PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks From Time Series Data

Yukun Tan; Fernando B. Lima Neto; Ulisses Braga Neto

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PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks From Time Series Data

机译：PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks From Time Series Data

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We present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. PALLAS is able to scale to networks of realistic size under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs more accurately than other methods, while being capable of working directly on gene expression data, without need of ad-hoc binarization. PALLAS is a fully-fledged program, written in python, and available on GitHub (https://github.com/yukuntan92/PALLAS).

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《IEEE/ACM transactions on computational biology and bioinformatics》 |2022年第3期|1807-1816|共10页
作者
Yukun Tan; Fernando B. Lima Neto; Ulisses Braga Neto;
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作者单位

Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA;

Department of Computer Engineering, Polytechnic University of Pernambuco, Recife, Brazil;

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原文格式 PDF
正文语种英语
中图分类生物信息论;
关键词
Particle swarm optimization; Data models; Biological system modeling; Gene expression; Time series analysis; Dynamical systems; Computational modeling;

PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks From Time Series Data

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