Third international conference “Bioinformatics: from Algorithms to Applications” (BiATA 2019)

doi:10.1186/s12859-019-3122-9

BMC Bioinformatics

2019

DOI: 10.1186/s12859-019-3122-9

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Third international conference “Bioinformatics: from Algorithms to Applications” (BiATA 2019)

Abstract: this data a statistically significant divergence with the model was detected. At the same time, no divergence was detected for diseases not related to onco-hematology. This experiment has shown that a V-D/D-J junction length distribution in Ig repertoire may be used as an indicator of the presence of pathological clones in a B-cell population. The possibility of the model application as an early predictor of various diseases presents a significant interest for further research.

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Cited by 2 publications

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Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

Prasanna¹,

Niranjan²

2021

CBIO

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Background: Since bacteria are the earliest known organisms, there has been significant interest in their variety and biology, most certainly concerning human health. Recent advances in Metagenomics sequencing (mNGS), a culture-independent sequencing technology have facilitated an accelerated development in clinical microbiology and our understanding of pathogens. Objective: For the implementation of mNGS in routine clinical practice to become feasible, a practical and scalable strategy for the study of mNGS data is essential. This study presents a robust automated pipeline to analyze clinical metagenomic data for pathogen identification and classification. Method: The proposed Clin-mNGS pipeline is an integrated, open-source, scalable, reproducible, and user-friendly framework scripted using the Snakemake workflow management software. The implementation avoids the hassle of manual installation and configuration of the multiple command-line tools and dependencies. The approach directly screens pathogens from clinical raw reads and generates consolidated reports for each sample. Results: The pipeline is demonstrated using publicly available data and is tested on a desktop Linux system and a High-performance cluster. The study compares variability in results from different tools and versions. The versions of the tools are made user modifiable. The pipeline results in quality check, filtered reads, host subtraction, assembled contigs, assembly metrics, relative abundances of bacterial species, antimicrobial resistance genes, plasmid finding, and virulence factors identification. The results obtained from the pipeline are evaluated based on sensitivity and positive predictive value. Conclusion: Clin-mNGS is an automated Snakemake pipeline validated for the analysis of microbial clinical metagenomics reads to perform taxonomic classification and antimicrobial resistance prediction.

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Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

Prasanna¹,

Niranjan²

2021

CBIO

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Using artificial intelligence in digital video production: A systematic review study

Orak,

Turan

2024

Journal of Educational Technology and Online Learning

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Artificial Intelligence (AI) advancements have significantly enhanced computer systems to better cater to user needs, consequently improving user experiences. One area witnessing widespread adoption of AI technology is in the production of digital videos, which find application across various fields, including education. This study aims to delineate the emerging trends in research concerning the integration of AI technology in digital video production. To achieve this objective, we conducted a systematic literature review across several databases, including Web of Science, ERIC, Taylor & Francis, Education Full Text EBSCO, and Sciencedirect. Following the PRISMA flowchart, studies were meticulously screened and selected based on predefined inclusion criteria aligned with the study's purpose. Our analysis encompassed 14 international studies, revealing diverse digital content creations facilitated by AI support. These technologies, offering various interaction dimensions, serve multiple purposes such as providing general guidance, reinforcing knowledge, facilitating design or experimentation, and delivering personalized experiences through tailored technologies. Despite these advancements, the full potential of AI technology remains underutilized. Hence, future research should prioritize the development of digital content that accommodates individual differences, fosters social interaction, incorporates enriched features, and is adaptable to diverse environments. In conclusion, while AI-driven advancements in digital video production hold promise, there exists ample room for further exploration and optimization to fully leverage the capabilities of this technology.

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Third international conference “Bioinformatics: from Algorithms to Applications” (BiATA 2019)

Cited by 2 publications

References 7 publications

Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

Using artificial intelligence in digital video production: A systematic review study

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