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Mateusz Chilinski

dblp:334/4903 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0001-6641-8504ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics › structural variation
structural variant detection
0.612022
ConsensuSV - from the whole-genome sequencing data to the complete variant list · Bioinform. 2022
Bioinformatics and computational biology › genomics
variant calling
0.612022
ConsensuSV - from the whole-genome sequencing data to the complete variant list · Bioinform. 2022
High-performance computing
parallel workflow
0.212022
ConsensuSV - from the whole-genome sequencing data to the complete variant list · Bioinform. 2022

Methods — techniques the papers use, named apart from their topics

luigi framework · 1.1consensus calling · 1.1
YearPublicationVenuePosition
2025 Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large Language Models with CheckboxQA
Michal Turski, Mateusz Chilinski, Lukasz Borchmann
ICDAR (4)2
2022 ConsensuSV - from the whole-genome sequencing data to the complete variant list
abstract
SUMMARY: The detection of the structural variants (SVs) using Illumina sequencing of human DNA is not an easy task. Multiple approaches have been proposed; however, all the methods have their limitations. In this article, we present ConsensuSV pipeline that aids the research in complex variant detection. By using consensus meta-approach, eight independent SV callers are being used to identify a uniform set of high-quality SVs. The pipeline works using raw sequencing data and performs all the necessary steps automatically, significantly reducing the researchers' time required for processing the data. The output files contain SVs, single nucleotide polymorphisms and Indels. The pipeline uses luigi framework, allowing the software to be run efficiently and parallelly using the high-performance computing infrastructure. We strongly believe that the software is useful to the scientific community interested in the germline variant detection. AVAILABILITY AND IMPLEMENTATION: https://github.com/SFGLab/ConsensuSV-pipeline. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Mateusz Chilinski, Dariusz Plewczynski
Bioinform.1