M
Marcus W. Feldman
Researcher at Stanford University
Publications - 658
Citations - 57446
Marcus W. Feldman is an academic researcher from Stanford University. The author has contributed to research in topics: Population & Niche construction. The author has an hindex of 97, co-authored 638 publications receiving 52656 citations. Previous affiliations of Marcus W. Feldman include Philippine Institute for Development Studies & Xi'an Jiaotong University.
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Journal ArticleDOI
Genomic microsatellites identify shared Jewish ancestry intermediate between Middle Eastern and European populations
Naama M. Kopelman,Lewi Stone,Chaolong Wang,Dov Gefel,Marcus W. Feldman,Jossi Hillel,Noah A. Rosenberg +6 more
TL;DR: The view that the Jewish populations largely share a common Middle Eastern ancestry and that over their history they have undergone varying degrees of admixture with non-Jewish populations of European descent is supported.
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Migration versus mutation in the evolution of recombination under multilocus selection.
TL;DR: Modifier theory is used to compare the evolution of recombination under mutation-selection and migration-selection balance models and shows that the success of a new modifier depends on the sign and amount of epistasis as well as on the linkage of the modifier locus to the loci under selection.
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Aspects of variance and covariance analysis with cultural inheritance
TL;DR: The nature of equilibrium assumptions under various modes of assortative mating are discussed with particular emphasis on expected correlations between relatives with the possibility of decomposing these in terms of correlations involving adoptive families.
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Evolution of cultural communication systems: the coevolution of cultural signals and genes encoding learning preferences
TL;DR: It is argued that examining how restrictive genetic predispositions are is a useful way of examining the evolutionary origin and maintenance of learning.
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A genetic algorithm with local search strategy for improved detection of community structure
TL;DR: The results of community detection for some classic networks indicate that the proposed genetic algorithm achieves better community structure than other methodologies based on modularity optimization, such as the algorithms based on betweenness analysis, simulated annealing, or Tasgin and Bingol's genetic algorithm.