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Items: 18

1.

Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity.

Hernández-Orozco S, Kiani NA, Zenil H.

R Soc Open Sci. 2018 Aug 29;5(8):180399. doi: 10.1098/rsos.180399. eCollection 2018 Aug.

2.

Predictive Systems Toxicology.

Kiani NA, Shang MM, Zenil H, Tegner J.

Methods Mol Biol. 2018;1800:535-557. doi: 10.1007/978-1-4939-7899-1_25.

PMID:
29934910
3.

Time-resolved transcriptome and proteome landscape of human regulatory T cell (Treg) differentiation reveals novel regulators of FOXP3.

Schmidt A, Marabita F, Kiani NA, Gross CC, Johansson HJ, Éliás S, Rautio S, Eriksson M, Fernandes SJ, Silberberg G, Ullah U, Bhatia U, Lähdesmäki H, Lehtiö J, Gomez-Cabrero D, Wiendl H, Lahesmaa R, Tegnér J.

BMC Biol. 2018 May 7;16(1):47. doi: 10.1186/s12915-018-0518-3.

4.

Low-algorithmic-complexity entropy-deceiving graphs.

Zenil H, Kiani NA, Tegnér J.

Phys Rev E. 2017 Jul;96(1-1):012308. doi: 10.1103/PhysRevE.96.012308. Epub 2017 Jul 7.

PMID:
29347130
5.

Dynamics and heterogeneity of brain damage in multiple sclerosis.

Kotelnikova E, Kiani NA, Abad E, Martinez-Lapiscina EH, Andorra M, Zubizarreta I, Pulido-Valdeolivas I, Pertsovskaya I, Alexopoulos LG, Olsson T, Martin R, Paul F, Tegnér J, Garcia-Ojalvo J, Villoslada P.

PLoS Comput Biol. 2017 Oct 26;13(10):e1005757. doi: 10.1371/journal.pcbi.1005757. eCollection 2017 Oct.

6.

HiDi: an efficient reverse engineering schema for large-scale dynamic regulatory network reconstruction using adaptive differentiation.

Deng Y, Zenil H, Tegnér J, Kiani NA.

Bioinformatics. 2017 Dec 15;33(24):3964-3972. doi: 10.1093/bioinformatics/btx501.

PMID:
28961895
7.

A minimal unified model of disease trajectories captures hallmarks of multiple sclerosis.

Kannan V, Kiani NA, Piehl F, Tegner J.

Math Biosci. 2017 Jul;289:1-8. doi: 10.1016/j.mbs.2017.03.006. Epub 2017 Mar 29.

8.

Corrigendum to "Methods of information theory and algorithmic complexity for network biology" [Semin. Cell Dev. Biol. 51 (2016) 32-43].

Zenil H, Kiani NA, Tegnér J.

Semin Cell Dev Biol. 2016 Dec;60:168. doi: 10.1016/j.semcdb.2016.11.006. No abstract available.

PMID:
27979328
9.

A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference.

Tegnér J, Zenil H, Kiani NA, Ball G, Gomez-Cabrero D.

Philos Trans A Math Phys Eng Sci. 2016 Nov 13;374(2080). pii: 20160144. Review.

10.

Systems Toxicology: Systematic Approach to Predict Toxicity.

Kiani NA, Shang MM, Tegner J.

Curr Pharm Des. 2016;22(46):6911-6917. doi: 10.2174/1381612822666161003115629. Review.

PMID:
27697024
11.

Conditional Disease Development extracted from Longitudinal Health Care Cohort Data using Layered Network Construction.

Kannan V, Swartz F, Kiani NA, Silberberg G, Tsipras G, Gomez-Cabrero D, Alexanderson K, Tegnèr J.

Sci Rep. 2016 May 23;6:26170. doi: 10.1038/srep26170.

12.

Evaluating network inference methods in terms of their ability to preserve the topology and complexity of genetic networks.

Kiani NA, Zenil H, Olczak J, Tegnér J.

Semin Cell Dev Biol. 2016 Mar;51:44-52. doi: 10.1016/j.semcdb.2016.01.012. Epub 2016 Feb 3. Review.

PMID:
26851626
13.

Methods of information theory and algorithmic complexity for network biology.

Zenil H, Kiani NA, Tegnér J.

Semin Cell Dev Biol. 2016 Mar;51:32-43. doi: 10.1016/j.semcdb.2016.01.011. Epub 2016 Jan 21. Review. Erratum in: Semin Cell Dev Biol. 2016 Dec;60:168.

PMID:
26802516
14.

Systems Medicine: from molecular features and models to the clinic in COPD.

Gomez-Cabrero D, Menche J, Cano I, Abugessaisa I, Huertas-Migueláñez M, Tenyi A, Marin de Mas I, Kiani NA, Marabita F, Falciani F, Burrowes K, Maier D, Wagner P, Selivanov V, Cascante M, Roca J, Barabási AL, Tegnér J.

J Transl Med. 2014 Nov 28;12 Suppl 2:S4. doi: 10.1186/1479-5876-12-S2-S4. Epub 2014 Nov 28.

15.

Signaling networks in MS: a systems-based approach to developing new pharmacological therapies.

Kotelnikova E, Bernardo-Faura M, Silberberg G, Kiani NA, Messinis D, Melas IN, Artigas L, Schwartz E, Mazo I, Masso M, Alexopoulos LG, Mas JM, Olsson T, Tegner J, Martin R, Zamora A, Paul F, Saez-Rodriguez J, Villoslada P.

Mult Scler. 2015 Feb;21(2):138-46. doi: 10.1177/1352458514543339. Epub 2014 Aug 11. Review.

16.

Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data.

Kiani NA, Kaderali L.

BMC Bioinformatics. 2014 Jul 22;15:250. doi: 10.1186/1471-2105-15-250.

17.

High-throughput RNA interference screens integrative analysis: Towards a comprehensive understanding of the virus-host interplay.

Amberkar S, Kiani NA, Bartenschlager R, Alvisi G, Kaderali L.

World J Virol. 2013 May 12;2(2):18-31. doi: 10.5501/wjv.v2.i2.18. Review.

18.

Normalizing for individual cell population context in the analysis of high-content cellular screens.

Knapp B, Rebhan I, Kumar A, Matula P, Kiani NA, Binder M, Erfle H, Rohr K, Eils R, Bartenschlager R, Kaderali L.

BMC Bioinformatics. 2011 Dec 20;12:485. doi: 10.1186/1471-2105-12-485.

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