about
forestSV: structural variant discovery through statistical learningcerebroViz: an R package for anatomical visualization of spatiotemporal brain data.Data-driven assessment of eQTL mapping methods.Integrative analysis of low- and high-resolution eQTLDifferential relationship of DNA replication timing to different forms of human mutation and variation.Teamwork: improved eQTL mapping using combinations of machine learning methods.A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis.Detection and interpretation of expression quantitative trait loci (eQTL).Common Genetic Variants in FOXP2 Are Not Associated with Individual Differences in Language DevelopmentAdaptation of the targeted capture Methyl-Seq platform for the mouse genome identifies novel tissue-specific DNA methylation patterns of genes involved in neurodevelopmentWhole-genome sequencing in autism identifies hot spots for de novo germline mutation.SLINGER: large-scale learning for predicting gene expressionHigh frequencies of de novo CNVs in bipolar disorder and schizophrenia.Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism.Novel and ultra-rare damaging variants in neuropeptide signaling are associated with disordered eating behaviorsGenetic Approaches to Understanding Psychiatric Disease.Neuronal PAS Domain Proteins 1 and 3 Are Master Regulators of Neuropsychiatric Risk Genes.Hotspots of missense mutation identify neurodevelopmental disorder genes and functional domains.Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study.PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction.SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids.Forecasting risk gene discovery in autism with machine learning and genome-scale dataExome sequencing of 457 autism families recruited online provides evidence for autism risk genes
P50
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P50
description
researcher ORCID ID = 0000-0001-9713-0992
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wetenschapper
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name
Jacob J Michaelson
@ast
Jacob J Michaelson
@en
Jacob J Michaelson
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Jacob J Michaelson
@nl
type
label
Jacob J Michaelson
@ast
Jacob J Michaelson
@en
Jacob J Michaelson
@es
Jacob J Michaelson
@nl
altLabel
Jacob Michaelson
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prefLabel
Jacob J Michaelson
@ast
Jacob J Michaelson
@en
Jacob J Michaelson
@es
Jacob J Michaelson
@nl
P106
P1153
35076418600
P21
P31
P496
0000-0001-9713-0992