Multi-task learning improves ancestral state reconstruction

被引：6

作者：

Lam Si Tung Ho ^{[1
]}

Vu Dinh ^{[2
]}

Nguyen, Cuong, V ^{[3
]}

机构：

[1] Dalhousie Univ, Dept Math & Stat, Halifax, NS, Canada

[2] Univ Delaware, Dept Math Sci, Newark, DE 19716 USA

[3] Univ Cambridge, Dept Engn, Cambridge, England

来源：

THEORETICAL POPULATION BIOLOGY | 2019年 / 126卷

基金：

加拿大自然科学与工程研究理事会; 英国工程与自然科学研究理事会;

关键词：

Ancestral state reconstruction; Multi-task learning; Maximum likelihood estimator; CONTINUOUS CHARACTERS; LIKELIHOOD; EVOLUTION;

D O I：

10.1016/j.tpb.2019.01.001

中图分类号：

Q14 [生态学（生物生态学）];

学科分类号：

071012 ; 0713 ;

摘要：

We consider the ancestral state reconstruction problem where we need to infer phenotypes of ancestors using observations from present-day species. For this problem, we propose a multi-task learning method that uses regularized maximum likelihood to estimate the ancestral states of various traits simultaneously. We then show both theoretically and by simulation that this method improves the estimates of the ancestral states compared to the maximum likelihood method. The result also indicates that for the problem of ancestral state reconstruction under the Brownian motion model, the maximum likelihood method can be improved. (C) 2019 Elsevier Inc. All rights reserved.

引用

页码：33 / 39

页数：7

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