Bonn Aachen International Center for Information Technology

Computer algebra systems are now ubiquitous in all areas of science and engineering. This highly successful textbook, widely regarded as the 'bible of computer algebra', gives a thorough introduction to the algorithmic basis of the mathematical engine in computer algebra systems. Designed to accompany one- or two-semester courses for advanced undergraduate or graduate students in computer science or mathematics, its comprehensiveness and reliability has also made it an essential reference for professionals in the area. Special features include: detailed study of algorithms including time analysis; implementation reports on several topics; complete proofs of the mathematical underpinnings; and a wide variety of applications (among others, in chemistry, coding theory, cryptography, computational logic, and the design of calendars and musical scales). A great deal of historical information and illustration enlivens the text. In this third edition, errors have been corrected and much of the Fast Euclidean Algorithm chapter has been renovated.

BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.

A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used to map molecule structures into a continuous latent space and vice versa and their performance as structure generator was assessed. Our results show that the latent space preserves chemical similarity principle and thus can be used for the generation of analogue structures. Furthermore, the latent space created by autoencoders were searched systematically to generate novel compounds with predicted activity against dopamine receptor type 2 and compounds similar to known active compounds not included in the trainings set were identified.

We propose a simple yet effective detector for pedestrian detection. The basic idea is to incorporate common sense and everyday knowledge into the design of simple and computationally efficient features. As pedestrians usually appear up-right in image or video data, the problem of pedestrian detection is considerably simpler than general purpose people detection. We therefore employ a statistical model of the up-right human body where the head, the upper body, and the lower body are treated as three distinct components. Our main contribution is to systematically design a pool of rectangular templates that are tailored to this shape model. As we incorporate different kinds of low-level measurements, the resulting multi-modal & multi-channel Haar-like features represent characteristic differences between parts of the human body yet are robust against variations in clothing or environmental settings. Our approach avoids exhaustive searches over all possible configurations of rectangle features and neither relies on random sampling. It thus marks a middle ground among recently published techniques and yields efficient low-dimensional yet highly discriminative features. Experimental results on the INRIA and Caltech pedestrian datasets show that our detector reaches state-of-the-art performance at low computational costs and that our features are robust against occlusions.

Brown adipocytes are a primary site of energy expenditure and reside not only in classical brown adipose tissue but can also be found in white adipose tissue. Here we show that microRNA 155 is enriched in brown adipose tissue and is highly expressed in proliferating brown preadipocytes but declines after induction of differentiation. Interestingly, microRNA 155 and its target, the adipogenic transcription factor CCAAT/enhancer-binding protein β, form a bistable feedback loop integrating hormonal signals that regulate proliferation or differentiation. Inhibition of microRNA 155 enhances brown adipocyte differentiation and induces a brown adipocyte-like phenotype (‘browning’) in white adipocytes. Consequently, microRNA 155-deficient mice exhibit increased brown adipose tissue function and ‘browning’ of white fat tissue. In contrast, transgenic overexpression of microRNA 155 in mice causes a reduction of brown adipose tissue mass and impairment of brown adipose tissue function. These data demonstrate that the bistable loop involving microRNA 155 and CCAAT/enhancer-binding protein β regulates brown lineage commitment, thereby, controlling the development of brown and beige fat cells. Brown fat can dissipate energy as heat and has an important role in energy homoeostasis of rodents and possibly humans. Chenet al. show that microRNA 155 regulates the differentiation of brown adipocytes as well as the 'browning' of white fat cells in mice.

Brown adipose tissue (BAT) dissipates energy and its activity correlates with leanness in human adults. (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography coupled with computer tomography (PET/CT) is still the standard for measuring BAT activity, but exposes subjects to ionizing radiation. To study BAT function in large human cohorts, novel diagnostic tools are needed. Here we show that brown adipocytes release exosomes and that BAT activation increases exosome release. Profiling miRNAs in exosomes released from brown adipocytes, and in exosomes isolated from mouse serum, we show that levels of miRNAs change after BAT activation in vitro and in vivo. One of these exosomal miRNAs, miR-92a, is also present in human serum exosomes. Importantly, serum concentrations of exosomal miR-92a inversely correlate with human BAT activity measured by (18)F-FDG PET/CT in two unique and independent cohorts comprising 41 healthy individuals. Thus, exosomal miR-92a represents a potential serum biomarker for BAT activity in mice and humans.

An increasing interest in a healthy lifestyle raises questions about optimal body weight. Evidently, it should be clearly discriminated between the standardised "normal" body weight and individually optimal weight. To this end, the basic principle of personalised medicine "one size does not fit all" has to be applied. Contextually, "normal" but e.g. borderline body mass index might be optimal for one person but apparently suboptimal for another one strongly depending on the individual genetic predisposition, geographic origin, cultural and nutritional habits and relevant lifestyle parameters-all included into comprehensive individual patient profile. Even if only slightly deviant, both overweight and underweight are acknowledged risk factors for a shifted metabolism which, if being not optimised, may strongly contribute to the development and progression of severe pathologies. Development of innovative screening programmes is essential to promote population health by application of health risks assessment, individualised patient profiling and multi-parametric analysis, further used for cost-effective targeted prevention and treatments tailored to the person. The following healthcare areas are considered to be potentially strongly benefiting from the above proposed measures: suboptimal health conditions, sports medicine, stress overload and associated complications, planned pregnancies, periodontal health and dentistry, sleep medicine, eye health and disorders, inflammatory disorders, healing and pain management, metabolic disorders, cardiovascular disease, cancers, psychiatric and neurologic disorders, stroke of known and unknown aetiology, improved individual and population outcomes under pandemic conditions such as COVID-19. In a long-term way, a significantly improved healthcare economy is one of benefits of the proposed paradigm shift from reactive to Predictive, Preventive and Personalised Medicine (PPPM/3PM). A tight collaboration between all stakeholders including scientific community, healthcare givers, patient organisations, policy-makers and educators is essential for the smooth implementation of 3PM concepts in daily practice.

MOTIVATION: Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools. RESULTS: We present an IUPAC name recognizer with an F(1) measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F(1) measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run. AVAILABILITY: We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component.

data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

With improvements in computer speed and algorithm efficiency, MD simulations are sampling larger amounts of molecular and biomolecular conformations. Being able to qualitatively and quantitatively sift these conformations into meaningful groups is a difficult and important task, especially when considering the structure-activity paradigm. Here we present a study that combines two popular techniques, principal component (PC) analysis and clustering, for revealing major conformational changes that occur in molecular dynamics (MD) simulations. Specifically, we explored how clustering different PC subspaces effects the resulting clusters versus clustering the complete trajectory data. As a case example, we used the trajectory data from an explicitly solvated simulation of a bacteria’s L11·23S ribosomal subdomain, which is a target of thiopeptide antibiotics. Clustering was performed, using K-means and average-linkage algorithms, on data involving the first two to the first five PC subspace dimensions. For the average-linkage algorithm we found that data-point membership, cluster shape, and cluster size depended on the selected PC subspace data. In contrast, K-means provided very consistent results regardless of the selected subspace. Since we present results on a single model system, generalization concerning the clustering of different PC subspaces of other molecular systems is currently premature. However, our hope is that this study illustrates a) the complexities in selecting the appropriate clustering algorithm, b) the complexities in interpreting and validating their results, and c) by combining PC analysis with subsequent clustering valuable dynamic and conformational information can be obtained.

SUMMARY: The COVID-19 crisis has elicited a global response by the scientific community that has led to a burst of publications on the pathophysiology of the virus. However, without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats. AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Biomedical ontologies offer the capability to structure and represent domain-specific knowledge semantically. Disease-specific ontologies can facilitate knowledge exchange across multiple disciplines, and ontology-driven mining approaches can generate great value for modeling disease mechanisms. However, in the case of neurodegenerative diseases such as Alzheimer's disease, there is a lack of formal representation of the relevant knowledge domain. METHODS: Alzheimer's disease ontology (ADO) is constructed in accordance to the ontology building life cycle. The Protégé OWL editor was used as a tool for building ADO in Ontology Web Language format. RESULTS: ADO was developed with the purpose of containing information relevant to four main biological views-preclinical, clinical, etiological, and molecular/cellular mechanisms-and was enriched by adding synonyms and references. Validation of the lexicalized ontology by means of named entity recognition-based methods showed a satisfactory performance (F score = 72%). In addition to structural and functional evaluation, a clinical expert in the field performed a manual evaluation and curation of ADO. Through integration of ADO into an information retrieval environment, we show that the ontology supports semantic search in scientific text. The usefulness of ADO is authenticated by dedicated use case scenarios. CONCLUSIONS: Development of ADO as an open ADO is a first attempt to organize information related to Alzheimer's disease in a formalized, structured manner. We demonstrate that ADO is able to capture both established and scattered knowledge existing in scientific text.

Accurate and individualized prediction of response to therapies is central to precision medicine. However, because of the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring integrating different data types from the same individual into one prediction model. We used the anti-epileptic drug brivaracetam as a case study and combine a hybrid data/knowledge-driven feature extraction with machine learning to systematically integrate clinical and genetic data from a clinical discovery dataset (n = 235 patients). We constructed a model that successfully predicts clinical drug response [area under the curve (AUC) = 0.76] and show that even with limited sample size, integrating high-dimensional genetics data with clinical data can inform drug response prediction. After further validation on data collected from an independently conducted clinical study (AUC = 0.75), we extensively explore our model to gain insights into the determinants of drug response, and identify various clinical and genetic characteristics predisposing to poor response. Finally, we assess the potential impact of our model on clinical trial design and demonstrate that, by enriching for probable responders, significant reductions in clinical study sizes may be achieved. To our knowledge, our model represents the first retrospectively validated machine learning model linking drug mechanism of action and the genetic, clinical and demographic background in epilepsy patients to clinical drug response. Hence, it provides a blueprint for how machine learning-based multimodal data integration can act as a driver in achieving the goals of precision medicine in fields such as neurology.

BACKGROUND: Increasing gene dosages of α-synuclein induce familial Parkinson's disease (PD); thus, the hypothesis has been put forward that regulation of gene expression, in particular altered α-synuclein gene methylation, might be associated with sporadic PD and could be used as a biological marker. METHODS: We performed a thorough analysis of α-synuclein methylation in bisulfite-treated DNA from peripheral blood of 490 sporadic PD patients and 485 healthy controls and in addition analyzed the effect of levodopa (L-dopa) on α-synuclein methylation and expression in cultured mononuclear cells. RESULTS: α-Synuclein was hypomethylated in sporadic PD patients, correlated with sex, age, and a polymorphism in the analyzed sequence stretch (rs3756063). α-Synuclein methylation separated healthy individuals from sporadic PD with a specificity of 74% (male) and 78% (female), respectively. α-Synuclein methylation was increased in sporadic PD patients with higher l-dopa dosage, and L-dopa specifically induced methylation of α-synuclein intron 1 in cultured mononuclear cells. CONCLUSIONS: α-Synuclein methylation levels depended on disease status, sex, age, and the genotype of rs3756063. The pharmacological action of L-dopa was not limited to the dopamine precursor function but included epigenetic off-target effects. The hypomethylation of α-synuclein in sporadic PD patients' blood already observed in previous studies was probably underestimated because of effect of L-dopa, which was not known previously. The analysis of α-synuclein methylation can help to identify nonparkinsonian individuals with reasonable specificity, which offers a valuable tool for researchers and clinicians.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

We start by discussing the computer representation and fundamental arithmetic algorithms for integers and polynomials. We will keep this discussion fairly informal and avoid all the intricacies of actual computer arithmetic—that is a topic on its own. The reader must be warned that modern-day processors do not represent numbers and operate on them as we describe now, but to describe the tricks they use would detract us from our current goal: a simple description of how one could, in principle, perform basic arithmetic.

Today, RFID enjoys enormous interest as the first widely deployed pervasive technology, and Near Field Communication will be the first widely deployed technology enabling humans to communicate with physical objects. This article reports on the Pertec (Pervasive RFID/Near Field Communication Technology and Applications) workshop, which discussed the future evolution of RFID beyond goods identification, including sensor integration, localization, NFC applications, and emerging challenges.

BACKGROUND: Abnormal sex chromosome numbers in humans are observed in Turner (45,X) and Klinefelter (47,XXY) syndromes. Both syndromes are associated with several clinical phenotypes, whose molecular mechanisms are obscure, and show a range of inter-individual penetrance. In order to understand the effect of abnormal numbers of X chromosome on the methylome and its correlation to the variable clinical phenotype, we performed a genome-wide methylation analysis using MeDIP and Illumina's Infinium assay on individuals with four karyotypes: 45,X, 46,XY, 46,XX, and 47,XXY. RESULTS: DNA methylation changes were widespread on all autosomal chromosomes in 45,X and in 47,XXY individuals, with Turner individuals presenting five times more affected loci. Differentially methylated CpGs, in most cases, have intermediate methylation levels and tend to occur outside CpG islands, especially in individuals with Turner syndrome. The X inactivation process appears to be less effective in Klinefelter syndrome as methylation on the X was decreased compared to normal female samples. In a large number of individuals, we verified several loci by pyrosequencing and observed only weak inter-loci correlations between the verified regions. This suggests a certain stochastic/random contribution to the methylation changes at each locus. Interestingly, methylation patterns on some PAR2 loci differ between male and Turner syndrome individuals and between female and Klinefelter syndrome individuals, which possibly contributed to this distinguished and unique autosomal methylation patterns in Turner and Klinefelter syndrome individuals. CONCLUSIONS: The presented data clearly show that gain or loss of an X chromosome results in different epigenetic effects, which are not necessary opposite.

Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence (AI) field. The transformer model is a type of DNN that was originally used for the natural language processing tasks and has since gained more and more attention for processing various kinds of sequential data, including biological sequences and structured electronic health records. Along with this development, transformer-based models such as BioBERT, MedBERT, and MassGenie have been trained and deployed by researchers to answer various scientific questions originating in the biomedical domain. In this paper, we review the development and application of transformer models for analyzing various biomedical-related datasets such as biomedical textual data, protein sequences, medical structured-longitudinal data, and biomedical images as well as graphs. Also, we look at explainable AI strategies that help to comprehend the predictions of transformer-based models. Finally, we discuss the limitations and challenges of current models, and point out emerging novel research directions.

BACKGROUND: Immunotherapy, including checkpoint inhibition, has remarkably improved prognosis in advanced melanoma. Despite this success, acquired resistance is still a major challenge. The T cell costimulatory receptor TNFRSF9 (also known as 4-1BB and CD137) is a promising new target for immunotherapy and two agonistic antibodies are currently tested in clinical trials. However, little is known about epigenetic regulation of the encoding gene. In this study we investigate a possible correlation of TNFRSF9 DNA methylation with gene expression, clinicopathological parameters, molecular and immune correlates, and response to anti-PD-1 immunotherapy to assess the validity of TNFRSF9 methylation to serve as a biomarker. METHODS: We performed a correlation analyses of methylation at twelve CpG sites within TNFRSF9 with regard to transcriptional activity, immune cell infiltration, mutation status, and survival in a cohort of N = 470 melanoma patients obtained from The Cancer Genome Atlas. Furthermore, we used quantitative methylation-specific PCR to confirm correlations in a cohort of N = 115 melanoma patients' samples (UHB validation cohort). Finally, we tested the ability of TNFRSF9 methylation and expression to predict progression-free survival (PFS) and response to anti-PD-1 immunotherapy in a cohort comprised of N = 121 patients (mRNA transcription), (mRNA ICB cohort) and a case-control study including N = 48 patients (DNA methylation, UHB ICB cohort). FINDINGS: We found a significant inverse correlation between TNFRSF9 DNA methylation and mRNA expression levels at six of twelve analyzed CpG sites (P ≤ 0.005), predominately located in the promoter flank region. Consistent with its role as costimulatory receptor in immune cells, TNFRSF9 mRNA expression and hypomethylation positively correlated with immune cell infiltrates and an interferon-γ signature. Furthermore, elevated TNFRSF9 mRNA expression and TNFRSF9 hypomethylation correlated with superior overall survival. In patients receiving anti-PD-1 immunotherapy (mRNA ICB cohort), we found that TNFRSF9 hypermethylation and reduced mRNA expression correlated with poor PFS and response. INTERPRETATION: Our study suggests that TNFRSF9 mRNA expression is regulated via DNA methylation. The observed correlations between TNFRSF9 DNA methylation or mRNA expression with known features of response to immune checkpoint blockage suggest TNFRSF9 methylation could serve as a biomarker in the context of immunotherapies. Concordantly, we identified a correlation between TNFRSF9 DNA methylation and mRNA expression with disease progression in patients under immunotherapy. Our study provides rationale for further investigating TNFRSF9 DNA methylation as a predictive biomarker for response to immunotherapy. FUNDING: AF was partly funded by the Mildred Scheel Foundation. SF received funding from the University Hospital Bonn BONFOR program (O-105.0069). DN was funded in part by DFG Cluster of Excellence ImmunoSensation (EXC 1023). The funders had no role in study design, data collection and analysis, interpretation, decision to publish, or preparation of the manuscript; or any aspect pertinent to the study.