Algorithms for inferring multiple microbial networks In this paper we consider the computational problem of inferring  K  microbial networks from a given sampletaxa matrix. We recently proposed a mixture model framework to address this problem, where the taxa counts in each of the  K  components is modeled using a Multivariate Poisson LogNormal (MPLN) distribution, and such that the precision matrix of the distribution associated with each mixture component defines a separate microbial network. The parameters of our mixture model (MixMPLN) were estimated in a maximum likelihood framework setting, and our optimization algorithm was based on the MinorizationMaximization (MM) principle combined with gradient ascent and block updates. Here, we extend the application of the MM framework to propose two new algorithms for inferring  K  microbial networks from a sampletaxa matrix. The first algorithm, MixGGM, applies a logratio transform to the sampletaxa count matrix and subsequently models the problem as a mixture of Multivariate Gaussian distributions. The second algorithm, MixMCMC, is based on the MixMPLN formulation but estimates a subset of the mixture parameters using a Markov Chain Monte Carlo (MCMC) sampling strategy rather than by direct optimization. We also extend these algorithms to address the inference of sparse networks. We evaluate the performances of the three methods (MixMPLN, MixMCMC, and MixGGM) on synthetic datasets generated from a comprehensive collection of graph types (band, cluster, scalefree, and random). We also applied MixMPLN and MixGGM to a real dataset. The code and datasets are available on https:github.comsahatavaMicNet.

Microbial Interaction Extraction from Biomedical Literature using MaxBiLSTM Microorganisms play a vital role in various ecosystems, but their complex interaction is still unclear. With the publication of a large number of microbial literatures, many experimentally verified microbial interaction is dispersed therein. Organizing them into a database or knowledge graph can facilitate the development of microbiology research. Text mining technology is able to automatically extract and integrate these microbial interactions, as well as discover implicit information in literatures. For this purpose, we manually annotate a Microbial Interaction Corpus (MICorpus) containing 1005 abstracts, which provide a useful data source for the MIE task. On this basis, we propose an automated MIE extraction system based on MaxBiLSTM model. The best result of the system is precision (P) 76.313%, recall (R) of 90.121%, and an F value (F) 82.476%.