scholarly journals Complex University Timetabling Using Iterative Forward Search Algorithm and Great Deluge Algorithm

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
I Gusti Agung Premananda ◽  
Ahmad Muklason
Keyword(s):  
Search Algorithm ◽  
Forward Search ◽  
University Timetabling ◽  
Great Deluge Algorithm

Detecting Outliers in Factor Analysis Using the Forward Search Algorithm

2008 ◽  
Vol 43 (3) ◽  
pp. 453-475 ◽  
Author(s):  
Dimitris Mavridis ◽  
Irini Moustaki
Keyword(s):  
Factor Analysis ◽  
Search Algorithm ◽  
Forward Search

A BASELINE GENE EXPRESSION-BASED PROGNOSTIC FOR ANTI-TNFα THERAPY RESPONSE IN PATIENTS WITH INFLAMMATORY BOWEL DISEASE

2021 ◽  
Vol 27 (Supplement_1) ◽  
pp. S8-S8
Author(s):  
Suraj Sakaram ◽  
Yudong He ◽  
Timothy Sweeney
Keyword(s):  
Gene Expression ◽  
Effect Size ◽  
Search Algorithm ◽  
Geometric Mean ◽  
Differentially Expressed ◽  
Expression Level ◽  
P Value ◽  
Predictive Test ◽  
Forward Search ◽  
Discriminatory Performance

Abstract Background Although anti-TNFα therapies have revolutionized the management and care of IBD, their administration and usage remain suboptimal because 1) over 50% of patients do not have a lasting therapeutic response, 2) they increase risk of infections, liver problems, arthritis, and lymphoma, and 3) they are expensive. With approximately 1.6 million people suffering from IBD in the US and global prevalence of IBD on the rise, a predictive test for anti-TNFα response would greatly improve the efficacy and cost-to-benefit ratio of these biologics. Methods We hypothesized that a multicohort analysis of the publicly available IBD gene expression datasets would yield a robust set of mRNAs for distinguishing anti-TNFα responders vs non-responders in the IBD patient population prior to treatment. We identified 5 datasets (n = 160) where whole-genome transcriptomic data was derived from colonic mucosal biopsies of IBD patients who were then subjected to anti-TNFα therapy and subsequently adjudicated for response. We used the MetaIntegrator framework which leverages a leave-one-study-out cross-validation technique in conjunction with effect size and FDR adjusted p-value to identify significant differentially expressed (DE) genes associated with a patient’s predisposition to a response outcome. DE genes were subjected to a greedy forward search to derive a parsimonious gene signature for a response score (geometric mean of the expression level for all positive mRNAs minus the geometric mean of the expression level of all negative mRNAs, multiplied by the ratio of counts of positive to negative genes). Area under the receiver operating characteristic curve (AUC) was subsequently calculated in a leave-one-study-out manner to assess discriminatory performance. Results We first identified 170 genes that were present in at least 40% of cohorts and significantly differentially expressed between responders and non-responders with effect size > 0.8 and q value < 0.1. A score based on these genes predicts responder vs non-responder across the 5 discovery cohorts with AUC of 0.82. Optimizing the variables with a greedy forward search algorithm allowed us to downselect to 7 genes from the set, and a score based on this parsimonious set of 7 genes improved the discriminatory performance to an AUC of 0.87. Choosing a high sensitivity (90%) for a rule-in scenario, the score had moderate specificity (60%); alternatively choosing a high specificity (90%) for a rule-out scenario, the score still had a good sensitivity (80%). Conclusions These initial findings suggest that there is a strong signal for predicting anti-TNFα response in colonic biopsies. In particular, we showed using the leave-one-study-out approach that a predictive signature using mRNA can be generalizable (works in independent cohorts). These initial results warrant further investigation.

scholarly journals Detection of natural structures and classification of HCI-HPR data using robust forward search algorithm

2016 ◽  
Vol 9 (1) ◽  
pp. 23-41 ◽  
Author(s):  
Fatima Isiaka ◽  
Kassim S Mwitondi ◽  
Adamu M Ibrahim
Keyword(s):  
Search Algorithm ◽  
Original Data ◽  
Data Sets ◽  
Forward Search ◽  
Distribution Method ◽  
Mahalanobis Distances ◽  
Content Type ◽  
Movement Data ◽  
Novel Approach ◽  
Fitting In

Purpose – The purpose of this paper is to proposes a forward search algorithm for detecting and identifying natural structures arising in human-computer interaction (HCI) and human physiological response (HPR) data. Design/methodology/approach – The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models used in classification algorithms. Data were also simulated based on multivariate normal distribution method and used to compare and validate the original data. Findings – Results shows that the forward search method provides robust features that are capable of repelling over-fitting in physiological and eye movement data. Research limitations/implications – One of the limitations of the robust forward search algorithm is that when the number of digits for residuals value is more than the expected size for stack flow, it normally yields an error caution; to counter this, the data sets are normally standardized by taking the logarithmic function of the model before running the algorithm. Practical implications – The authors conducted some of the experiments at individual residence which may affect environmental constraints. Originality/value – The novel approach to this method is the detection of outliers for data sets based on the Mahalanobis distances on HCI and HPR. And can also involve a large size of data with p possible parameters. The improvement made to the algorithm is application of more graphical display and rendering of the residual plot.

scholarly journals A forward search algorithm for detecting extreme study effects in network meta‐analysis

2021 ◽  
Author(s):  
Maria Petropoulou ◽  
Georgia Salanti ◽  
Gerta Rücker ◽  
Guido Schwarzer ◽  
Irini Moustaki ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Meta Analysis ◽  
Forward Search

Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection

2011 ◽  
Vol 46 (1) ◽  
pp. 58-89 ◽  
Author(s):  
Wobbe P. Zijlstra ◽  
L. Andries van der Ark ◽  
Klaas Sijtsma
Keyword(s):  
Outlier Detection ◽  
Search Algorithm ◽  
Scale Analysis ◽  
Forward Search ◽  
Mokken Scale Analysis

scholarly journals Research on Particle Collision Forward Search Algorithm and the CFD-DEM Variable Time Step Coupling Calculation Method

2021 ◽  
Author(s):  
Xuefei Wang ◽  
Suling Wang ◽  
Ming Wang ◽  
Xuemei Li ◽  
Lin Chi ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Accurate Solution ◽  
Particle Collision ◽  
Particle Collisions ◽  
Forward Search ◽  
Time Step ◽  
Calculation Time ◽  
Fluid Coupling ◽  
Center Particle ◽  
Variable Time Step

Abstract In CFD-DEM coupling calculations, an excessively large selection for particle calculation time step affects the calculation accuracy, and an extremely small selection affects the calculation efficiency. A search ball is constructed by taking each target particle as the center particle with the fastest displacement in the calculation domain. Subsequently, the particles that may collide are screened to establish a search list, and a forward search method is used to determine particle collisions. Finally, a particle calculation time step is proposed. The improved DEM method, which automatically adjusts the collision time, resolves the contradiction between particle calculation time step selection, accuracy, and efficiency. The relative error between the numerical simulation results of particle collision and the theoretical solution was less than 3%. The three calculation time steps selected in this study can guarantee excellent calculation accuracy and efficiency. For multi-particle and fluid coupling simulations, the traditional CFD-DEM method selects 10-7s or less in the calculation time step to obtain an accurate solution. The method proposed in this paper selects 10-5s to obtain an accurate solution, which increased the calculation efficiency by 19.8%.

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