Browse
Submit Data
Databases
API
Help

Dataset Information

13 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The Binary-Based Model (BBM) for Improved Human Factors Method Selection.

ABSTRACT:

Objective

This paper presents the Binary-Based Model (BBM), a new approach to Human Factors (HF) method selection. The BBM helps practitioners select the most appropriate HF methodology in relation to the complexity within the target system.

Background

There are over 200 HF methods available to the practitioner and little guidance to help choose between them.

Method

The BBM defines a HF "problem space" comprising three complexity attributes. HF problems can be rated against these attributes and located in the "problem space." In addition, a similar HF "approach space" in which 66 predictive methods are rated according to their ability to confront those attributes is defined. These spaces are combined into a "utility space" in which problems and methods coexist. In the utility space, the match between HF problems and methods can be formally assessed.

Results

The method space is split into octants to establish broad groupings of methods distributed throughout the space. About 77% of the methods reside in Octant 1 which corresponds to problems with low levels of complexity. This demonstrates that most HF methods are suited to problems in low-complexity systems.

Conclusion

The location of 77% of the rated methods in Octant 1 indicates that HF practitioners are underserved with methods for analysis of HF problems exhibiting high complexity.

Application

The BBM can be used by multidisciplinary teams to select the most appropriate HF methodology for the problem under analysis. All the materials and analysis are placed in the public domain for modification and consensus building by the wider HF community.

SUBMITTER: Holman M

PROVIDER: S-EPMC8593309 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

An improved wrapper-based feature selection method for machinery fault diagnosis.

Project description:A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.

| S-EPMC5738058 | biostudies-other

Improved Functional Causal Likelihood-Based Causal Discovery Method for Diabetes Risk Factors.

Project description:Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors. However, understanding the causality is also essential to preventing the disease. In this study, a causal discovery method for diabetes risk factors was developed based on an improved functional causal likelihood (IFCL) model. Firstly, the issue of excessive redundant and false edges in functional causal likelihood structures was resolved through the construction of an IFCL model using an adjustment threshold value. On this basis, an IFCL-based causal discovery algorithm was designed, and a simulation experiment was performed with the developed algorithm. The experimental results revealed that the causal structure generated using a dataset with a sample size of 2000 provided more information than that produced using a dataset with a sample size of 768. In addition, the causal structures obtained with the developed algorithm had fewer redundant and false edges. The following six causal relationships were identified: insulin→plasma glucose concentration, plasma glucose concentration→body mass index (BMI), triceps skin fold thickness→BMI and age, diastolic blood pressure→BMI, and number of times pregnant→age. Furthermore, the reasonableness of these causal relationships was investigated. The algorithm developed in this study enables the discovery of causal relationships among various diabetes risk factors and can serve as a reference for future causality studies on diabetes risk factors.

| S-EPMC8143882 | biostudies-literature

Novel sulI binary vectors enable an inexpensive foliar selection method in Arabidopsis.

Project description:BACKGROUND: Sulfonamide resistance is conferred by the sulI gene found on many Enterobacteriaceae R plasmids and Tn21 type transposons. The sulI gene encodes a sulfonamide insensitive dihydropteroate synthase enzyme required for folate biosynthesis. Transformation of tobacco, potato or Arabidopsis using sulI as a selectable marker generates sulfadiazine-resistant plants. Typically sulI-based selection of transgenic plants is performed on tissue culture media under sterile conditions. FINDINGS: A set of novel binary vectors containing a sulI selectable marker expression cassette were constructed and used to generate transgenic Arabidopsis. We demonstrate that the sulI selectable marker can be utilized for direct selection of plants grown in soil with a simple foliar spray application procedure. A highly effective and inexpensive high throughput screening strategy to identify transgenic Arabidopsis without use of tissue culture was developed. CONCLUSION: Novel sulI-containing Agrobacterium binary vectors designed to over-express a gene of interest or to characterize a test promoter in transgenic plants have been constructed. These new vector tools combined with the various beneficial attributes of sulfonamide selection and the simple foliar screening strategy provide an advantageous alternative for plant biotechnology researchers. The set of binary vectors is freely available upon request.

| S-EPMC3060128 | biostudies-literature

Improved feature-selection method considering the imbalance problem in text categorization.

Project description:The filtering feature-selection algorithm is a kind of important approach to dimensionality reduction in the field of the text categorization. Most of filtering feature-selection algorithms evaluate the significance of a feature for category based on balanced dataset and do not consider the imbalance factor of dataset. In this paper, a new scheme was proposed, which can weaken the adverse effect caused by the imbalance factor in the corpus. We evaluated the improved versions of nine well-known feature-selection methods (Information Gain, Chi statistic, Document Frequency, Orthogonal Centroid Feature Selection, DIA association factor, Comprehensive Measurement Feature Selection, Deviation from Poisson Feature Selection, improved Gini index, and Mutual Information) using naïve Bayes and support vector machines on three benchmark document collections (20-Newsgroups, Reuters-21578, and WebKB). The experimental results show that the improved scheme can significantly enhance the performance of the feature-selection methods.

| S-EPMC4058251 | biostudies-other

Kernel method based human model for enhancing interactive evolutionary optimization.

Project description:A fitness landscape presents the relationship between individual and its reproductive success in evolutionary computation (EC). However, discrete and approximate landscape in an original search space may not support enough and accurate information for EC search, especially in interactive EC (IEC). The fitness landscape of human subjective evaluation in IEC is very difficult and impossible to model, even with a hypothesis of what its definition might be. In this paper, we propose a method to establish a human model in projected high dimensional search space by kernel classification for enhancing IEC search. Because bivalent logic is a simplest perceptual paradigm, the human model is established by considering this paradigm principle. In feature space, we design a linear classifier as a human model to obtain user preference knowledge, which cannot be supported linearly in original discrete search space. The human model is established by this method for predicting potential perceptual knowledge of human. With the human model, we design an evolution control method to enhance IEC search. From experimental evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search significantly.

| S-EPMC4386548 | biostudies-other

Model based Rational Design of an Oncolytic Virus with Improved Therapeutic Potential

Project description:Oncolytic viruses are complex biological agents that interact at multiple levels with both tumor and normal tissues. Anti-viral pathways induced by interferon are known to play a critical role in determining tumor cell sensitivity and normal cell resistance to infection with oncolytic viruses. Here we pursue a synthetic biology approach to identify methods that enhance anti-tumor activity of oncolytic viruses through suppression of IFN signaling. Based on the mathematical analysis of multiple strategies, we hypothesize that a positive feedback loop, established by virus-mediated expression of a soluble interferon-binding decoy receptor, increases tumor cytotoxicity without compromising normal cells. Oncolytic rhabodviruses engineered to express a secreted interferon antagonist have improved oncolytic potential in cellular cancer models, and display improved therapeutic potential in tumor-bearing mice. Our results demonstrate the potential of this methodology in evaluating potential caveats of viral immune evasion strategies and improving the design of oncolytic viruses. The following series of microarray experiments was utilized to assess the impact of cloning an IFN decoy receptor isolated from vaccinia virus termed B19R on the transcriptional response against an IFN sensitive maraba virus strain termed MG1. RNA extraction was performed 24h post infection in 786-0 cells. Duplicate samples were pooled, and hybridized on Affymetrix human gene 1.0 ST arrays according to manufacturer instructions. Data analysis was performed using AltAnalyze. Briefly, probeset filtering implemented a DABG threshold of 70 & pV<0.05 and utilized exclusively constitutively expressed exons to assess levels of gene expression.

2013-06-01 | E-MTAB-1636 | biostudies-arrayexpress

An improved method for identifying biological circular RNAs

Project description:Purpose: We are using the illumina sequencing to compare the false positive and true positive circular RNA findings to confine the method to detect the true circular RNAs Methods: The testis whole transcriptome profiling was generated from 4-week mouse testis using illumina Nextseq, duplicated. The sequence reads that passed quality filters were analyzed at the transcript isoform level with TopHat followed by Cufflinks. Results: our data suggest that circular RNAs identified based on junction sequences in the RNA-seq reads, especially those from Illumina Hiseq sequencing, mostly result from template-switching events during reverse transcription by MMLV-derived reverse transcriptases. It is critical to employ reverse transcriptases lacking terminal transferase activity (e.g., MonsterScript) to construct sequencing library or perform RT-PCR for identification and quantification of true circular RNAs. Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions. The wild type mouse testis RNAs were constructed NGS library by two different enzyme, then parallel sequenced in illumina Nextseq

2016-07-05 | E-GEOD-83992 | biostudies-arrayexpress

An improved memory-based collaborative filtering method based on the TOPSIS technique.

Project description:This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics.

| S-EPMC6171847 | biostudies-literature

Correcting for purifying selection: an improved human mitochondrial molecular clock.

Project description:There is currently no calibration available for the whole human mtDNA genome, incorporating both coding and control regions. Furthermore, as several authors have pointed out recently, linear molecular clocks that incorporate selectable characters are in any case problematic. We here confirm a modest effect of purifying selection on the mtDNA coding region and propose an improved molecular clock for dating human mtDNA, based on a worldwide phylogeny of > 2000 complete mtDNA genomes and calibrating against recent evidence for the divergence time of humans and chimpanzees. We focus on a time-dependent mutation rate based on the entire mtDNA genome and supported by a neutral clock based on synonymous mutations alone. We show that the corrected rate is further corroborated by archaeological dating for the settlement of the Canary Islands and Remote Oceania and also, given certain phylogeographic assumptions, by the timing of the first modern human settlement of Europe and resettlement after the Last Glacial Maximum. The corrected rate yields an age of modern human expansion in the Americas at approximately 15 kya that-unlike the uncorrected clock-matches the archaeological evidence, but continues to indicate an out-of-Africa dispersal at around 55-70 kya, 5-20 ky before any clear archaeological record, suggesting the need for archaeological research efforts focusing on this time window. We also present improved rates for the mtDNA control region, and the first comprehensive estimates of positional mutation rates for human mtDNA, which are essential for defining mutation models in phylogenetic analyses.

| S-EPMC2694979 | biostudies-literature

An improved method for identifying biological circular RNAs

2016-07-05 | GSE83992 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data