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Vol. 12 No. 1 (2022): JITA - APEIRON

Dragana Dudić, Bojana Banović Đeri, Željko Stanković, Zoran Ž. Avramović

prepRNA: an integrative tool for Illumina RNAseq data filtering

Original scientific paper

DOI:https://doi.org/10.7251/JIT2201026D

Abstract

The vast amount of currently available transcriptome sequences is comprised of Illumina RNAseq data. Usually, publicly available datasets are provided as raw data and preparing them for the downstream NGS analysis is the first step required. Such preprocessing step, besides the evaluation of the quality of the raw data, includes data filtering, in order to provide high quality results of the downstream analysis. Existing tools for NGS data filtering are either too general or incomplete for the Illumina RNAseq filtering task, which is why a new tool for this endeavor was needed. We present prepRNA, a novel tool intended for Illumina RNAseq data filtering, which was designed as a comprehensive and user-friendly wrapper tool with possibility of further upgrading with a quality control option.

Keywords: RNAseq, data filtering, data preprocessing, NGS data, Illumina.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.