Dr. Alireza Fotuhi Siahpirani

 

                         

    Alireza Fotuhi Siahpirani, Ph.D.
     Assistant Professor
     E-mail: a.fotuhi@ut.ac.ir 

  

 

                                                                                                                                                                                        

CV

 

Personal Records                                                

 

Education

 
 
Last name: Fotuhi Siahpirani
First name: Alireza
Date of birth:
 
Position: Assistant Professor of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran.   
 
Contact Info
Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
P.O.Box: 13145-1384.
 
E-mail:       a.fotuhi@ut.ac.ir
Web page:  ibb.ut.ac.ir/fotuhi
 
Office:       Tel: 
                 Fax: (+98)-21-66404680 
 
Ph.D. in Computer Sciences, UW-Madison, Madison, WI, USA (2011-2019)
 
M.Sc. in Computer Science, University of Tehran, Tehran, Iran. (2006-2009)
 
B.Sc. in Computer Science, University of Tehran, Tehran, Iran. (2001-2006)
 

 

Research Interests

• Bioinformatics & Computational Biology

• Machine Learning

 

Publications

Siahpirani AF, Knaack S, Chasman D, et al. (2022)
"Dynamic regulatory module networks for inference of cell type-specific transcriptional networks."
Genome Res.
https://doi.org/10.1101/gr.276542.121

Huang K, Wu Y, Shin J, et al. (2021)
"Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder."
PLOS Genetics
https://doi.org/10.1371/journal.pgen.1009309

Sinha D, Steyer B, Shahi P, et al. (2020)
"Human iPSC Modeling Reveals Mutation-Specific Responses to Gene Therapy in a Genotypically Diverse Dominant Maculopathy."
The American Journal of Human Genetics
https://doi.org/10.1016/j.ajhg.2020.06.011

Wheeler HE, Ploch S, Barbeira AN, et al. (2019)
"Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits."
Genet. Epidemiol
https://doi.org/10.1002/gepi.22205

Chasman D, Iyer N, Siahpirani AF, et al. (2019)
"Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development."
Cell Systems
https://doi.org/10.1016/j.cels.2019.05.012

Tran KA, Pietrzak SJ, Zaidan NZ, et al. (2019)
"Defining Reprogramming Checkpoints from Single-Cell Analyses of Induced Pluripotency."
Cell Reports
https://doi.org/10.1016/j.celrep.2019.04.056

Siahpirani AF, Roy S, (2017) "A prior-based integrative framework for functional transcriptional regulatory network inference." Nucleic Acids Research
https://doi.org/10.1093/nar/gkw963

Siahpirani AF, Ay F, Roy S. (2016)
"A multi-task graph-clustering approach for chromosome conformation capture data sets identifies conserved modules of chromosomal interactions."
Genome Biol https://doi.org/10.1186/s13059-016-0962-8

Marx, H., Minogue, C., Jayaraman, D. et al. (2016)
"A proteomic atlas of the legume Medicago truncatula and its nitrogen-fixing endosymbiont Sinorhizobium meliloti."
Nat Biotechnol
https://doi.org/10.1038/nbt.3681

Chasman D, Siahpirani AF, Roy S, (2016)
"Network-based approaches for analysis of complex biological systems."
Current Opinion in Biotechnology
https://doi.org/10.1016/j.copbio.2016.04.007

Roy S, Siahpirani AF, Chasman D, et al. (2015)
"A predictive modeling approach for cell line-specific long-range regulatory interactions."
Nucleic Acids Research
https://doi.org/10.1093/nar/gkv865

Larrainzar E, Riely BK, Kim SC, et al. (2015)
"Deep Sequencing of the Medicago truncatula Root Transcriptome Reveals a Massive and Early Interaction between Nodulation Factor and Ethylene Signals."
Plant Physiology
https://doi.org/10.1104/pp.15.00350

Dittenhafer-Reed KE, Richards AL, Fan J, et al. (2015)
"SIRT3 Mediates Multi-Tissue Coupling for Metabolic Fuel Switching."
Cell Metabolism
https://doi.org/10.1016/j.cmet.2015.03.007

 

Course Titles 

• Algorithms in Bioinformatics

• Machine Learning

• Discrete Mathematics