SCHOOL OF BIOLOGICAL SCIENCES

NCBI

E-Services

Papers

All Papers
	Pages: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
61.	A. Pariz, I. Fischer, A. Valizadeh and C. Mirasso, Transmission delays and frequency detuning can regulate information flow between brain regions, PLOS Computational Biology (to appear) [abstract]

62.	M. Habibi, G. Taheri and R. Aghdam, A SARS-CoV-2 (COVID-19) biological network to find targets for drug repurposing, Scientific Reports 11(2021), 1-15 [abstract] DOI: https://doi.org/10.1038/s41598-021-88427-w ArXiV: https://www.nature.com/articles/s41598-021-88427-w

63.	A. R. Alizad-Rahvar, S. Vafadar, M. Totonchi and M. Sadeghi, False Negative Mitigation in Group Testing for COVID-19 Screening, frontiers in medicine 8(2021), 579 [abstract] DOI: 10.3389/fmed.2021.661277 ArXiV: https://www.frontiersin.org/articles/10.3389/fmed.2021.661277/full

64.	N. Rohani and C. Eslahchi, Classifying Breast Cancer Molecular Subtypes using Deep Clustering Approach, Frontiers in Genetics (to appear) [abstract] DOI: 10.21203/rs.2.19530/v1

65.	N. Rohani, F. Ahmadi Moughari and C. Eslahchi, DisCoVering potential candidates of RNAi-based therapy for COVID-19 using computational methods, PeerJ 10.7717/peerj.10505(2021), [abstract] DOI: 10.7717/peerj.10505

66.	S. H. Mahmoodi, R. Aghdam and C. Eslahchi, An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests, Scientific Reports 11(2021), 1-15 [abstract] DOI: https://doi.org/10.1038/s41598-021-87074-5

67.	A. Emdadi and C. Eslahchi, Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model, BMC Bioinformatics (2021), 1-22 [abstract]

68.	F. Mohseni-Salehi, F. Zare-Mirakabad, M. Sadeghi and S. Ghafouri-Fard, A Stochastic Model of DNA Double-Strand Breaks Repair Throughout the Cell Cycle, Bulletin of Mathematical Biology 82(2021), 1-36 [abstract] DOI: https://doi.org/10.1007/s11538-019-00692-z

69.	S. salmanian, H. Pezeshk and M. Sadeghi, Inter protein residue covariation information unravels physically interacting protein dimers, BMC Bioinformatics 21(2020), 1-21 https://doi.org/10.1186/s12859-020-03930-7 [abstract] DOI: https://doi.org/10.1186/s12859-020-03930-7

70.	M. Ghamghami, N. Ghahreman, P. Irannejad and H. Pezeshk, A parametric empirical Bayes (PEB) approach for estimating maize progress percentage at field scale, Agricultural and Forest Meteorology 281(2020), https://doi.org/10.1016/j.agrformet.2019.107829 [abstract] DOI: https://doi.org/10.1016/j.agrformet.2019.107829

back to top
	Pages: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28