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101. Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: a systematic review with recommendations

Author

Friedrich, Sarah, primary, Groß, Stefan, additional, König, Inke R, additional, Engelhardt, Sandy, additional, Bahls, Martin, additional, Heinz, Judith, additional, Huber, Cynthia, additional, Kaderali, Lars, additional, Kelm, Marcus, additional, Leha, Andreas, additional, Rühl, Jasmin, additional, Schaller, Jens, additional, Scherer, Clemens, additional, Vollmer, Marcus, additional, Seidler, Tim, additional, and Friede, Tim, additional

Published
2021
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102. Convergent use of phosphatidic acid for Hepatitis C virus and SARS-CoV-2 replication organelle formation

Author

Tabata, Keisuke, primary, Prasad, Vibhu, additional, Paul, David, additional, Lee, Ji-Young, additional, Pham, Minh-Tu, additional, Twu, Woan-Ing, additional, Neufeldt, Christopher J., additional, Cortese, Mirko, additional, Cerikan, Berati, additional, Si Tran, Cong, additional, Lüchtenborg, Christian, additional, V’kovski, Philip, additional, Hörmann, Katrin, additional, Müller, André C., additional, Zitzmann, Carolin, additional, Haselmann, Uta, additional, Beneke, Jürgen, additional, Kaderali, Lars, additional, Erfle, Holger, additional, Thiel, Volker, additional, Lohmann, Volker, additional, Superti-Furga, Giulio, additional, Brügger, Britta, additional, and Bartenschlager, Ralf, additional

Published
2021
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113. Hypertension delays viral clearance and exacerbates airway hyperinflammation in patients with COVID-19

Author

Trump, Saskia, primary, Lukassen, Soeren, additional, Anker, Markus S., additional, Chua, Robert Lorenz, additional, Liebig, Johannes, additional, Thürmann, Loreen, additional, Corman, Victor Max, additional, Binder, Marco, additional, Loske, Jennifer, additional, Klasa, Christina, additional, Krieger, Teresa, additional, Hennig, Bianca P., additional, Messingschlager, Marey, additional, Pott, Fabian, additional, Kazmierski, Julia, additional, Twardziok, Sven, additional, Albrecht, Jan Philipp, additional, Eils, Jürgen, additional, Hadzibegovic, Sara, additional, Lena, Alessia, additional, Heidecker, Bettina, additional, Bürgel, Thore, additional, Steinfeldt, Jakob, additional, Goffinet, Christine, additional, Kurth, Florian, additional, Witzenrath, Martin, additional, Völker, Maria Theresa, additional, Müller, Sarah Dorothea, additional, Liebert, Uwe Gerd, additional, Ishaque, Naveed, additional, Kaderali, Lars, additional, Sander, Leif-Erik, additional, Drosten, Christian, additional, Laudi, Sven, additional, Eils, Roland, additional, Conrad, Christian, additional, Landmesser, Ulf, additional, and Lehmann, Irina, additional

Published
2020
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115. Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

Author

Hufsky, Franziska, primary, Lamkiewicz, Kevin, additional, Almeida, Alexandre, additional, Aouacheria, Abdel, additional, Arighi, Cecilia, additional, Bateman, Alex, additional, Baumbach, Jan, additional, Beerenwinkel, Niko, additional, Brandt, Christian, additional, Cacciabue, Marco, additional, Chuguransky, Sara, additional, Drechsel, Oliver, additional, Finn, Robert D, additional, Fritz, Adrian, additional, Fuchs, Stephan, additional, Hattab, Georges, additional, Hauschild, Anne-Christin, additional, Heider, Dominik, additional, Hoffmann, Marie, additional, Hölzer, Martin, additional, Hoops, Stefan, additional, Kaderali, Lars, additional, Kalvari, Ioanna, additional, von Kleist, Max, additional, Kmiecinski, Renó, additional, Kühnert, Denise, additional, Lasso, Gorka, additional, Libin, Pieter, additional, List, Markus, additional, Löchel, Hannah F, additional, Martin, Maria J, additional, Martin, Roman, additional, Matschinske, Julian, additional, McHardy, Alice C, additional, Mendes, Pedro, additional, Mistry, Jaina, additional, Navratil, Vincent, additional, Nawrocki, Eric P, additional, O’Toole, Áine Niamh, additional, Ontiveros-Palacios, Nancy, additional, Petrov, Anton I, additional, Rangel-Pineros, Guillermo, additional, Redaschi, Nicole, additional, Reimering, Susanne, additional, Reinert, Knut, additional, Reyes, Alejandro, additional, Richardson, Lorna, additional, Robertson, David L, additional, Sadegh, Sepideh, additional, Singer, Joshua B, additional, Theys, Kristof, additional, Upton, Chris, additional, Welzel, Marius, additional, Williams, Lowri, additional, and Marz, Manja, additional

Published
2020
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117. Delayed viral clearance and exacerbated airway hyperinflammation in hypertensive COVID-19 patients

Author

Trump, Saskia, primary, Lukassen, Soeren, additional, Anker, Markus S., additional, Chua, Robert Lorenz, additional, Liebig, Johannes, additional, Thürmann, Loreen, additional, Corman, Victor, additional, Binder, Marco, additional, Loske, Jennifer, additional, Klasa, Christina, additional, Krieger, Teresa, additional, Hennig, Bianca P., additional, Messingschlager, Marey, additional, Pott, Fabian, additional, Kazmierski, Julia, additional, Twardziok, Sven, additional, Albrecht, Jan Philipp, additional, Eils, Jürgen, additional, Hadzibegovic, Sara, additional, Lena, Alessia, additional, Heidecker, Bettina, additional, Goffinet, Christine, additional, Kurth, Florian, additional, Witzenrath, Martin, additional, Völker, Maria Theresa, additional, Müller, Sarah Dorothea, additional, Liebert, Uwe Gerd, additional, Ishaque, Naveed, additional, Kaderali, Lars, additional, Sander, Leif-Erik, additional, Laudi, Sven, additional, Drosten, Christian, additional, Eils, Roland, additional, Conrad, Christian, additional, Landmesser, Ulf, additional, and Lehmann, Irina, additional

Published
2020
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120. Interaction of Staphylococcus aureus and Host Cells upon Infection of Bronchial Epithelium during Different Stages of Regeneration

Author

Palma Medina, Laura M., primary, Becker, Ann-Kristin, additional, Michalik, Stephan, additional, Surmann, Kristin, additional, Hildebrandt, Petra, additional, Gesell Salazar, Manuela, additional, Mekonnen, Solomon A., additional, Kaderali, Lars, additional, Völker, Uwe, additional, and van Dijl, Jan Maarten, additional

Published
2020
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129. Stimulation of soluble guanylyl cyclase (sGC) by riociguat attenuates heart failure and pathological cardiac remodelling.

Author

Rüdebusch, Julia, Benkner, Alexander, Nath, Neetika, Fleuch, Lina, Kaderali, Lars, Grube, Karina, Klingel, Karin, Eckstein, Gertrud, Meitinger, Thomas, Fielitz, Jens, and Felix, Stephan B.

Subjects
GUANYLATE cyclase, HEART failure, CARDIAC hypertrophy, RNA sequencing, CELL communication
Abstract

Background and Purpose: Heart failure is associated with an impaired NO–soluble guanylyl cyclase (sGC)–cGMP pathway and its augmentation is thought to be beneficial for its therapy. We hypothesized that stimulation of sGC by the sGC stimulator riociguat prevents pathological cardiac remodelling and heart failure in response to chronic pressure overload. Experimental Approach Transverse aortic constriction or sham surgery was performed in C57BL/6N mice. After 3 weeks of transverse aortic constriction when heart failure was established, animals receive either riociguat or its vehicle for 5 additional weeks. Cardiac function was evaluated weekly by echocardiography. Eight weeks after surgery, histological analyses were performed to evaluate remodelling and the transcriptome of the left ventricles (LVs) was analysed by RNA sequencing. Cell culture experiments were used for mechanistically studies. Key Results: Transverse aortic constriction resulted in a continuous decrease of LV ejection fraction and an increase in LV mass until week 3. Five weeks of riociguat treatment resulted in an improved LV ejection fraction and a decrease in the ratio of left ventricular mass to total body weight (LVM/BW), myocardial fibrosis and myocyte cross‐sectional area. RNA sequencing revealed that riociguat reduced the expression of myocardial stress and remodelling genes (e.g. Nppa, Nppb, Myh7 and collagen) and attenuated the activation of biological pathways associated with cardiac hypertrophy and heart failure. Riociguat reversed pathological stress response in cultivated myocytes and fibroblasts. Conclusion and Implications: Stimulation of the sGC reverses transverse aortic constriction‐induced heart failure and remodelling, which is associated with improved myocardial gene expression. LINKED ARTICLES: This article is part of a themed issue on cGMP Signalling in Cell Growth and Survival. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v179.11/issuetoc [ABSTRACT FROM AUTHOR]

Published
2022
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130. Genome-wide DNA alterations in X-iIrradiated human gingiva fibroblasts

Author

Nath, Neetika, Hagenau, Lisa, Weiss, Stefan, Tzvetkova, Ana, Jensen, Lars R., Kaderali, Lars, Port, Matthias, Scherthan, Harry, and Kuss, Andreas W.

Subjects
IR-induced variants, InDels, DNA repair, Translocation, Genetic, Translokation, DDC 570 / Life sciences, Radiation dosimetry, INDEL Mutation, ddc:570, SNVs, ddc:610, transition transversion ratio, topological associating domains, DDC 610 / Medicine & health, repair mechanism
Abstract

While ionizing radiation (IR) is a powerful tool in medical diagnostics, nuclear medicine, and radiology, it also is a serious threat to the integrity of genetic material. Mutagenic effects of IR to the human genome have long been the subject of research, yet still comparatively little is known about the genome-wide effects of IR exposure on the DNA-sequence level. In this study, we employed high throughput sequencing technologies to investigate IR-induced DNA alterations in human gingiva fibroblasts (HGF) that were acutely exposed to 0.5, 2, and 10 Gy of 240 kV X-radiation followed by repair times of 16 h or 7 days before whole-genome sequencing (WGS). Our analysis of the obtained WGS datasets revealed patterns of IR-induced variant (SNV and InDel) accumulation across the genome, within chromosomes as well as around the borders of topologically associating domains (TADs). Chromosome 19 consistently accumulated the highest SNVs and InDels events. Translocations showed variable patterns but with recurrent chromosomes of origin (e.g., Chr7 and Chr16). IR-induced InDels showed a relative increase in number relative to SNVs and a characteristic signature with respect to the frequency of triplet deletions in areas without repetitive or microhomology features. Overall experimental conditions and datasets the majority of SNVs per genome had no or little predicted functional impact with a maximum of 62, showing damaging potential. A dose-dependent effect of IR was surprisingly not apparent. We also observed a significant reduction in transition/transversion (Ti/Tv) ratios for IR-dependent SNVs, which could point to a contribution of the mismatch repair (MMR) system that strongly favors the repair of transitions over transversions, to the IR-induced DNA-damage response in human cells. Taken together, our results show the presence of distinguishable characteristic patterns of IR-induced DNA-alterations on a genome-wide level and implicate DNA-repair mechanisms in the formation of these signatures., publishedVersion

Published
2020
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131. Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation

Author

Dirmeier, Simon, Dächert, Christopher, van Hemert, Martijn, Tas, Ali, Ogando, Natacha S, van Kuppeveld, Frank, Bartenschlager, Ralf, Kaderali, Lars, Binder, Marco, Beerenwinkel, Niko, Virologie, dI&I I&I-1, Virologie, and dI&I I&I-1

Subjects
0301 basic medicine, RNA viruses, Genetic Screens, Genes, Viral, Gene regulatory network, Gene Identification and Analysis, Hepacivirus, Genetic Networks, Dengue virus, medicine.disease_cause, Pathology and Laboratory Medicine, Biochemistry, 0302 clinical medicine, RNA interference, Viral replication, Medicine and Health Sciences, Gene Regulatory Networks, Small interfering RNAs, Biology (General), Host factor, Ecology, Hepatitis C virus, Genetic screens, Nucleic acids, Genetic networks, Computational Theory and Mathematics, Genetic interference, Medical Microbiology, Modeling and Simulation, Viral Pathogens, Viruses, Epigenetics, Pathogens, Network Analysis, Research Article, Computer and Information Sciences, QH301-705.5, Computational biology, Biology, Models, Biological, Microbiology, 03 medical and health sciences, Cellular and Molecular Neuroscience, Interaction network, ssRNA viruses, Small interfering RNA, Modelling and Simulation, Virology, medicine, Genetics, Non-coding RNA, Gene, Molecular Biology, Microbial Pathogens, Ecology, Evolution, Behavior and Systematics, Host Microbial Interactions, Flaviviruses, Organisms, Biology and Life Sciences, Dengue Virus, Viral Replication, Hepatitis viruses, Gene regulation, 030104 developmental biology, RNA, Gene expression, 030217 neurology & neurosurgery, Genetic screen
Abstract

Genetic perturbation screens using RNA interference (RNAi) have been conducted successfully to identify host factors that are essential for the life cycle of bacteria or viruses. So far, most published studies identified host factors primarily for single pathogens. Furthermore, often only a small subset of genes, e.g., genes encoding kinases, have been targeted. Identification of host factors on a pan-pathogen level, i.e., genes that are crucial for the replication of a diverse group of pathogens has received relatively little attention, despite the fact that such common host factors would be highly relevant, for instance, for devising broad-spectrum anti-pathogenic drugs. Here, we present a novel two-stage procedure for the identification of host factors involved in the replication of different viruses using a combination of random effects models and Markov random walks on a functional interaction network. We first infer candidate genes by jointly analyzing multiple perturbations screens while at the same time adjusting for high variance inherent in these screens. Subsequently the inferred estimates are spread across a network of functional interactions thereby allowing for the analysis of missing genes in the biological studies, smoothing the effect sizes of previously found host factors, and considering a priori pathway information defined over edges of the network. We applied the procedure to RNAi screening data of four different positive-sense single-stranded RNA viruses, Hepatitis C virus, Chikungunya virus, Dengue virus and Severe acute respiratory syndrome coronavirus, and detected novel host factors, including UBC, PLCG1, and DYRK1B, which are predicted to significantly impact the replication cycles of these viruses. We validated the detected host factors experimentally using pharmacological inhibition and an additional siRNA screen and found that some of the predicted host factors indeed influence the replication of these pathogens, PLoS Computational Biology, 16 (2), ISSN:1553-734X, ISSN:1553-7358

Published
2019

133. Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation

Author

Dirmeier, Simon, Dächert, Christopher, van Hemert, Martijn, Tas, Ali, Ogando, Natacha S, van Kuppeveld, Frank, Bartenschlager, Ralf, Kaderali, Lars, Binder, Marco, Beerenwinkel, Niko, Dirmeier, Simon, Dächert, Christopher, van Hemert, Martijn, Tas, Ali, Ogando, Natacha S, van Kuppeveld, Frank, Bartenschlager, Ralf, Kaderali, Lars, Binder, Marco, and Beerenwinkel, Niko

Abstract

Genetic perturbation screens using RNA interference (RNAi) have been conducted successfully to identify host factors that are essential for the life cycle of bacteria or viruses. So far, most published studies identified host factors primarily for single pathogens. Furthermore, often only a small subset of genes, e.g., genes encoding kinases, have been targeted. Identification of host factors on a pan-pathogen level, i.e., genes that are crucial for the replication of a diverse group of pathogens has received relatively little attention, despite the fact that such common host factors would be highly relevant, for instance, for devising broad-spectrum anti-pathogenic drugs. Here, we present a novel two-stage procedure for the identification of host factors involved in the replication of different viruses using a combination of random effects models and Markov random walks on a functional interaction network. We first infer candidate genes by jointly analyzing multiple perturbations screens while at the same time adjusting for high variance inherent in these screens. Subsequently the inferred estimates are spread across a network of functional interactions thereby allowing for the analysis of missing genes in the biological studies, smoothing the effect sizes of previously found host factors, and considering a priori pathway information defined over edges of the network. We applied the procedure to RNAi screening data of four different positive-sense single-stranded RNA viruses, Hepatitis C virus, Chikungunya virus, Dengue virus and Severe acute respiratory syndrome coronavirus, and detected novel host factors, including UBC, PLCG1, and DYRK1B, which are predicted to significantly impact the replication cycles of these viruses. We validated the detected host factors experimentally using pharmacological inhibition and an additional siRNA screen and found that some of the predicted host factors indeed influence the replication of these pathogens.

Published
2020

134. Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation

Author

Virologie, dI&I I&I-1, Dirmeier, Simon, Dächert, Christopher, van Hemert, Martijn, Tas, Ali, Ogando, Natacha S, van Kuppeveld, Frank, Bartenschlager, Ralf, Kaderali, Lars, Binder, Marco, Beerenwinkel, Niko, Virologie, dI&I I&I-1, Dirmeier, Simon, Dächert, Christopher, van Hemert, Martijn, Tas, Ali, Ogando, Natacha S, van Kuppeveld, Frank, Bartenschlager, Ralf, Kaderali, Lars, Binder, Marco, and Beerenwinkel, Niko

Published
2020

141. Synthetic lethality in CCNE1-amplified high grade serous ovarian cancer through combined inhibition of Polo-like kinase 1 and microtubule dynamics

Author

Noack, Sabrina, Raab, Monika, Matthess, Yves, Sanhaji, Mourad, Krämer, Andrea, Győrffy, Balázs, Kaderali, Lars, Balat, Ahmed el, Becker, Sven, and Strebhardt, Klaus

Subjects
ddc:610
Abstract

The taxanes are effective microtubule-stabilizing chemotherapy drugs that inhibit mitosis, induce apoptosis, and produce regression in a fraction of cancers that arise at many sites including the ovary. Novel therapeutic targets that augment taxane effects are needed to improve clinical chemotherapy response in CCNE1-amplified high grade serous ovarian cancer (HGSOC) cells. In this study, we conducted an siRNA-based kinome screen to identify modulators of mitotic progression in CCNE1-amplified HGSOC cells that may influence clinical paclitaxel response. PLK1 is overexpressed in many types of cancer, which correlates with poor prognosis. Here, we identified a novel synthetic lethal interaction of the clinical PLK1 inhibitor BI6727 and the microtubule-targeting drug paclitaxel in HGSOC cell lines with CCNE1-amplification and elucidated the underlying molecular mechanisms of this synergism. BI6727 synergistically induces apoptosis together with paclitaxel in different cell lines including a patient-derived primary ovarian cancer culture. Moreover, the inhibition of PLK1 reduced the paclitaxel-induced neurotoxicity in a neurite outgrowth assay. Mechanistically, the combinatorial treatment with BI6727/paclitaxel triggers mitotic arrest, which initiates mitochondrial apoptosis by inactivation of anti-apoptotic BCL-2 family proteins, followed by significant loss of the mitochondrial membrane potential and activation of caspase-dependent effector pathways. This conclusion is supported by data showing that BI6727/paclitaxel-co-treatment stabilizes FBW7, a component of SCF-type ubiquitin ligases that bind and regulate key modulators of cell division and growth including MCL-1 and Cyclin E. This identification of a novel synthetic lethality of PLK1 inhibitors and a microtubule-stabilizing drug has important implications for developing PLK1 inhibitor-based combination treatments in CCNE1-amplified HGSOC cells.

Published
2018

143. Deep sequencing of a recurrent oligodendroglioma and the derived xenografts reveals new insights into the evolution of human oligodendroglioma and candidate driver genes

Author

Exner, Nadin D., primary, Valenzuela, Jaime Alberto Campos, additional, Abou-El-Ardat, Khalil, additional, Miletic, Hrvoje, additional, Huszthy, Peter C., additional, Radehaus, Petra M., additional, Schröck, Evelin, additional, Bjerkvig, Rolf, additional, Kaderali, Lars, additional, Klink, Barbara, additional, and Nigro, Janice M., additional

Published
2019
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144. Normalizing for individual cell population context in the analysis of high-content cellular screens

Author

Knapp Bettina, Rebhan Ilka, Kumar Anil, Matula Petr, Kiani Narsis A, Binder Marco, Erfle Holger, Rohr Karl, Eils Roland, Bartenschlager Ralf, and Kaderali Lars

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. Results We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. Conclusions Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.

Published
2011
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145. Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling

Author

Reinelt Gerhard, Ritter Daniel, Mazur Johanna, and Kaderali Lars

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background The reconstruction of gene regulatory networks from time series gene expression data is one of the most difficult problems in systems biology. This is due to several reasons, among them the combinatorial explosion of possible network topologies, limited information content of the experimental data with high levels of noise, and the complexity of gene regulation at the transcriptional, translational and post-translational levels. At the same time, quantitative, dynamic models, ideally with probability distributions over model topologies and parameters, are highly desirable. Results We present a novel approach to infer such models from data, based on nonlinear differential equations, which we embed into a stochastic Bayesian framework. We thus address both the stochasticity of experimental data and the need for quantitative dynamic models. Furthermore, the Bayesian framework allows it to easily integrate prior knowledge into the inference process. Using stochastic sampling from the Bayes' posterior distribution, our approach can infer different likely network topologies and model parameters along with their respective probabilities from given data. We evaluate our approach on simulated data and the challenge #3 data from the DREAM 2 initiative. On the simulated data, we study effects of different levels of noise and dataset sizes. Results on real data show that the dynamics and main regulatory interactions are correctly reconstructed. Conclusions Our approach combines dynamic modeling using differential equations with a stochastic learning framework, thus bridging the gap between biophysical modeling and stochastic inference approaches. Results show that the method can reap the advantages of both worlds, and allows the reconstruction of biophysically accurate dynamic models from noisy data. In addition, the stochastic learning framework used permits the computation of probability distributions over models and model parameters, which holds interesting prospects for experimental design purposes.

Published
2009
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146. Editorial: Integrative Computational Systems Biology Approaches in Immunology and Medicine

Author

Kaderali, Lars, Theis, Fabian, Ganusov, Vitaly V., Ciupe, Mihaela Stanca, Mehr, Ramit, Ribeiro, Ruy M., Hernandez-Vargas, Esteban A., Kaderali, Lars, Theis, Fabian, Ganusov, Vitaly V., Ciupe, Mihaela Stanca, Mehr, Ramit, Ribeiro, Ruy M., and Hernandez-Vargas, Esteban A.

Abstract

New technologies provide the ability to generate massive amounts of immunological data in health and disease. However, this “Big data” trend is becoming more challenging and unfeasible by the steep increase in the number of different pieces of information and the complexity of large datasets. Thus, systematic and tractable approaches that integrate a variety of biological and medical research data at multiple scales into mathematical, statistical, and computational models are crucial to harness knowledge and to develop new therapies...

Published
2019
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147. Editorial: Integrative Computational Systems Biology Approaches in Immunology and Medicine

Author

Mathematics, Kaderali, Lars, Theis, Fabian, Ganusov, Vitaly V., Ciupe, Stanca M., Mehr, Ramit, Ribeiro, Ruy M., Hernandez-Vargas, Esteban A., Mathematics, Kaderali, Lars, Theis, Fabian, Ganusov, Vitaly V., Ciupe, Stanca M., Mehr, Ramit, Ribeiro, Ruy M., and Hernandez-Vargas, Esteban A.

Abstract

New technologies provide the ability to generate massive amounts of immunological data in health and disease. However, this “Big data” trend is becoming more challenging and unfeasible by the steep increase in the number of different pieces of information and the complexity of large datasets. Thus, systematic and tractable approaches that integrate a variety of biological and medical research data at multiple scales into mathematical, statistical, and computational models are crucial to harness knowledge and to develop new therapies...

Published
2019

148. Editorial : integrative computational systems biology approaches in immunology and medicine

Author

Collin, Mattias, Kaderali, Lars, Theis, Fabian J., Ganusov, Vitaly V., Ciupe, Stanca M., Mehr, Ramit, Ribeiro, Ruy M., Hernandez-Vargas, Esteban Abelardo, Collin, Mattias, Kaderali, Lars, Theis, Fabian J., Ganusov, Vitaly V., Ciupe, Stanca M., Mehr, Ramit, Ribeiro, Ruy M., and Hernandez-Vargas, Esteban Abelardo

Abstract

Editorial on the Research Topic "Integrative Computational Systems Biology Approaches in Immunology and Medicine"

Published
2019

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