Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Mais filtros

Base de dados
Ano de publicação
Tipo de documento
País de afiliação
Intervalo de ano de publicação
1.
Proc Natl Acad Sci U S A ; 119(12): e2114271119, 2022 03 22.
Artigo em Inglês | MEDLINE | ID: mdl-35286190

RESUMO

SignificanceOur study presents the largest whole-genome investigation of leadership phenotypes to date. We identified genome-wide significant loci for leadership phenotypes, which are overlapped with top hits for bipolar disorder, schizophrenia, and intelligence. Our study demonstrated the polygenetic nature of leadership, the positive genetic correlations between leadership traits and a broad range of well-being indicators, and the unique association of leadership with well-being after accounting for genetic influences related to other socioeconomic status measures. Our findings offer insights into the biological underpinnings of leadership.


Assuntos
Estudo de Associação Genômica Ampla , Esquizofrenia , Humanos , Liderança , Herança Multifatorial , Fenótipo , Esquizofrenia/genética
2.
Brief Bioinform ; 21(4): 1437-1447, 2020 07 15.
Artigo em Inglês | MEDLINE | ID: mdl-31504150

RESUMO

Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.


Assuntos
Aprendizado Profundo , Proteínas/química , Algoritmos , Redes Neurais de Computação
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA