scholarly journals Test Set Diameter: Quantifying the Diversity of Sets of Test Cases

2016 ◽  
Author(s):  
Robert Feldt ◽  
Simon Poulding ◽  
David Clark ◽  
Shin Yoo
Keyword(s):  
Test Cases ◽  
Test Set

scholarly journals Improving the organization and interactivity of metabolic pathfinding with precomputed pathways

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Sarah M. Kim ◽  
Matthew I. Peña ◽  
Mark Moll ◽  
George N. Bennett ◽  
Lydia E. Kavraki
Keyword(s):  
Metabolic Pathways ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Test Cases ◽  
Test Set ◽  
Computational Search ◽  
Hub Network ◽  
Pathway Search ◽  
Core Reaction ◽  
Modular Nature

Abstract Background The rapid growth of available knowledge on metabolic processes across thousands of species continues to expand the possibilities of producing chemicals by combining pathways found in different species. Several computational search algorithms have been developed for automating the identification of possible heterologous pathways; however, these searches may return thousands of pathway results. Although the large number of results are in part due to the large number of possible compounds and reactions, a subset of core reaction modules is repeatedly observed in pathway results across multiple searches, suggesting that some subpaths between common compounds were more consistently explored than others.To reduce the resources spent on searching the same metabolic space, a new meta-algorithm for metabolic pathfinding, Hub Pathway search with Atom Tracking (HPAT), was developed to take advantage of a precomputed network of subpath modules. To investigate the efficacy of this method, we created a table describing a network of common hub metabolites and how they are biochemically connected and only offloaded searches to and from this hub network onto an interactive webserver capable of visualizing the resulting pathways. Results A test set of nineteen known pathways taken from literature and metabolic databases were used to evaluate if HPAT was capable of identifying known pathways. HPAT found the exact pathway for eleven of the nineteen test cases using a diverse set of precomputed subpaths, whereas a comparable pathfinding search algorithm that does not use precomputed subpaths found only seven of the nineteen test cases. The capability of HPAT to find novel pathways was demonstrated by its ability to identify novel 3-hydroxypropanoate (3-HP) synthesis pathways. As for pathway visualization, the new interactive pathway filters enable a reduction of the number of displayed pathways from hundreds down to less than ten pathways in several test cases, illustrating their utility in reducing the amount of presented information while retaining pathways of interest. Conclusions This work presents the first step in incorporating a precomputed subpath network into metabolic pathfinding and demonstrates how this leads to a concise, interactive visualization of pathway results. The modular nature of metabolic pathways is exploited to facilitate efficient discovery of alternate pathways.

scholarly journals GAPSO: Optimal Test Set Generator for Pairwise Testing

2019 ◽  
Vol 8 (6) ◽  
pp. 2346-2350
Keyword(s):  
Software Systems ◽  
Test Cases ◽  
Optimal Test ◽  
Swarm Optimization ◽  
Test Set ◽  
New Approach ◽  
Time Consumption ◽  
Pairwise Testing ◽  
The Cost ◽  
High Degree

Exhaustive testing is impossible for all sorts of software systems, owing to the cost and time consumption. Combinatorial testing is the solution to this issue and aims at picking the necessary set of parameters which can ensure high degree of interaction between the parameters. This paper presents a new approach for generating unique test cases by exploiting Genetic and Particle Swarm Optimization (GAPSO) algorithm for achieving pairwise testing. The generated test cases are refined, so as to arrive at the optimal test set. The outcome of the proposed algorithm is the minimal count of high quality test cases.

Linear Visco-Resistive Computations of Magnetohydrodynamic Waves: I. The Code and Test Cases

1994 ◽  
Vol 144 ◽  
pp. 503-505
Author(s):  
R. Erdélyi ◽  
M. Goossens ◽  
S. Poedts
Keyword(s):  
Resonant Absorption ◽  
Numerical Code ◽  
Mhd Waves ◽  
Test Cases ◽  
Numerical Accuracy ◽  
Element Discretization ◽  
Flux Tubes ◽  
Magnetohydrodynamic Waves ◽  
Viscosity Coefficients ◽  
Magnetic Flux Tubes

AbstractThe stationary state of resonant absorption of linear, MHD waves in cylindrical magnetic flux tubes is studied in viscous, compressible MHD with a numerical code using finite element discretization. The full viscosity tensor with the five viscosity coefficients as given by Braginskii is included in the analysis. Our computations reproduce the absorption rates obtained by Lou in scalar viscous MHD and Goossens and Poedts in resistive MHD, which guarantee the numerical accuracy of the tensorial viscous MHD code.

Using Connectionist Modules for Decision Support

1990 ◽  
Vol 29 (03) ◽  
pp. 167-181 ◽  
Author(s):  
G. Hripcsak
Keyword(s):  
Decision Support ◽  
Standard Deviation ◽  
Confidence Interval ◽  
Posterior Probability ◽  
Back Propagation ◽  
Connectionist Model ◽  
Test Set ◽  
The Third ◽  
Independent Test ◽  
Better Than

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

RetroBioCat: Computer-Aided Synthesis Planning for Biocatalytic Reactions and Cascades

2020 ◽  
Author(s):  
William Finnigan ◽  
Lorna J. Hepworth ◽  
Nicholas J. Turner ◽  
Sabine Flitsch
Keyword(s):  
Organic Chemistry ◽  
Computer Aided Design ◽  
Selective Synthesis ◽  
Test Set ◽  
Synthesis Design ◽  
Computer Aided ◽  
Synthesis Planning ◽  
Enzymatic Cascades ◽  
Target Molecules ◽  
Aided Design

As the enzyme toolbox for biocatalysis has expanded, so has the potential for the construction of powerful enzymatic cascades for efficient and selective synthesis of target molecules. Additionally, recent advances in computer-aided synthesis planning (CASP) are revolutionizing synthesis design in both synthetic biology and organic chemistry. However, the potential for biocatalysis is not well captured by tools currently available in either field. Here we present RetroBioCat, an intuitive and accessible tool for computer-aided design of biocatalytic cascades, freely available at retrobiocat.com. Our approach uses a set of expertly encoded reaction rules encompassing the enzyme toolbox for biocatalysis, and a system for identifying literature precedent for enzymes with the correct substrate specificity where this is available. Applying these rules for automated biocatalytic retrosynthesis, we show our tool to be capable of identifying promising biocatalytic pathways to target molecules, validated using a test-set of recent cascades described in the literature.

PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Haekal ◽  
Henki Bayu Seta ◽  
Mayanda Mega Santoni
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Online Monitoring ◽  
Training Set ◽  
Microsoft Excel ◽  
Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

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