Corrigendum: 'TIME': A Web Application for Obtaining Insights into Microbial Ecology Using Longitudinal Microbiome Data.

[This corrects the article DOI: 10.3389/fmicb.2018.

Corrigendum: "EviMass": A Literature Evidence-Based Miner for Human Microbial Associations.

[This corrects the article DOI: 10.3389/fgene.2019.

Correction to: NetConfer: a web application for comparative analysis of multiple biological networks.

An amendment to this paper has been published and can be accessed via the original article.

NetConfer: a web application for comparative analysis of multiple biological networks.

Background: Most biological experiments are inherently designed to compare changes or transitions of state between conditions of interest. The advancements in data intensive research have in particular elevated the need for resources and tools enabling comparative analysis of biological data. The complexity of biological systems and the interactions of their various components, such as genes, proteins, taxa, and metabolites, have been inferred, represented, and visualized via graph theory-based networks.

A Multi-Organ Nucleus Segmentation Challenge.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated.

"EviMass": A Literature Evidence-Based Miner for Human Microbial Associations.

The importance of understanding microbe-microbe as well as microbe-disease associations is one of the key thrust areas in human microbiome research. High-throughput metagenomic and transcriptomic projects have fueled discovery of a number of new microbial associations. Consequently, a plethora of information is being added routinely to biomedical literature, thereby contributing toward enhancing our knowledge on microbial associations.

'TIME': A Web Application for Obtaining Insights into Microbial Ecology Using Longitudinal Microbiome Data.

Realization of the importance of microbiome studies, coupled with the decreasing sequencing cost, has led to the exponential growth of microbiome data. A number of these microbiome studies have focused on understanding changes in the microbial community over time. Such longitudinal microbiome studies have the potential to offer unique insights pertaining to the microbial social networks as well as their responses to perturbations.