aids to causal model specification
Richard Scheines, Peter Spirtes, Clark Glymour, Christopher Meek, and Thomas Richardson · 1998
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Population issues in clinical trials
Zab Mosenifar · 2007
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Modelling and analysis of gene regulatory networks
Guy Karlebach and Ron Shamir · 2008
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Inferring regulatory networks from expression data using tree-based methods
Vân Anh Huynh-Thu, Alexandre Irrthum, Louis Wehenkel, and Pierre Geurts · 2010
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The encode project
Natalie de Souza · 2012
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Regnetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse
Zhi-Ping Liu, Canglin Wu, Hongyu Miao, and Hulin Wu · 2015
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Transcriptional heterogeneity and lineage commitment in myeloid progenitors
Franziska Paul, Ya’ara Arkin, Amir Giladi, Diego Adhemar Jaitin, Ephraim Kenigsberg, Hadas Keren-Shaul, Deborah Winter, David Lara-Astiaso, Meital Gury, Assaf Weiner, et al · 2015
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Diversity and inclusion in genomic research: why the uneven progress?
Amy R Bentley, Shawneequa Callier, and Charles N Rotimi · 2017
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Massively parallel digital transcriptional profiling of single cells
Grace XY Zheng, Jessica M Terry, Phillip Belgrader, Paul Ryvkin, Zachary W Bent, Ryan Wilson, Solongo B Ziraldo, Tobias D Wheeler, Geoff P McDermott, Junjie Zhu, et al · 2017
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CausalGAN: Learning causal implicit generative models with adversarial training
Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, and Sriram Vishwanath · 2018
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Lack of diversity in genomic databases is a barrier to translating precision medicine research into practice
Latrice G Landry, Nadya Ali, David R Williams, Heidi L Rehm, and Vence L Bonham · 2018
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Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
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Ancestry patterns inferred from massive rna-seq data
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A qualitative study exploring barriers and facilitators of enrolling underrepresented populations in clinical trials and biobanking
Terry C Davis, Connie L Arnold, Glenn Mills, and Lucio Miele · 2019
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Animaltfdb 3.0: a comprehensive resource for annotation and prediction of animal transcription factors
Hui Hu, Ya-Ru Miao, Long-Hao Jia, Qing-Yang Yu, Qiong Zhang, and An-Yuan Guo · 2019
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Grnboost2 and arboreto: efficient and scalable inference of gene regulatory networks
Thomas Moerman, Sara Aibar Santos, Carmen Bravo González-Blas, Jaak Simm, Yves Moreau, Jan Aerts, and Stein Aerts · 2019
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The missing diversity in human genetic studies
Giorgio Sirugo, Scott M Williams, and Sarah A Tishkoff · 2019
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Differentiable causal discovery from interventional data
Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, and Alexandre Drouin · 2020
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Gene regulatory network inference resources: A practical overview
Daniele Mercatelli, Laura Scalambra, Luca Triboli, Forest Ray, and Federico M Giorgi · 2020
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Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
Aditya Pratapa, Amogh P Jalihal, Jeffrey N Law, Aditya Bharadwaj, and TM Murali · 2020
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Boosting synthetic data generation with effective nonlinear causal discovery
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Analysis of female enrollment and participant sex by burden of disease in us clinical trials between 2000 and 2020
Jecca R Steinberg, Brandon E Turner, Brannon T Weeks, Christopher J Magnani, Bonnie O Wong, Fatima Rodriguez, Lynn M Yee, and Mark R Cullen · 2021
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Causal inference using llm-guided discovery
Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N Balasubramanian, and Amit Sharma · 2023
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Effective long-context scaling of foundation models
Wenhan Xiong, Jingyu Liu, Igor Molybog, Hejia Zhang, Prajjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, et al · 2023
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How do large language models understand genes and cells
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Perturbnet predicts single-cell responses to unseen chemical and genetic perturbations
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