scholarly journals AMBER: Assessment of Metagenome BinnERs

2017 ◽  
Author(s):  
Fernando Meyer ◽  
Peter Hofmann ◽  
Peter Belmann ◽  
Ruben Garrido-Oter ◽  
Adrian Fritz ◽  
...  
Keyword(s):  
Microbial Community ◽  
Performance Metrics ◽  
Comparative Assessment ◽  
Critical Assessment ◽  
Data Sets ◽  
Community Members ◽  
Benchmark Data ◽  
Link Type ◽  
Genome Binning

AbstractReconstructing the genomes of microbial community members is key to the interpretation of shotgun metagenome samples. Genome binning programs deconvolute reads or assembled contigs of such samples into individual bins, but assessing their quality is difficult due to the lack of evaluation software and standardized metrics. We present AMBER, an evaluation package for the comparative assessment of genome reconstructions from metagenome benchmark data sets. It calculates the performance metrics and comparative visualizations used in the first benchmarking challenge of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). As an application, we show the outputs of AMBER for ten different binnings on two CAMI benchmark data sets. AMBER is implemented in Python and available under the Apache 2.0 license on GitHub (https://github.com/CAMI-challenge/AMBER).

scholarly journals AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection

2018 ◽  
Vol 8 (12) ◽  
pp. 2421 ◽  
Author(s):  
Chongya Song ◽  
Alexander Pons ◽  
Kang Yen
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Performance Metrics ◽  
Hidden Markov ◽  
Data Sets ◽  
Learning Abilities ◽  
Benchmark Data ◽  
Malicious Behavior ◽  
Network Intrusion ◽  
The Common

In the field of network intrusion, malware usually evades anomaly detection by disguising malicious behavior as legitimate access. Therefore, detecting these attacks from network traffic has become a challenge in this an adversarial setting. In this paper, an enhanced Hidden Markov Model, called the Anti-Adversarial Hidden Markov Model (AA-HMM), is proposed to effectively detect evasion pattern, using the Dynamic Window and Threshold techniques to achieve adaptive, anti-adversarial, and online-learning abilities. In addition, a concept called Pattern Entropy is defined and acts as the foundation of AA-HMM. We evaluate the effectiveness of our approach employing two well-known benchmark data sets, NSL-KDD and CTU-13, in terms of the common performance metrics and the algorithm’s adaptation and anti-adversary abilities.

scholarly journals HgtSIM: a simulator for horizontal gene transfer (HGT) in microbial communities

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4015 ◽  
Author(s):  
Weizhi Song ◽  
Kerrin Steensen ◽  
Torsten Thomas
Keyword(s):  
Microbial Community ◽  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Microbial Communities ◽  
Metagenomic Data ◽  
Community Members ◽  
Data Simulation ◽  
Simulation Tools ◽  
Link Type

The development and application of metagenomic approaches have provided an opportunity to study and define horizontal gene transfer (HGT) on the level of microbial communities. However, no current metagenomic data simulation tools offers the option to introduce defined HGT within a microbial community. Here, we present HgtSIM, a pipeline to simulate HGT event among microbial community members with user-defined mutation levels. It was developed for testing and benchmarking pipelines for recovering HGTs from complex microbial datasets. HgtSIM is implemented in Python3 and is freely available at: https://github.com/songweizhi/HgtSIM.

Non-Covalent Interactions Atlas Benchmark Data Sets 3: Repulsive Contacts

2021 ◽  
Vol 17 (3) ◽  
pp. 1548-1561
Author(s):  
Kristian Kříž ◽  
Martin Nováček ◽  
Jan Řezáč
Keyword(s):  
Data Sets ◽  
Benchmark Data ◽  
Non Covalent Interactions ◽  
Covalent Interactions

Bayesian Trigonometric Support Vector Classifier

2003 ◽  
Vol 15 (9) ◽  
pp. 2227-2254 ◽  
Author(s):  
Wei Chu ◽  
S. Sathiya Keerthi ◽  
Chong Jin Ong
Keyword(s):  
Loss Function ◽  
Gaussian Processes ◽  
Likelihood Function ◽  
Support Vector ◽  
Data Sets ◽  
Model Adaptation ◽  
Bayesian Techniques ◽  
Benchmark Data ◽  
Support Vector Classifier ◽  
Set Up

This letter describes Bayesian techniques for support vector classification. In particular, we propose a novel differentiable loss function, called the trigonometric loss function, which has the desirable characteristic of natural normalization in the likelihood function, and then follow standard gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implement model adaptation, while keeping the merits of support vector classifier, such as sparseness and convex programming. This differs from standard gaussian processes for classification. Moreover, we put forward class probability in making predictions. Experimental results on benchmark data sets indicate the usefulness of this approach.

Deep Learning for Biometric Face Recognition: Experimental Study on Benchmark Data Sets

2020 ◽  
pp. 71-97
Author(s):  
Natalya Selitskaya ◽  
S. Sielicki ◽  
L. Jakaite ◽  
V. Schetinin ◽  
F. Evans ◽  
...  
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Face Recognition ◽  
Data Sets ◽  
Benchmark Data

scholarly journals Optimizing viral genome subsampling by genetic diversity and temporal distribution (TARDiS) for Phylogenetics

2021 ◽  
Author(s):  
Simone Marini ◽  
Carla Mavian ◽  
Alberto Riva ◽  
Marco Salemi ◽  
Brittany Rife Magalis
Keyword(s):  
Genetic Algorithm ◽  
Genetic Diversity ◽  
Viral Genome ◽  
Temporal Distribution ◽  
Data Sets ◽  
Link Type

AbstractTARDiS for Philogenetics is a novel tool for optimal genetic sub-sampling. It optimizes both genetic diversity and temporal distribution through a genetic algorithm. TARDiS, along with example data sets and a user manual, is available at https://github.com/smarini/tardis-phylogenetics

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