scholarly journals A novel optimization algorithm for the missing data in HCC based on multiple imputation and genetic algorithm

2021 ◽  
Vol 8 (2) ◽  
pp. 151-155
Author(s):  
Y. Salah ◽  
M. Adel Hammad ◽  
Hatem Abdel-Kader
Keyword(s):  
Genetic Algorithm ◽  
Missing Data ◽  
Multiple Imputation ◽  
Optimization Algorithm

scholarly journals Multiple Imputation of Missing Data with Genetic Algorithm based Techniques

2010 ◽  
Vol ecot (2) ◽  
pp. 74-78 ◽  
Author(s):  
Dipak V. Patil ◽  
R. S. Bichkar
Keyword(s):  
Genetic Algorithm ◽  
Missing Data ◽  
Multiple Imputation

scholarly journals P189 Comparison of secukinumab versus adalimumab efficacy on skin outcomes in psoriatic arthritis: 52-week results from the EXCEED study

Rheumatology ◽  
2021 ◽  
Vol 60 (Supplement_1) ◽  
Author(s):  
Alice Gottlieb ◽  
Frank Behrens ◽  
Peter Nash ◽  
Joseph F Merola ◽  
Pascale Pellet ◽  
...  
Keyword(s):  
Psoriatic Arthritis ◽  
Missing Data ◽  
Multiple Imputation ◽  
Plaque Psoriasis ◽  
Stock Ownership ◽  
Research Support ◽  
Acr20 Response ◽  
Number Of Patients ◽  
Pasi Score ◽  
To Receive

Abstract Background/Aims  Psoriatic arthritis (PsA) is a heterogeneous disease comprising musculoskeletal and dermatological manifestations, especially plaque psoriasis. Secukinumab, an interleukin17A inhibitor, provided significantly greater PASI75/100 responses in two head-to-head trials versus etanercept or ustekinumab, a tumour necrosis factor inhibitor (TNFi), in patients with moderate-to-severe plaque psoriasis. The EXCEED study (NCT02745080) investigated whether secukinumab was superior to adalimumab, another TNFi, as monotherapy in biologic-naive active PsA patients with active plaque psoriasis (defined as having ≥1 psoriatic plaque of ≥ 2 cm diameter, nail changes consistent with psoriasis or documented history of plaque psoriasis). Here we report the pre-specified skin outcomes from the EXCEED study in the subset of patients with ≥3% body surface area (BSA) affected with psoriasis at baseline. Methods  In this head-to-head, Phase 3b, randomised, double-blind, active-controlled, multicentre, parallel-group trial, patients were randomised to receive subcutaneous secukinumab 300 mg at baseline and Weeks 1-4, followed by dosing every 4 weeks until Week 48, or subcutaneous adalimumab 40 mg at baseline followed by the same dosing every 2 weeks until Week 50. The primary endpoint was superiority of secukinumab versus adalimumab on ACR20 response at Week 52. Pre-specified outcomes included the proportion of patients achieving a combined ACR50 and PASI100 response, PASI100 response, and absolute PASI score ≤3. Missing data were handled using multiple imputation. Results  Overall, 853 patients were randomised to receive secukinumab (n = 426) or adalimumab (n = 427). At baseline, 215 and 202 patients had at least 3% BSA affected with psoriasis in the secukinumab and adalimumab groups, respectively. At Week 52, more patients achieved simultaneous improvement in ACR50 and PASI100 response with secukinumab versus adalimumab (30.7% versus 19.2%, respectively; P = 0.0087). Greater efficacy was demonstrated for secukinumab versus adalimumab for PASI100 responses and for the proportion of patients achieving absolute PASI score ≤3 (Table 1). Conclusion  In this pre-specified analysis, secukinumab provided higher responses compared with adalimumab in achievement of combined improvement in joint and skin disease (combined ACR50 and PASI100 response) and in skin-specific endpoints (PASI100 and absolute PASI score ≤3) at Week 52. P189 Table 1:Skin-specific outcomes at Week 52Endpoints, % responseSEC 300 mg (N = 215)ADA 40 mg (N = 202)P value (unadjusted)PASI10046300.0007Combined ACR50 and PASI10031190.0087Absolute PASI score ≤379650.0015P value vs ADA; unadjusted P values are presented. Multiple imputation was used for handling missing data. ADA, adalimumab; ACR, American College of Rheumatology; N, number of patients in the psoriasis subset; PASI, Psoriasis Area and Severity Index; SEC, secukinumab. Disclosure  A. Gottlieb: Grants/research support; A.G. has received research support, consultation fees or speaker honoraria from Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB. F. Behrens: Consultancies; F.B. is a consultant for Pfizer, AbbVie, Sanofi, Lilly, Novartis, Genzyme, Boehringer Ingelheim, Janssen, MSD, Celgene, Roche and Chugai. Grants/research support; F.B. has received grant/research support from Pfizer, Janssen, Chugai, Celgene, Lilly and Roche. P. Nash: Consultancies; P.N. is a consultant for AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, MSD, Novartis, Pfizer Inc., Roche, Sanofi and UCB. Member of speakers’ bureau; for AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, MSD, Novartis, Pfizer Inc., Roche, Sanofi and UCB. Grants/research support; P.N. has received research support from AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, MSD, Novartis, Pfizer Inc, Roche, Sanofi and UCB. J. Merola: Consultancies; J.F.M. is a consultant for Merck, AbbVie, Dermavant, Eli Lilly, Novartis, Janssen, UCB Pharma, Celgene, Sanofi, Regeneron, Arena, Sun Pharma, Biogen, Pfizer, EMD Sorono, Avotres and LEO Pharma. P. Pellet: Corporate appointments; P.P. is an employee of Novartis. Shareholder/stock ownership; P.P. is a shareholder of Novartis. L. Pricop: Corporate appointments; L.P. is an employee of Novartis. Shareholder/stock ownership; L.P. is a shareholder of Novartis. I. McInnes: Consultancies; I.M. is a consultant for AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer and UCB. Grants/research support; I.M. has received grant/research support from Bristol Myers Squibb, Celgene, Eli Lilly and Company, Janssen and UCB.

scholarly journals Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Seid Miad Zandavi ◽  
Vera Chung ◽  
Ali Anaissi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Simplex Algorithm ◽  
Local Optimum ◽  
Hybrid Optimization Algorithm ◽  
Multimodal Function ◽  
Remote Laboratories ◽  
Remote Lab ◽  
Timetable Problem

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

AN EVOLUTIONARY APPROACH FOR IMPUTING MISSING DATA IN TIME SERIES

2010 ◽  
Vol 19 (01) ◽  
pp. 107-121 ◽  
Author(s):  
JUAN CARLOS FIGUEROA GARCÍA ◽  
DUSKO KALENATIC ◽  
CESAR AMILCAR LÓPEZ BELLO
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Missing Data ◽  
Genetic Structure ◽  
Evolutionary Algorithm ◽  
Fitness Function ◽  
Error Function ◽  
Missing Observations ◽  
Mean And Variance ◽  
Methodological Aspects

This paper presents a proposal based on an evolutionary algorithm for imputing missing observations in time series. A genetic algorithm based on the minimization of an error function derived from their autocorrelation function, mean, and variance is presented. All methodological aspects of the genetic structure are presented. An extended description of the design of the fitness function is provided. Four application examples are provided and solved by using the proposed method.

Missing Value Imputation Using ANN Optimized by Genetic Algorithm

2018 ◽  
Vol 5 (2) ◽  
pp. 41-57 ◽  
Author(s):  
Anjana Mishra ◽  
Bighnaraj Naik ◽  
Suresh Kumar Srichandan
Keyword(s):  
Genetic Algorithm ◽  
Missing Data ◽  
Data Entry ◽  
Missing Information ◽  
Missing Value ◽  
Need Analysis ◽  
Processing Data ◽  
Real World Applications ◽  
Long Time ◽  
Almost All

Missing value arises in almost all serious statistical analyses and creates numerous problems in processing data in databases. In real world applications, information may be missing due to instrumental errors, optional fields and non-response to some questions in surveys, data entry errors, etc. Most of the data mining techniques need analysis of complete data without any missing information and this induces researchers to develop efficient methods to handle them. It is one of the most important areas where research is being carried out for a long time in various domains. The objective of this article is to handle missing data, using an evolutionary (genetic) algorithm including some relatively simple methodologies that can often yield reasonable results. The proposed method uses genetic algorithm and multi-layer perceptron (MLP) for accurately predicting missing data with higher accuracy.

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