Enzyme Function Classification Based on Borda Count Ranking Aggregation Method

2016 ◽  
pp. 75-85 ◽  
Author(s):  
Mahir M. Sharif ◽  
Alaa Tharwat ◽  
Aboul Ella Hassanien ◽  
Hesham A. Hefny ◽  
Gerald Schaefer
Keyword(s):  
Enzyme Function ◽  
Borda Count ◽  
Aggregation Method ◽  
Ranking Aggregation

scholarly journals A new hierarchical ranking aggregation method

2018 ◽  
Vol 453 ◽  
pp. 168-185 ◽  
Author(s):  
Jiankun Ding ◽  
Deqiang Han ◽  
Jean Dezert ◽  
Yi Yang
Keyword(s):  
Aggregation Method ◽  
Ranking Aggregation

scholarly journals The Borda Count as an Initial Threshold for Kemeny Ranking Aggregation

2021 ◽  
Author(s):  
Noelia Rico ◽  
Camino R. Vela ◽  
Raúl Pérez-Fernández ◽  
Irene Díaz
Keyword(s):  
Borda Count ◽  
Ranking Aggregation

Systematic comparison of ranking aggregation methods for gene lists in experimental results

2022 ◽  
Author(s):  
Bo Wang ◽  
Andy Law ◽  
Tim Regan ◽  
Nicholas Parkinson ◽  
Joby Cole ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Simulated Data ◽  
Real Data ◽  
Biomedical Science ◽  
Aggregation Method ◽  
Aggregation Methods ◽  
The Common ◽  
Ranking Aggregation ◽  
Meta Analyses ◽  
Answer Ranking

A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The results of a group of studies answering the same, or similar, questions can be combined by meta-analysis to find a consensus or a more reliable answer. Ranking aggregation methods can be used to combine gene lists from various sources in meta-analyses. Evaluating a ranking aggregation method on a specific type of dataset before using it is required to support the reliability of the result since the property of a dataset can influence the performance of an algorithm. Evaluation of aggregation methods is usually based on a simulated database especially for the algorithms designed for gene lists because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. In this study, a group of existing methods and their variations which are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data was used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomics data, with various heterogeneity of quality, noise level, and a mix of unranked and ranked data using 20000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (NSCLC), and bacteria (macrophage apoptosis) was performed. We summarise our evaluation results in terms of a simple flowchart to select a ranking aggregation method for genomics data.

Aiming for a Standardized Protocol for Preparing a Process Green Synthesis Report and for Ranking Multiple Synthesis Plans to a Common Target Product

2019 ◽  
Author(s):  
john andraos
Keyword(s):  
Green Synthesis ◽  
Block Diagram ◽  
Computational Method ◽  
Borda Count ◽  
Target Product ◽  
Material Efficiency ◽  
Synthesis Report ◽  
Common Target ◽  
Greenness Index ◽  
Multiple Synthesis

This paper proposes a standardized format for the preparation of process green synthesis reports that can be applied to chemical syntheses of active pharmaceutical ingredients (APIs) of importance to the pharmaceutical industry. Such a report is comprised of the following eight sections: a synthesis scheme, a synthesis tree, radial pentagons and step E-factor breakdowns for each reaction step, a tabular summary of key material efficiency step and overall metrics for a synthesis plan, a mass process block diagram, an energy consumption audit based on heating and cooling reaction and auxiliary solvents, a summary of environmental and safety-hazard impacts based on organic solvent consumption using the Rowan solvent greenness index, and a cycle time process schedule. Illustrative examples of process green synthesis reports are given for the following pharmaceuticals: 5-HT2B and 5-HT7 receptors antagonist (Astellas Pharma), brivanib (Bristol-Myers Squibb), and orexin receptor agonist (Merck). Methods of ranking synthesis plans to a common target product are also discussed using 6 industrial synthesis plans of apixaban (Bristol-Myers Squibb) as a working example. The Borda count method is suggested as a facile and reliable computational method for ranking multiple synthesis plans to a common target product using the following 4 attributes obtained from a process green synthesis report: process mass intensity, mass of sacrificial reagents used per kg of product, input enthalpic energy for solvents, and Rowan solvent greenness index for organic solvents.<br>

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