Bertil Schmidt
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Explore the profile of Bertil Schmidt including associated specialties, affiliations and a list of published articles.
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74
Citations
957
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Recent Articles
11.
Kallenborn F, Cascitti J, Schmidt B
BMC Bioinformatics
. 2022 Jun;
23(1):227.
PMID: 35698033
Background: Next-generation sequencing pipelines often perform error correction as a preprocessing step to obtain cleaned input data. State-of-the-art error correction programs are able to reliably detect and correct the majority...
12.
RabbitV: fast detection of viruses and microorganisms in sequencing data on multi-core architectures
Zhang H, Chang Q, Yin Z, Xu X, Wei Y, Schmidt B, et al.
Bioinformatics
. 2022 May;
38(10):2932-2933.
PMID: 35561184
Motivation: Detection and identification of viruses and microorganisms in sequencing data plays an important role in pathogen diagnosis and research. However, existing tools for this problem often suffer from high...
13.
Schmidt B, Hildebrandt A
Drug Discov Today
. 2020 Oct;
26(1):173-180.
PMID: 33059075
Next-generation sequencing (NGS) methods lie at the heart of large parts of biological and medical research. Their fundamental importance has created a continuously increasing demand for processing and analysis methods...
14.
Abuin J, Lopes N, Ferreira L, Pena T, Schmidt B
PLoS One
. 2020 Oct;
15(10):e0239741.
PMID: 33022000
The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest...
15.
Ringe K, Vo Chieu V, Wacker F, Lenzen H, Manns M, Hundt C, et al.
Eur Radiol
. 2020 Sep;
31(4):2482-2489.
PMID: 32974688
Objectives: To develop and evaluate a deep learning algorithm for fully automated detection of primary sclerosing cholangitis (PSC)-compatible cholangiographic changes on three-dimensional magnetic resonance cholangiopancreatography (3D-MRCP) images. Methods: The datasets...
16.
Winther H, Hundt C, Ringe K, Wacker F, Schmidt B, Jurgens J, et al.
Rofo
. 2020 Sep;
193(3):305-314.
PMID: 32882724
Purpose: To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Materials And Methods: Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients...
17.
Yin Z, Xu X, Zhang J, Wei Y, Schmidt B, Liu W
Bioinformatics
. 2020 Aug;
37(6):873-875.
PMID: 32845281
Motivation: Mash is a popular hash-based genome analysis toolkit with applications to important downstream analyses tasks such as clustering and assembly. However, Mash is currently not able to fully exploit...
18.
Kallenborn F, Hildebrandt A, Schmidt B
Bioinformatics
. 2020 Aug;
37(7):889-895.
PMID: 32818262
Motivation: Error correction is a fundamental pre-processing step in many Next-Generation Sequencing (NGS) pipelines, in particular for de novo genome assembly. However, existing error correction methods either suffer from high...
19.
Yin Z, Zhang H, Liu M, Zhang W, Song H, Lan H, et al.
Bioinformatics
. 2020 Aug;
37(4):573-574.
PMID: 32790850
Motivation: Modern sequencing technologies continue to revolutionize many areas of biology and medicine. Since the generated datasets are error-prone, downstream applications usually require quality control methods to pre-process FASTQ files....
20.
Niebler S, Muller A, Hankeln T, Schmidt B
BMC Bioinformatics
. 2020 Jul;
21(1):274.
PMID: 32611394
Background: Obtaining data from single-cell transcriptomic sequencing allows for the investigation of cell-specific gene expression patterns, which could not be addressed a few years ago. With the advancement of droplet-based...