bnRep (version 0.0.3)

poultry: poultry Bayesian Network

Description

Practical application of a Bayesian network approach to poultry epigenetics and stress.

Value

An object of class bn.fit. Refer to the documentation of bnlearn for details.

Format

A discrete Bayesian network to provide further insights into the relationships among epigenetic features and a stressful condition in chickens. The Bayesian network is learned as in the referenced paper. The vertices are:

ARHGAP26

(0,1);

BOP1

(0,1);

CANX

(0,1);

CWC25

(0,1);

DGKD

(0,1);

DMR1

(0,1);

DMR2

(0,1);

DMR5

(0,1);

DMR6

(0,1);

DMR7

(0,1);

DOCK5

(0,1);

EEPD1

(0,1);

EFR3B

(0,1);

ENS10218

(0,1);

ENS27231

(0,1);

ENS46425

(0,1);

ENS47746

(0,1);

ENS50012

(0,1);

ENS50641

(0,1);

ENS51236

(0,1);

ENS53725

(0,1);

FBN1

(0,1);

GNAO1

(0,1);

GRP141

(0,1);

LOC101750642

(0,1);

LOC770074

(0,1);

LRP5

(0,1);

MFSD4A

(0,1);

MIP

(0,1);

OCLN

(0,1);

PAPK2

(0,1);

PLXNA2

(0,1);

POP5

(0,1);

RP1_27O5_3

(0,1);

SCHIP1

(0,1);

SELENOI

(0,1);

SHISA2

(0,1);

SKOR2

(0,1);

STAT3

(0,1);

Stress

(0,1);

TPST2

(0,1);

TRMT10A

(0,1);

TTLL9

(0,1);

VGLL4

(0,1);

XRCC4

(0,1);

ZBTB48

(0,1);

ZDHHC18

(0,1);

References

Videla Rodriguez, E. A., Pertille, F., Guerrero-Bosagna, C., Mitchell, J. B., Jensen, P., & Smith, V. A. (2022). Practical application of a Bayesian network approach to poultry epigenetics and stress. BMC Bioinformatics, 23(1), 261.