TY - JOUR A1 - Ladleif, Jan A1 - Weske, Mathias T1 - Which event happened first? BT - Deferred choice on blockchain using oracles JF - Frontiers in blockchain N2 - First come, first served: Critical choices between alternative actions are often made based on events external to an organization, and reacting promptly to their occurrence can be a major advantage over the competition. In Business Process Management (BPM), such deferred choices can be expressed in process models, and they are an important aspect of process engines. Blockchain-based process execution approaches are no exception to this, but are severely limited by the inherent properties of the platform: The isolated environment prevents direct access to external entities and data, and the non-continual runtime based entirely on atomic transactions impedes the monitoring and detection of events. In this paper we provide an in-depth examination of the semantics of deferred choice, and transfer them to environments such as the blockchain. We introduce and compare several oracle architectures able to satisfy certain requirements, and show that they can be implemented using state-of-the-art blockchain technology. KW - business processes KW - business process management KW - deferred choice KW - workflow patterns KW - blockchain KW - smart contracts KW - oracles KW - formal semantics Y1 - 2021 U6 - https://doi.org/10.3389/fbloc.2021.758169 SN - 2624-7852 VL - 4 SP - 1 EP - 16 PB - Frontiers in Blockchain CY - Lausanne, Schweiz ER - TY - JOUR A1 - Perscheid, Cindy T1 - Comprior BT - Facilitating the implementation and automated benchmarking of prior knowledge-based feature selection approaches on gene expression data sets JF - BMC Bioinformatics N2 - Background Reproducible benchmarking is important for assessing the effectiveness of novel feature selection approaches applied on gene expression data, especially for prior knowledge approaches that incorporate biological information from online knowledge bases. However, no full-fledged benchmarking system exists that is extensible, provides built-in feature selection approaches, and a comprehensive result assessment encompassing classification performance, robustness, and biological relevance. Moreover, the particular needs of prior knowledge feature selection approaches, i.e. uniform access to knowledge bases, are not addressed. As a consequence, prior knowledge approaches are not evaluated amongst each other, leaving open questions regarding their effectiveness. Results We present the Comprior benchmark tool, which facilitates the rapid development and effortless benchmarking of feature selection approaches, with a special focus on prior knowledge approaches. Comprior is extensible by custom approaches, offers built-in standard feature selection approaches, enables uniform access to multiple knowledge bases, and provides a customizable evaluation infrastructure to compare multiple feature selection approaches regarding their classification performance, robustness, runtime, and biological relevance. Conclusion Comprior allows reproducible benchmarking especially of prior knowledge approaches, which facilitates their applicability and for the first time enables a comprehensive assessment of their effectiveness KW - Feature selection KW - Prior knowledge KW - Gene expression KW - Reproducible benchmarking Y1 - 2021 U6 - https://doi.org/10.1186/s12859-021-04308-z SN - 1471-2105 VL - 22 SP - 1 EP - 15 PB - Springer Nature CY - London ER - TY - JOUR A1 - Freitas da Cruz, Harry A1 - Pfahringer, Boris A1 - Martensen, Tom A1 - Schneider, Frederic A1 - Meyer, Alexander A1 - Böttinger, Erwin A1 - Schapranow, Matthieu-Patrick T1 - Using interpretability approaches to update "black-box" clinical prediction models BT - an external validation study in nephrology JF - Artificial intelligence in medicine : AIM N2 - Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III dataset when applied to an external cohort of an American research hospital. To help account for the performance differences observed, we utilized interpretability methods based on feature importance, which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. The knowledge gleaned upon derivation can be potentially useful to assist model update during validation for more generalizable and simpler models. We argue that interpretability methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies. KW - Clinical predictive modeling KW - Nephrology KW - Validation KW - Interpretability KW - methods Y1 - 2021 U6 - https://doi.org/10.1016/j.artmed.2020.101982 SN - 0933-3657 SN - 1873-2860 VL - 111 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Monti, Remo A1 - Rautenstrauch, Pia A1 - Ghanbari, Mahsa A1 - Rani James, Alva A1 - Kirchler, Matthias A1 - Ohler, Uwe A1 - Konigorski, Stefan A1 - Lippert, Christoph T1 - Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes JF - Nature Communications N2 - Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene- based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for mis- sense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood- ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability. Y1 - 2022 U6 - https://doi.org/10.1038/s41467-022-32864-2 SN - 2041-1723 VL - 13 PB - Nature Publishing Group UK CY - London ER - TY - JOUR A1 - Ulrich, Jens-Uwe A1 - Lutfi, Ahmad A1 - Rutzen, Kilian A1 - Renard, Bernhard Y. T1 - ReadBouncer BT - precise and scalable adaptive sampling for nanopore sequencing JF - Bioinformatics N2 - Motivation: Nanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast graphical processing units (GPU) base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications. Results: Here, we present a new approach for nanopore adaptive sampling that combines fast CPU and GPU base calling with read classification based on Interleaved Bloom Filters. ReadBouncer improves the potential enrichment of low abundance sequences by its high read classification sensitivity and specificity, outperforming existing tools in the field. It robustly removes even reads belonging to large reference sequences while running on commodity hardware without GPUs, making adaptive sampling accessible for in-field researchers. Readbouncer also provides a user-friendly interface and installer files for end-users without a bioinformatics background. Y1 - 2022 U6 - https://doi.org/10.1093/bioinformatics/btac223 SN - 1367-4803 SN - 1460-2059 VL - 38 IS - SUPPL 1 SP - 153 EP - 160 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Doerr, Benjamin A1 - Kötzing, Timo T1 - Multiplicative Up-Drift JF - Algorithmica N2 - Drift analysis aims at translating the expected progress of an evolutionary algorithm (or more generally, a random process) into a probabilistic guarantee on its run time (hitting time). So far, drift arguments have been successfully employed in the rigorous analysis of evolutionary algorithms, however, only for the situation that the progress is constant or becomes weaker when approaching the target. Motivated by questions like how fast fit individuals take over a population, we analyze random processes exhibiting a (1+delta)-multiplicative growth in expectation. We prove a drift theorem translating this expected progress into a hitting time. This drift theorem gives a simple and insightful proof of the level-based theorem first proposed by Lehre (2011). Our version of this theorem has, for the first time, the best-possible near-linear dependence on 1/delta} (the previous results had an at least near-quadratic dependence), and it only requires a population size near-linear in delta (this was super-quadratic in previous results). These improvements immediately lead to stronger run time guarantees for a number of applications. We also discuss the case of large delta and show stronger results for this setting. KW - drift theory KW - evolutionary computation KW - stochastic process Y1 - 2020 U6 - https://doi.org/10.1007/s00453-020-00775-7 SN - 0178-4617 SN - 1432-0541 VL - 83 IS - 10 SP - 3017 EP - 3058 PB - Springer CY - New York ER - TY - JOUR A1 - Richly, Keven A1 - Schlosser, Rainer A1 - Boissier, Martin T1 - Budget-conscious fine-grained configuration optimization for spatio-temporal applications JF - Proceedings of the VLDB Endowment N2 - Based on the performance requirements of modern spatio-temporal data mining applications, in-memory database systems are often used to store and process the data. To efficiently utilize the scarce DRAM capacities, modern database systems support various tuning possibilities to reduce the memory footprint (e.g., data compression) or increase performance (e.g., additional indexes). However, the selection of cost and performance balancing configurations is challenging due to the vast number of possible setups consisting of mutually dependent individual decisions. In this paper, we introduce a novel approach to jointly optimize the compression, sorting, indexing, and tiering configuration for spatio-temporal workloads. Further, we consider horizontal data partitioning, which enables the independent application of different tuning options on a fine-grained level. We propose different linear programming (LP) models addressing cost dependencies at different levels of accuracy to compute optimized tuning configurations for a given workload and memory budgets. To yield maintainable and robust configurations, we extend our LP-based approach to incorporate reconfiguration costs as well as a worst-case optimization for potential workload scenarios. Further, we demonstrate on a real-world dataset that our models allow to significantly reduce the memory footprint with equal performance or increase the performance with equal memory size compared to existing tuning heuristics. KW - General Earth and Planetary Sciences KW - Water Science and Technology KW - Geography, Planning and Development Y1 - 2022 U6 - https://doi.org/10.14778/3565838.3565858 SN - 2150-8097 VL - 15 IS - 13 SP - 4079 EP - 4092 PB - Association for Computing Machinery (ACM) CY - [New York] ER - TY - JOUR A1 - Wittig, Alice A1 - Miranda, Fabio Malcher A1 - Hölzer, Martin A1 - Altenburg, Tom A1 - Bartoszewicz, Jakub Maciej A1 - Beyvers, Sebastian A1 - Dieckmann, Marius Alfred A1 - Genske, Ulrich A1 - Giese, Sven Hans-Joachim A1 - Nowicka, Melania A1 - Richard, Hugues A1 - Schiebenhoefer, Henning A1 - Schmachtenberg, Anna-Juliane A1 - Sieben, Paul A1 - Tang, Ming A1 - Tembrockhaus, Julius A1 - Renard, Bernhard Y. A1 - Fuchs, Stephan T1 - CovRadar BT - continuously tracking and filtering SARS-CoV-2 mutations for genomic surveillance JF - Bioinformatics N2 - The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of genomic surveillance to understand the evolution of the virus, to monitor the viral population, and plan epidemiological responses. Detailed analysis, easy visualization and intuitive filtering of the latest viral sequences are powerful for this purpose. We present CovRadar, a tool for genomic surveillance of the SARS-CoV-2 Spike protein. CovRadar consists of an analytical pipeline and a web application that enable the analysis and visualization of hundreds of thousand sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants and consensus and finally presents the results in an interactive app, making accessing and reporting simple, flexible and fast. Y1 - 2022 U6 - https://doi.org/10.1093/bioinformatics/btac411 SN - 1367-4803 SN - 1367-4811 VL - 38 IS - 17 SP - 4223 EP - 4225 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Bläsius, Thomas A1 - Friedrich, Tobias A1 - Lischeid, Julius A1 - Meeks, Kitty A1 - Schirneck, Friedrich Martin T1 - Efficiently enumerating hitting sets of hypergraphs arising in data profiling JF - Journal of computer and system sciences : JCSS N2 - The transversal hypergraph problem asks to enumerate the minimal hitting sets of a hypergraph. If the solutions have bounded size, Eiter and Gottlob [SICOMP'95] gave an algorithm running in output-polynomial time, but whose space requirement also scales with the output. We improve this to polynomial delay and space. Central to our approach is the extension problem, deciding for a set X of vertices whether it is contained in any minimal hitting set. We show that this is one of the first natural problems to be W[3]-complete. We give an algorithm for the extension problem running in time O(m(vertical bar X vertical bar+1) n) and prove a SETH-lower bound showing that this is close to optimal. We apply our enumeration method to the discovery problem of minimal unique column combinations from data profiling. Our empirical evaluation suggests that the algorithm outperforms its worst-case guarantees on hypergraphs stemming from real-world databases. KW - Data profiling KW - Enumeration algorithm KW - Minimal hitting set KW - Transversal hypergraph KW - Unique column combination KW - W[3]-Completeness Y1 - 2022 U6 - https://doi.org/10.1016/j.jcss.2021.10.002 SN - 0022-0000 SN - 1090-2724 VL - 124 SP - 192 EP - 213 PB - Elsevier CY - San Diego ER - TY - JOUR A1 - Bin Tareaf, Raad A1 - Berger, Philipp A1 - Hennig, Patrick A1 - Meinel, Christoph T1 - Cross-platform personality exploration system for online social networks BT - Facebook vs. Twitter JF - Web intelligence N2 - Social networking sites (SNS) are a rich source of latent information about individual characteristics. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, commercial brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. Predictive evaluation on brands' accounts reveals that Facebook platform provides a slight advantage over Twitter platform in offering more self-disclosure for users' to express their emotions especially their demographic and psychological traits. Results also confirm the wider perspective that the same social media account carry a quite similar and comparable personality scores over different social media platforms. For evaluating our prediction results on actual brands' accounts, we crawled the Facebook API and Twitter API respectively for 100k posts from the most valuable brands' pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions. KW - Big Five model KW - personality prediction KW - brand personality KW - machine KW - learning KW - social media analysis Y1 - 2020 U6 - https://doi.org/10.3233/WEB-200427 SN - 2405-6456 SN - 2405-6464 VL - 18 IS - 1 SP - 35 EP - 51 PB - IOS Press CY - Amsterdam ER -