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Narsis Kiani (1978-)

Mathematikerin

Name	Narsis Kiani
Zeit	1978- (Lebensdaten)
Ort	Iran (Geburtsort) Stockholm (Wirkungsort) Schweden
Geschlecht	weiblich
Beruf	Mathematikerin (charakteristischer Beruf)
Variante	Narsis A. Kiani Narsis Aftab Kiani
Beziehung	Karolinska institutet (2014-) BioQuant Mathematik (Thema) Algorithmus (Thema)
Weitere Angaben	Seit 2014 am Karolinska Institut
Quelle	Internet: http://narsiskiani.net/ (Stand: 15.04.2021)
Datensatztyp	Person
GND-Nummer	1161016198
Datensatz-ID	1161016198
Beteiligt an alle anzeigen On the salient limitations of the methods of assembly theory and their classification of molecular biosignatures A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers Causal deconvolution by algorithmic generative models Time-resolved transcriptome and proteome landscape of human regulatory T cell (Treg) differentiation reveals novel regulators of FOXP3 Network modules uncover mechanisms of skeletal muscle dysfunction in COPD patients Systems Medicine: from molecular features and models to the clinic in COPD Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data Normalizing for individual cell population context in the analysis of high-content cellular screens

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Narsis Kiani (1978-)

On the salient limitations of the methods of assembly theory and their classification of molecular biosignatures

A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis

Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers

Causal deconvolution by algorithmic generative models

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

Network modules uncover mechanisms of skeletal muscle dysfunction in COPD patients

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

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

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