Nonparametric correction of estimated gene trees using TRACTION.  :0,gene trees are often inaccurate, due to insufficient phylogenetic signal in the single :0,gene alignment, among other causes. Gene tree correction aims to improve the accuracy of an estimated :0,gene tree by using computational techniques along with auxiliary information, such as a reference species tree or sequencing data. However, :0,gene trees and species trees can differ as a result of :0,gene duplication and loss (GDL), incomplete lineage sorting (ILS), and other biological processes. Thus :0,gene tree correction methods need to take estimation error as well as :0,gene tree heterogeneity into account. Many prior :0,gene tree correction methods have been developed for the case where GDL is present.

Genetic Process Mining: Alignmentbased Process Model Mutation The Evolutionary Tree Miner (ETM) is a genetic process discovery algorithm that enables the user to guide the discovery process based on preferences with respect to four process model quality dimensions: replay fitness, precision, generalization and simplicity. Traditionally, the ETM algorithm uses random creation of process models for the initial population, as well as random mutation and crossover techniques for the evolution of generations. In this paper, we present an approach that improves the performance of the ETM algorithm by enabling it to make guided changes to process models, in order to obtain higher quality models in fewer generations. The two parts of this approach are: (1) creating an initial population of process models with a reasonable quality; (2) using information from the alignment between an event log and a process model to identify quality issues in a given part of a model, and resolving those issues using guided mutation operations.