Benchmarking biology
What is benchmarking in bioinformatics?
Benchmarking exercises are important to assess the performance, and reliability of the available bioinformatics tools which have different complexity in design and function.
The purpose of a benchmarking exercise is to assess the ability of the bioinformatics tool to provide reliable analysis of AMR gene content..
What is benchmarking in biology?
A benchmarking study consists of a robust and comprehensive evaluation of the capabilities of existing algorithms to solve a particular computational biology problem.Mar 27, 2019.
What is benchmarking in science?
Definition.
Benchmarking means evaluating or checking something by comparison with a standard.
Etymologically, it derives from the term benchmark, a surveyor's mark used as a reference point in measuring altitudes..
What is genetic benchmarking?
Gene prioritization is a critical step in translating genetic discoveries into biological insights, so many methods for gene prioritization have been developed.
However, it is not straightforward to compare, or “benchmark,” the performance of these methods and assess which of them produces the most accurate results..
- Benchmarking exercises are important to assess the performance, and reliability of the available bioinformatics tools which have different complexity in design and function.
The purpose of a benchmarking exercise is to assess the ability of the bioinformatics tool to provide reliable analysis of AMR gene content. - Why is benchmarking important? Benchmarking provides a way to contextualize results, helping brands understand how and where they need to make changes to a current product to improve that product's performance against competitors in their industry.
Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine
In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for
A benchmarking study consists of a robust and comprehensive evaluation of the capabilities of existing algorithms to solve a particular computational biology problem.
Can a benchmarking study identify a single winner?
A benchmarking study can rarely identify a single winner according to all evaluation metrics.
Instead, a valid outcome may include:
identifying multiple methods with excellent performance under different evaluation criteria 37. Do benchmarking studies provide accurate and unbiased results?
However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results.
Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.
Why should you use a genomics benchmark?
Especially in genomics, with its wide tool-range, users save time when a benchmark includes ,metrics such as:
accuracy running speed ease of use or deployment reproducibility and stability says Caicedo.
Method for simulating ion transport
Biology Monte Carlo methods (BioMOCA) have been developed at the University of Illinois at Urbana-Champaign to simulate ion transport in an electrolyte environment through ion channels or nano-pores embedded in membranes.
It is a 3-D particle-based Monte Carlo simulator for analyzing and studying the ion transport problem in ion channel systems or similar nanopores in wet/biological environments.
The system simulated consists of a protein forming an ion channel (or an artificial nanopores like a Carbon Nano Tube, CNT), with a membrane (i.e. lipid bilayer) that separates two ion baths on either side.
BioMOCA is based on two methodologies, namely the Boltzmann transport Monte Carlo (BTMC) and particle-particle-particle-mesh (P3M).
The first one uses Monte Carlo method to solve the Boltzmann equation, while the later splits the electrostatic forces into short-range and long-range components.
Method for simulating ion transport
Biology Monte Carlo methods (BioMOCA) have been developed at the University of Illinois at Urbana-Champaign to simulate ion transport in an electrolyte environment through ion channels or nano-pores embedded in membranes.
It is a 3-D particle-based Monte Carlo simulator for analyzing and studying the ion transport problem in ion channel systems or similar nanopores in wet/biological environments.
The system simulated consists of a protein forming an ion channel (or an artificial nanopores like a Carbon Nano Tube, CNT), with a membrane (i.e. lipid bilayer) that separates two ion baths on either side.
BioMOCA is based on two methodologies, namely the Boltzmann transport Monte Carlo (BTMC) and particle-particle-particle-mesh (P3M).
The first one uses Monte Carlo method to solve the Boltzmann equation, while the later splits the electrostatic forces into short-range and long-range components.
