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Life Sciences Group

Peertechz Journal of Bioinformatics and Biostatistics

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Aims and Scope

Peertechz Journal of Bioinformatics and Biostatistics aims to publish high quality, original research articles, expository tutorial papers and review papers as well as short, critical comments on technical issues associated with the analysis of cellular information.

The journal aims to be one of the advocates of modern quantitative methodologies supported by mathematical and statistical theory and a pioneer that provides a forum for challenging mathematical and statistical models and methods.

Classifications of Peertechz Journal of Bioinformatics and Biostatistics (but not limited to following fields):
  • Agricultural Data Modeling, Bioinformatics Methods
  • Biological Experimental Data Modeling, Biostatistics Theory and Methods
  • Pain Management Anesthesia, Clinical Study Design and Analysis
  • Clinical Trials and Assessment Methods, Climate and Environmental Data Modeling
  • Computational Biology, Statistical Computation and Applications
  • Distribution and Modeling of Disease Dynamics, Epidemiological Data Modeling and Analysis
  • Evolution and Population Genetics, Genetics and Genetic Epidemiology
  • Health Services Research and Assessment, Mathematical Biology
  • Medical Imaging Data Modeling, Public Health and Healthcare Evaluation
  • Public Health Policy Research, Agriculture, Biochemistry
  • Cancer Biology, Cell Biology, Clinical Medicine, Data and Text Mining
  • Databases and Ontology's, Gene Expression, Genetics and Population
  • Analysis, Genome analysis, Sequence analysis, Structural Bioinformatics
  • Structural Biology, Systems Biology, Databases, bio-grid, Biomedical image processing
  • Modeling and simulation, Bio-ontology and data mining, DNA assembly
  • Clustering, mapping, Computational genomics/proteomics
  • Silicon technology: computational intelligence, high performance computing, E-health, telemedicine, Gene expression, microarrays, identification, annotation
  • Genetic algorithms, fuzzy logic, neural networks, data visualization
  • Hidden Markov models, machine learning, support vector machines
  • Molecular evolution, phylogeny, modeling, simulation, sequence analysis
  • Parallel algorithms/architectures, computational structural biology
  • Sequence assembly, search, alignment, Signaling/computational biomedical data engineering
  • Simulated annealing, statistical analysis
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