Results 141 to 150 of about 48,693 (213)

Epigenetic modulation of human neurobiological disorders: Lesch-Nyhan disease as a model disorder. [PDF]

AIMS Neurosci
Nguyen KV.
europepmc   +1 more source

Deep learning captures the effect of epistasis in multifactorial diseases. [PDF]

Front Med (Lausanne)
Perelygin V, Kamelin A, Syzrantsev N, Shaheen L, Kim A, Plotnikov N, Ilinskaya A, Ilinsky V, Rakitko A, Poptsova M.   +9 more
europepmc   +1 more source

Data-driven protease engineering by DNA-recording and epistasis-aware machine learning. [PDF]

Nat Commun
Huber L, Kucera T, Höllerer S, Borgwardt K, Panke S, Jeschek M.   +5 more
europepmc   +1 more source

Genomic prediction with kinship-based multiple kernel learning produces hypothesis on the underlying inheritance mechanisms of phenotypic traits. [PDF]

Genome Biol
Raimondi D, Verplaetse N, Passemiers A, Jans DS, Cleynen I, Moreau Y.   +5 more
europepmc   +1 more source

Bayesian inference of fitness landscapes via tree-structured branching processes. [PDF]

Bioinformatics
Luo XG, Kuipers J, Rupp K, Takahashi K, Beerenwinkel N.   +4 more
europepmc   +1 more source

Compensatory Evolution Following Deleterious Episodes of GC-biased Gene Conversion in Rodents. [PDF]

Mol Biol Evol
Riffis M, Saclier N, Galtier N.
europepmc   +1 more source

Intramolecular epistasis correlates with divergence of specificity in promiscuous and bifunctional NSAR/OSBS enzymes. [PDF]

Protein Sci
Truong DP, Dharmatti R, Suriadinata D, Huddleston J, Skouby R, Owusu Addo G, Zhu M, Acharige AD, Sankari Bayana R, Davila C, Fults SC, Raushel FM, Hull KG, Romo D, Glasner ME.   +14 more
europepmc   +1 more source

Performance of deep-learning-based approaches to improve polygenic scores [PDF]

Nat Commun
Kelemen M, Xu Y, Jiang T, Zhao J, Anderson C, Wallace C, Butterworth A, Inouye M.   +7 more
europepmc   +2 more sources

Epistatic effects between two genes in the renin-angiotensin system and systolic blood pressure and coronary artery calcification [PDF]

, 2006
Bielak, Lawrence F., Boerwinkle, Eric, Cheverud, James M., Kardia, Sharon L.R., Lange, Leslie A., Peyser, Patricia A., Sheedy, Patrick F., II, Turner, Stephen T.   +7 more
core   +1 more source

MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data. [PDF]

Genome Biol
Faure AJ, Lehner B.
europepmc   +1 more source