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1. Analysis of Persian Bioinformatics Research with Topic Modeling.

Author

Ebrahimi, Fezzeh, Dehghani, Mohammad, and Makkizadeh, Fatemah

Subjects
LIFE sciences, RESEARCH, BIOMARKERS, PHONOLOGICAL awareness, MATHEMATICAL models, NATURAL language processing, RESEARCH methodology, BIBLIOMETRICS, MOLECULAR models, BIOINFORMATICS, MATHEMATICS, CITATION analysis, GENE expression, THEORY, MEDICAL research, INFORMATION technology, ALGORITHMS
Abstract

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Use of a Novel Grammatical Inference Approach in Classification of Amyloidogenic Hexapeptides

Author

Wieczorek, Wojciech and Unold, Olgierd

Subjects
0301 basic medicine, Amyloid, Support Vector Machine, Databases, Factual, Article Subject, Bioinformatics, Factual Databases, lcsh:Computer applications to medicine. Medical informatics, Machine learning, computer.software_genre, Models, Biological, Sensitivity and Specificity, General Biochemistry, Genetics and Molecular Biology, Field (computer science), Automation, 03 medical and health sciences, Humans, False Positive Reactions, Regular expression, Language, Mathematics, Models, Statistical, General Immunology and Microbiology, business.industry, Heuristic, Applied Mathematics, Computational Biology, Reproducibility of Results, General Medicine, Grammar induction, Automaton, Support vector machine, Correlation coefficient, 030104 developmental biology, Modeling and Simulation, Data analysis, lcsh:R858-859.7, Graph (abstract data type), Artificial intelligence, Peptides, business, computer, Algorithms, Research Article
Abstract

The present paper is a novel contribution to the field of bioinformatics by using grammatical inference in the analysis of data. We developed an algorithm for generating star-free regular expressions which turned out to be good recommendation tools, as they are characterized by a relatively high correlation coefficient between the observed and predicted binary classifications. The experiments have been performed for three datasets of amyloidogenic hexapeptides, and our results are compared with those obtained using the graph approaches, the current state-of-the-art methods in heuristic automata induction, and the support vector machine. The results showed the superior performance of the new grammatical inference algorithm on fixed-length amyloid datasets.

Published
2016

3. Bayesian mixture structural equation modelling in multiple-trait QTL mapping

Author

Ismail Dweikat, Xiaojuan Mi, Kent M. Eskridge, Kulvinder S. Gill, B. Todd Campbell, P. Stephen Baenziger, and Dong Wang

Subjects
Models, Genetic, Quantitative Trait Loci, Bayesian probability, Posterior probability, Chromosome Mapping, Bayes Theorem, Markov chain Monte Carlo, Bayes factor, General Medicine, Quantitative trait locus, Bioinformatics, Markov Chains, Bayes' theorem, symbols.namesake, Inclusive composite interval mapping, Statistics, Genetics, symbols, Monte Carlo Method, Algorithms, Triticum, Gibbs sampling, Mathematics
Abstract

SummaryQuantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis–Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.

Published
2010