TY - JOUR A1 - Conrad, Tim A1 - Leichtle, Alexander Benedikt A1 - Ceglarek, Uta A1 - Weinert, P. A1 - Nakas, C.T. A1 - Nuoffer, Jean-Marc A1 - Kase, Julia A1 - Witzigmann, Helmut A1 - Thiery, Joachim A1 - Fiedler, Georg Martin T1 - Pancreatic carcinoma, pancreatitis, and healthy controls - metabolite models in a three-class diagnostic dilemma JF - Metabolomics N2 - Background: Metabolomics as one of the most rapidly growing technologies in the ?-omics?field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput massspectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. Methods: We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels.  Results: Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Volume under ROC surface (VUS) = 0.891 (95\% CI 0.794 - 0.968)]. Conclusions: We combined highly standardized samples, a three-class study design, a highthroughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and ?despite all its current limitations? can deliver marker panels with high selectivity even in multi-class settings. Y1 - 2013 U6 - https://doi.org/10.1007/s11306-012-0476-7 VL - 9 IS - 3 SP - 677 EP - 687 ER - TY - JOUR A1 - Gupta, Pooja A1 - Reinsch, Norbert A1 - Spötter, Andreas A1 - Conrad, Tim A1 - Bienefeld, Kaspar T1 - Accuracy of the unified approach in maternally influenced traits - illustrated by a simulation study in the honey bee (Apis mellifera) JF - BMC Genetics Y1 - 2013 U6 - https://doi.org/10.1186/1471-2156-14-36 VL - 14 IS - 36 ER - TY - JOUR A1 - Conrad, Tim A1 - You, Xintian T1 - Acfs: accurate circRNA identification and quantification from NGS data JF - Nature Scientific Reports N2 - Circular RNAs (circRNAs) are a group of single-stranded RNAs in closed circular form. They are splicing-generated, widely expressed in various tissues and have functional implications in development and diseases. To facilitate genome-wide characterization of circRNAs using RNA-Seq data, we present a freely available software package named acfs. Acfs allows de novo, accurate and fast identification and abundance quantification of circRNAs from single- and paired-ended RNA-Seq data. On simulated datasets, acfs achieved the highest F1 accuracy and lowest false discovery rate among current state-of-the-art tools. On real-world datasets, acfs efficiently identified more bona fide circRNAs. Furthermore, we demonstrated the power of circRNA analysis on two leukemia datasets. We identified a set of circRNAs that are differentially expressed between AML and APL samples, which might shed light on the potential molecular classification of complex diseases using circRNA profiles. Moreover, chromosomal translocation, as manifested in numerous diseases, could produce not only fusion transcripts but also fusion circRNAs of clinical relevance. Featured with high accuracy, low FDR and the ability to identify fusion circRNAs, we believe that acfs is well suited for a wide spectrum of applications in characterizing the landscape of circRNAs from non-model organisms to cancer biology. Y1 - 2016 U6 - https://doi.org/10.1038/srep38820 VL - 6 ER - TY - JOUR A1 - Mireles, Victor A1 - Conrad, Tim T1 - Decomposing biological systems into reusable modules reveals characteristic module size distributions N2 - One of the widely recognized features of biological systems is their modularity. The modules that comprise biological systems are said to be redeployed and combined across several conditions. In this work, we analyze to what extent are these modules indeed reusable as compared to randomized versions of a system. We develop a notion of modular decompositions of systems that allows for modules to overlap while maximizing the number of times a module is reused across several conditions. Different biological systems present modules whose reusability ranges from the condition specific to the constitutive, although their average reusability is not always higher than random equivalents of the system. These decompositions reveal a distinct distribution of module sizes in real biological systems. This distribution stems, in part, from the peculiar usage pattern of the elements of biological systems, and constitutes a new angle to study the evolution of modularity. Y1 - 2016 ER - TY - JOUR A1 - Hoppe, Christian A1 - Obermeier, Patrick A1 - Mehlhans, S. A1 - Alchikh, Maren A1 - Seeber, L. A1 - Tief, Franziska A1 - Karsch, K. A1 - Chen, X. A1 - Boettcher, Sindy A1 - Diedrich, S. A1 - Conrad, Tim T1 - Innovative Digital Tools and Surveillance Systems for the Timely Detection of Adverse Events at the Point of Care: A Proof-of-Concept Study JF - Drug Safety N2 - Regulatory authorities often receive poorly structured safety reports requiring considerable effort to investigate potential adverse events post hoc. Automated question-and-answer systems may help to improve the overall quality of safety information transmitted to pharmacovigilance agencies. This paper explores the use of the VACC-Tool (ViVI Automated Case Classification Tool) 2.0, a mobile application enabling physicians to classify clinical cases according to 14 pre-defined case definitions for neuroinflammatory adverse events (NIAE) and in full compliance with data standards issued by the Clinical Data Interchange Standards Consortium. METHODS: The validation of the VACC-Tool 2.0 (beta-version) was conducted in the context of a unique quality management program for children with suspected NIAE in collaboration with the Robert Koch Institute in Berlin, Germany. The VACC-Tool was used for instant case classification and for longitudinal follow-up throughout the course of hospitalization. Results were compared to International Classification of Diseases , Tenth Revision (ICD-10) codes assigned in the emergency department (ED). RESULTS: From 07/2013 to 10/2014, a total of 34,368 patients were seen in the ED, and 5243 patients were hospitalized; 243 of these were admitted for suspected NIAE (mean age: 8.5 years), thus participating in the quality management program. Using the VACC-Tool in the ED, 209 cases were classified successfully, 69 \% of which had been missed or miscoded in the ED reports. Longitudinal follow-up with the VACC-Tool identified additional NIAE. CONCLUSION: Mobile applications are taking data standards to the point of care, enabling clinicians to ascertain potential adverse events in the ED setting and during inpatient follow-up. Compliance with Clinical Data Interchange Standards Consortium (CDISC) data standards facilitates data interoperability according to regulatory requirements. Y1 - 2016 U6 - https://doi.org/10.1007/s40264-016-0437-6 VL - 39 IS - 10 SP - 977 EP - 988 ER - TY - CHAP A1 - Shao, Borong A1 - Conrad, Tim T1 - Epithelial Mesenchymal Transition Regulatory Network-based Feature Selection in Lung Cancer Prognosis Prediction T2 - Lecture Notes in Computer Science (LNCS) N2 - Feature selection technique is often applied in identifying cancer prognosis biomarkers. However, many feature selection methods are prone to over-fitting or poor biological interpretation when applied on biological high-dimensional data. Network-based feature selection and data integration approaches are proposed to identify more robust biomarkers. We conducted experiments to investigate the advantages of the two approaches using epithelial mesenchymal transition regulatory network, which is demonstrated as highly relevant to cancer prognosis. We obtained data from The Cancer Genome Atlas. Prognosis prediction was made using Support Vector Machine. Under our experimental settings, the results showed that network-based features gave significantly more accurate predictions than individual molecular features, and features selected from integrated data (RNA-Seq and micro-RNA data) gave significantly more accurate predictions than features selected from single source data (RNA-Seq data). Our study indicated that biological network-based feature transformation and data integration are two useful approaches to identify robust cancer biomarkers. Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-31744-1_13 VL - 9656 SP - 1235 EP - 146 ER - TY - JOUR A1 - Tief, Franziska A1 - Hoppe, Christian A1 - Seeber, L. A1 - Obermeier, Patrick A1 - Chen, X. A1 - Karsch, K. A1 - Muehlhans, S. A1 - Adamou, E. A1 - Conrad, Tim A1 - Schweiger, Brunhilde A1 - Adam, T. A1 - Rath, Barbara T1 - An inception cohort study assessing the role of bacterial co-infections in children with influenza and ILI and a clinical decision model for stringent antibiotic use JF - Antiviral Therapy N2 - BACKGROUND: Influenza-like illness (ILI) is a common reason for paediatric consultations. Viral causes predominate, but antibiotics are used frequently. With regard to influenza, pneumococcal coinfections are considered major contributors to morbidity/mortality. METHODS: In the context of a perennial quality management (QM) programme at the Charit{\'e} Departments of Paediatrics and Microbiology in collaboration with the Robert Koch Institute, children aged 0-18 years presenting with signs and symptoms of ILI were followed from the time of initial presentation until hospital discharge (Charit{\'e} Influenza-Like Disease = ChILD Cohort). An independent QM team performed highly standardized clinical assessments using a disease severity score based on World Health Organization criteria for uncomplicated and complicated/progressive disease. Nasopharyngeal and pharyngeal samples were collected for viral reverse transcription polymerase chain reaction and bacterial culture/sensitivity and MaldiTOF analyses. The term 'detection' was used to denote any evidence of viral or bacterial pathogens in the (naso)pharyngeal cavity. With the ChILD Cohort data collected, a standard operating procedure (SOP) was created as a model system to reduce the inappropriate use of antibiotics in children with ILI. Monte Carlo simulations were performed to assess cost-effectiveness. RESULTS: Among 2,569 ChILD Cohort patients enrolled from 12/2010 to 04/2013 (55\% male, mean age 3.2 years, range 0-18, 19\% {\ensuremath{>}}5 years), 411 patients showed laboratory-confirmed influenza, with bacterial co-detection in 35\%. Influenza and pneumococcus were detected simultaneously in 12/2,569 patients, with disease severity clearly below average. Pneumococcal vaccination rates were close to 90\%. Nonetheless, every fifth patient was already on antibiotics upon presentation; new antibiotic prescriptions were issued in an additional 20\%. Simulation of the model SOP in the same dataset revealed that the proposed decision model could have reduced the inappropriate use of antibiotics significantly (P{\ensuremath{<}}0.01) with an incremental cost-effectiveness ratio of -99.55?. CONCLUSIONS: Physicians should be made aware that in times of pneumococcal vaccination the prevalence and severity of influenza infections complicated by pneumococci may decline. Microbiological testing in combination with standardized disease severity assessments and review of vaccination records could be cost-effective, as well as promoting stringent use of antibiotics and a personalized approach to managing children with ILI. Y1 - 2016 U6 - https://doi.org/10.3851/IMP3034 VL - 21 SP - 413 EP - 424 ER - TY - JOUR A1 - Obermeier, Patrick A1 - Muehlhans, S. A1 - Hoppe, Christian A1 - Karsch, K. A1 - Tief, Franziska A1 - Seeber, L. A1 - Chen, X. A1 - Conrad, Tim A1 - Boettcher, Sindy A1 - Diedrich, S. A1 - Rath, Barbara T1 - Enabling Precision Medicine With Digital Case Classification at the Point-of-Care JF - EBioMedicine N2 - Infectious and inflammatory diseases of the central nervous system are difficult to identify early. Case definitions for aseptic meningitis, encephalitis, myelitis, and acute disseminated encephalomyelitis (ADEM) are available, but rarely put to use. The VACC-Tool (Vienna Vaccine Safety Initiative Automated Case Classification-Tool) is a mobile application enabling immediate case ascertainment based on consensus criteria at the point-of-care. The VACC-Tool was validated in a quality management program in collaboration with the Robert-Koch-Institute. Results were compared to ICD-10 coding and retrospective analysis of electronic health records using the same case criteria. Of 68,921 patients attending the emergency room in 10/2010-06/2013, 11,575 were hospitalized, with 521 eligible patients (mean age: 7.6 years) entering the quality management program. Using the VACC-Tool at the point-of-care, 180/521 cases were classified successfully and 194/521 ruled out with certainty. Of the 180 confirmed cases, 116 had been missed by ICD-10 coding, 38 misclassified. By retrospective application of the same case criteria, 33 cases were missed. Encephalitis and ADEM cases were most likely missed or misclassified. The VACC-Tool enables physicians to ask the right questions at the right time, thereby classifying cases consistently and accurately, facilitating translational research. Future applications will alert physicians when additional diagnostic procedures are required. Y1 - 2016 U6 - https://doi.org/10.1016/j.ebiom.2016.01.008 VL - 4 SP - 191 EP - 196 ER - TY - JOUR A1 - Conrad, Tim A1 - Bruckner, Sharon A1 - Kayser, Bastian T1 - Finding Modules in Networks with Non-modular Regions JF - Lecture Notes in Computer Science (Proceedings of SEA 2013) N2 - Most network clustering methods share the assumption that the network can be completely decomposed into modules, that is, every node belongs to (usually exactly one) module. Forcing this constraint can lead to misidentification of modules where none exist, while the true modules are drowned out in the noise, as has been observed e.g. for protein interaction networks. We thus propose a clustering model where networks contain both a modular region consisting of nodes that can be partitioned into modules, and a transition region containing nodes that lie between or outside modules. We propose two scores based on spectral properties to determine how well a network fits this model. We then evaluate three (partially adapted) clustering algorithms from the literature on random networks that fit our model, based on the scores and comparison to the ground truth. This allows to pinpoint the types of networks for which the different algorithms perform well. Y1 - 2013 U6 - https://doi.org/10.1007/978-3-642-38527-8_18 VL - 7933 SP - 188 EP - 199 ER - TY - JOUR A1 - Conrad, Tim A1 - Rath, Barbara A1 - Tief, Franziska A1 - Karsch, K. A1 - Muehlhans, S. A1 - Obermeier, Patrick A1 - Adamou, E. A1 - Chen, X. A1 - Seeber, L. A1 - Peiser, Ch. A1 - Hoppe, Christian A1 - von Kleist, Max A1 - Schweiger, Brunhilde T1 - Towards a personalized approach to managing of influenza infections in infants and children - food for thought and a note on oseltamivir JF - Infectious Disorders - Drug Targets Y1 - 2013 VL - 13 IS - 1 SP - 25 EP - 33 ER - TY - JOUR A1 - Conrad, Tim A1 - Leichtle, Alexander Benedikt A1 - Nuoffer, Jean-Marc A1 - Ceglarek, Uta A1 - Kase, Julia A1 - Witzigmann, Helmut A1 - Thiery, Joachim A1 - Fiedler, Georg Martin T1 - Serum amino acid profiles and their alterations in colorectal cancer JF - Metabolomics N2 - Mass spectrometry-based serum metabolic profiling is a promising tool to analyse complex cancer associated metabolic alterations, which may broaden our pathophysiological understanding of the disease and may function as a source of new cancer-associated biomarkers. Highly standardized serum samples of patients suffering from colon cancer (n = 59) and controls (n = 58) were collected at the University Hospital Leipzig. We based our investigations on amino acid screening profiles using electrospray tandem-mass spectrometry. Metabolic profiles were evaluated using the Analyst 1.4.2 software. General, comparative and equivalence statistics were performed by R 2.12.2. 11 out of 26 serum amino acid concentrations were significantly different between colorectal cancer patients and healthy controls. We found a model including CEA, glycine, and tyrosine as best discriminating and superior to CEA alone with an AUROC of 0.878 (95\% CI 0.815?0.941). Our serum metabolic profiling in colon cancer revealed multiple significant disease-associated alterations in the amino acid profile with promising diagnostic power. Further large-scale studies are necessary to elucidate the potential of our model also to discriminate between cancer and potential differential diagnoses. In conclusion, serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination between colorectal cancer patients and controls. Y1 - 2012 U6 - https://doi.org/10.1007/s11306-011-0357-5 ER - TY - JOUR A1 - Gupta, Pooja A1 - Conrad, Tim A1 - Spötter, Andreas A1 - Reinsch, Norbert A1 - Bienefeld, Kaspar T1 - Simulating a base population in honey bee for molecular genetic studies JF - Genetics Selection Evolution N2 - Over the past years, reports have indicated that honey bee populations are declining and that infestation by an ecto-parasitic mite (Varroa destructor) is one of the main causes. Selective breeding of resistant bees can help to prevent losses due to the parasite, but it requires that a robust breeding program and genetic evaluation are implemented. Genomic selection has emerged as an important tool in animal breeding programs and simulation studies have shown that it yields more accurate breeding values estimates, higher genetic gain and low rates of inbreeding. Since genomic selection relies on marker data, simulations conducted on a genomic dataset are a pre-requisite before selection can be implemented. Although genomic datasets have been simulated in other species undergoing genetic evaluation, simulation of a genomic dataset specific to the honey bee is required since this species has distinct genetic and reproductive biology characteristics. Our software program was aimed at constructing a base population by simulating a random mating honey bee population. A forward-time population simulation approach was applied since it allows modeling of genetic characteristics and reproductive behavior specific to the honey bee.  Results: Our software program yielded a genomic dataset for a base population in linkage disequilibrium. In addition, information was obtained on (1) the position of markers on each chromosome, (2) allele frequency, (3) ?2 statistics for Hardy- Weinberg equilibrium, (4) a sorted list of markers with a minor allele frequency less than or equal to the input value, (5) average r2 values of linkage disequilibrium between all simulated marker loci pair for all generations and (6) average r2 value of linkage disequilibrium in the last generation for selected markers with the highest minor allele frequency. Conclusion: We developed a software program that takes into account the genetic and reproductive biology characteristics specific to the honey bee and that can be used to constitute a genomic dataset compatible with the simulation studies necessary to optimize breeding programs. The source code together with an instruction file is freely accessible at http://msproteomics.org/Research/Misc/honeybeepopulationsimulator.html Y1 - 2012 U6 - https://doi.org/10.1186/1297-9686-44-14 ER - TY - JOUR A1 - Conrad, Tim T1 - New Appraches for Visualizing and Analyzing Metabolic Pathways JF - Proceedings of the Second Australian Undergraduate Students? Computing Conference N2 - Visualizing of metabolic pathways (or networks) has been done by many differentapproaches. In this work, we implemented and tested existing graph layout algorithms, and present a new approach to lay-out medium size metabolic pathways (500-20,000 vertices) by implementing and combining three well known graph lay-out algorithms (high dimension embedding, spring-embedder preprocessing, spring-embedder), through 3D space density analysis facilitated by the Octree technique. For the analysis of the results of metabolic pathways simulations we present two new techniques: rstly, a powerful technique to visualize pathways simulation data was created to unveil and understand concentration ows through metabolic pathways. This was achieved by mapping the color encoded concentration value of every substance from each time step of the simulation to its graphical representation in the layout. By combining all resulting images (from each time step) and displaying them as a movie, many characteristics such as subnetworks, alternative routes through the network, and differences between a modied pathway and its unmodied version can be revealed. Secondly, a new method to detect co-regulated substances in metabolic pathways and to recognize differences between two versions of a pathway, was established. To do this, we transformed the simulation data into a row-based representation, color-coded these rows, and reordered them with respect to similarity by using a Genetic Algorithm variant. From the arising discrete 2-dimensional matrix consisting of concentration values, a continuous 2-dimensional fourier row function was computed. This function can be used to measure properties, such as similarities in a pathway between time steps, or substances, or to detect and evaluate differences between modied versions of the same pathway. Y1 - 2004 ER -