“Papers of School of Biological Sciences”

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191. H. Poormohammadi. , C. Eslahchi. and R. Tusserkani. ,
TripNet: A Method for Constructing Rooted Phylogenetic Networks from Triplets,
Plos One 9(2014), 1-12  [abstract]

   192. F, Dorri. , H. Mahini. , A. Sharifi-Zarchi. , M. Totonchi. , R. Tusserkani. , H. Pezeshk. and M. Sadeghi. ,
Natural Biased Coin Encoded in the Genome Determines Cell Strategy,
Plos One 9(2014), e103569  [abstract]

   193. R. Hassanzadeh. , C. Eslahchi. and W. Sung. ,
Do Triplets Have Enough Information to Construct the Multi-Labeled Phylogenetic Tree?,
Plos One DOI: 10.1371/journal.pone.0103622(2014),   [abstract]

   194. C. Eslahchi. , H.R. Maimani. , R. Torabi. and R. Tuserkani. ,
Proper nearly perfect sets in graphs,
Ars Combinatoria  (to appear) [abstract]

   195. C. Eslahchi. , H.R. Maimani. , R. Torabi. and R. Tuserkani. ,
Dynamical 2-domination in graphs,
Ars Combinatoria  (to appear) [abstract]

   196. M. Ganjali. and T. Baghfalaki. ,
A Bayesian Shared Parameter Model for Analysing Longitudinal Skewed Responses with Nonignorable Dropout,
IJSMR 3(2014), 103-115  [abstract]

   197. V. Maroufy. , P. Marriott. and H. Pezeshk. ,
An Optimization Approach To Calculating Sample Sizes For Trials With Binary Responses,
Biopharmaceutical Statistics 24(2014), 715-731  [abstract]

   198. S. N. Hassani. , M. Totonchi. , A. Sharifi-Zarchi. , S. Mollamohammadi. , M. Pakzad. , S. Moradi. , A. Samadian. , N. Masoudi. , S. Mirshahvaladi. , A. Farrokhi. , B, Greber. , M. J. Araúzo-Bravo. , D. Sabour. , M. Sadeghi. , G. Hosseini Salekdeh. , G. Gourabi. , H. R. Schöler. and H. Baharvand. ,
Inhibition of TGFβ Signaling Promotes Ground State Pluripotency,
Stem Cell Reviews and Reports 10(2014), 16-30  [abstract]

   199. M. H. Foroughmand-Araabi. , B. Goliaei. , K. Alishahi. and M. Sadeghi. ,
Dependency of codon usage on protein sequence patterns: a statistical study,
Theoretical Biology and Medical Modelling 11(2014), 2  [abstract]

   200. M. P. Naeini. , B. Moshiri. , B. N. Araabi. and M. Sadeghi. ,
Learning by abstraction: Hierarchical classification model using evidential theoretic approach and Bayesian ensemble model, Neurocomputing,
Neurocomputing 130(2014), 73-82  [abstract]

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