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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Articles
74
Citations
957
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Recent Articles
1.
Schmidt B, Kallenborn F, Chacon A, Hundt C
BMC Bioinformatics
. 2024 Nov;
25(1):342.
PMID: 39488701
Background: The maximal sensitivity for local pairwise alignment makes the Smith-Waterman algorithm a popular choice for protein sequence database search. However, its quadratic time complexity makes it compute-intensive. Unfortunately, current...
2.
Heilmann X, Henkys V, Apeldoorn D, Strauch K, Schmidt B, Lilienthal T, et al.
Stud Health Technol Inform
. 2024 Sep;
317:261-269.
PMID: 39234730
Introduction: Retrieving comprehensible rule-based knowledge from medical data by machine learning is a beneficial task, e.g., for automating the process of creating a decision support system. While this has recently...
3.
Kallenborn F, Schmidt B
BMC Bioinformatics
. 2024 May;
25(1):186.
PMID: 38730374
Background: Commonly used next generation sequencing machines typically produce large amounts of short reads of a few hundred base-pairs in length. However, many downstream applications would generally benefit from longer...
4.
Schmidt B, Hildebrandt A
Drug Discov Today
. 2024 Apr;
29(6):103990.
PMID: 38663581
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has...
5.
Xu X, Yin Z, Yan L, Yi H, Wang H, Schmidt B, et al.
Bioinformatics
. 2023 Nov;
39(11).
PMID: 37971961
Summary: We propose RabbitKSSD, a high-speed genome distance estimation tool. Specifically, we leverage load-balanced task partitioning, fast I/O, efficient intermediate result accesses, and high-performance data structures to improve overall efficiency....
6.
Wichmann A, Buschong E, Muller A, Junger D, Hildebrandt A, Hankeln T, et al.
NAR Genom Bioinform
. 2023 Sep;
5(3):lqad082.
PMID: 37705831
Deep learning has emerged as a paradigm that revolutionizes numerous domains of scientific research. Transformers have been utilized in language modeling outperforming previous approaches. Therefore, the utilization of deep learning...
7.
Yan L, Yin Z, Zhang H, Zhao Z, Wang M, Muller A, et al.
Methods
. 2023 Jun;
216:39-50.
PMID: 37330158
Assessing the quality of sequencing data plays a crucial role in downstream data analysis. However, existing tools often achieve sub-optimal efficiency, especially when dealing with compressed files or performing complicated...
8.
Xu X, Yin Z, Yan L, Zhang H, Xu B, Wei Y, et al.
Genome Biol
. 2023 May;
24(1):121.
PMID: 37198663
We present RabbitTClust, a fast and memory-efficient genome clustering tool based on sketch-based distance estimation. Our approach enables efficient processing of large-scale datasets by combining dimensionality reduction techniques with streaming...
9.
Zhang H, Song H, Xu X, Chang Q, Wang M, Wei Y, et al.
IEEE/ACM Trans Comput Biol Bioinform
. 2022 Nov;
20(3):2341-2348.
PMID: 36327193
The continuous growth of generated sequencing data leads to the development of a variety of associated bioinformatics tools. However, many of them are not able to fully exploit the resources...
10.
Bob K, Teschner D, Kemmer T, Gomez-Zepeda D, Tenzer S, Schmidt B, et al.
BMC Bioinformatics
. 2022 Jul;
23(1):287.
PMID: 35858828
Background: Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: first, even a single...