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Introduction: Inflammatory bowel diseases (IBDs) are lifelong conditions causing relapsing inflammation of the intestine. In the absence of a cure, clinical management of IBDs is extremely challeng...

Introduction: Crohn's disease and ulcerative colitis, both under the umbrella of inflammatory bowel diseases (IBD), involve many distinct molecular processes. The difference in their molecular proc...

Technological progress in medicine is constantly garnering pace, requiring that physicians constantly update their knowledge. The new wave of technologies breaking through into clinical practice includes the following: a) mHealth , which allows constant monitoring of biological parameters, anytime, anyplace, of hundreds of patients at the same time; b) artificial intelligence, which, powered by new deep learning techniques, are starting to beat human experts at their own game: diagnosis by imaging or electrocardiography ; c) 3-dimensional printing, which may lead to patient-specific prostheses; d) systems medicine, which has arisen from big data, and which will open the way to personalized medicine by bringing together genetic, epigenetic , environmental, clinical and social data into complex integral mathematical models to design highly personalized therapies. This state-of-the-art review aims to summarize in a single document the most recent and most important technological trends that are being applied to cardiology, and to provide an overall view that will allow readers to discern at a glance the direction of cardiology in the next few years.

Coronavirus disease-2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is evolving across the world and new treatments are urgently needed as with vaccines to prevent the illness and stem the contagion. The virus affects not only the lungs but also other tissues, thus lending support to the idea that COVID-19 is a systemic disease. The current vaccine and treatment development strategies ought to consider such systems medicine perspectives rather than a narrower focus on the lung infection only. COVID-19 is associated with elevated levels of the inflammatory cytokines such as interleukin-6 (IL-6), IL-10, and interferon-gamma (IFN-γ). Elevated levels of cytokines and the cytokine storm have been linked to fatal disease. This suggests new therapeutic strategies through blocking the cytokine storm. IL-6 is one of the major cytokines associated with the cytokine storm. IL-6 is also known to display pleiotropic/diverse pathophysiological effects. We suggest the blockage of IL-6 signaling and its downstream mediators such as Janus kinases (JAKs), and signal transducer and activators of transcription (STATs) offer potential hope for the treatment of severe cases of COVID-19. Thus, repurposing of already approved IL-6-JAK-STAT signaling inhibitors as well as other anti-inflammatory drugs, including dexamethasone, is under development for severe COVID-19 cases. We conclude this expert review by highlighting the potential role of precision herbal medicines, for example, the Cannabis sativa, provided that omics technologies can be utilized to build a robust scientific evidence base on their clinical safety and efficacy. Precision herbal medicine buttressed by omics systems science would also help identify new molecular targets for drug discovery against COVID-19.

Networks and network analyses are fundamental tools of systems biology. Networks are built by inferring pair-wise relationships among biological entities from a large number of samples such that subject-specific information is lost. The possibility of constructing these sample (individual)-specific networks from single molecular profiles might offer new insights in systems and personalized medicine and as a consequence is attracting more and more research interest. In this study, we evaluated and compared LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) and ssPCC (single sample network based on Pearson correlation) in the metabolomics context of metabolite-metabolite association networks. We illustrated and explored the characteristics of these two methods on (i) simulated data, (ii) data generated from a dynamic metabolic model to simulate real-life observed metabolite concentration profiles, and (iii) 22 metabolomic data sets and (iv) we applied single sample network inference to a study case pertaining to the investigation of necrotizing soft tissue infections to show how these methods can be applied in metabolomics. We also proposed some adaptations of the methods that can be used for data exploration. Overall, despite some limitations, we found single sample networks to be a promising tool for the analysis of metabolomics data.

Background The early recognition of paroxysmal atrial fibrillation (pAF) is a major clinical challenge for preventing thromboembolic events. In this prospective and multicentric study we evaluated prediction scores for the presence of pAF, calculated from non-invasive medical history and echocardiographic parameters, in patients with unknown AF status. Methods The 12-parameter score with parameters age, LA diameter, aortic root diameter, LV,ESD, TDI Aʹ, heart frequency, sleep apnea, hyperlipidemia, type II diabetes, smoker, ß-blocker, catheter ablation, and the 4-parameter score with parameters age, LA diameter, aortic root diameter and TDI A’ were tested. Presence of pAF was verified by continuous electrocardiogram (ECG) monitoring for up to 21 days in 305 patients. Results The 12-parameter score correctly predicted pAF in all 34 patients, in which pAF was newly detected by ECG monitoring. The 12- and 4-parameter scores showed sensitivities of 100% and 82% (95%-CI 65%, 93%), specificities of 75% (95%-CI 70%, 80%) and 67% (95%-CI 61%, 73%), and areas under the receiver operating characteristic (ROC) curves of 0.84 (95%-CI 0.80, 0.88) and 0.81 (95%-CI 0.74, 0.87). Furthermore, properties of AF episodes and durations of ECG monitoring necessary to detect pAF were analysed. Conclusions The prediction scores adequately detected pAF using variables readily available during routine cardiac assessment and echocardiography. The model scores, denoted as ECHO-AF scores, represent simple, highly sensitive and non-invasive tools for detecting pAF that can be easily implemented in the clinical practice and might serve as screening test to initiate further diagnostic investigations for validating the presence of pAF. Graphic abstract Prospective validation of a novel prediction model for paroxysmal atrial fibrillation based on echocardiography and medical history parameters by long-term Holter ECG

Type 2 diabetes (T2D) accounts for approximately 90% of diabetes worldwide and has become a global public health problem. Generally, individuals go to hospitals and get healthcare only when they have obvious T2D symptoms. While the underlying cause and mechanism of the disease are usually not well understood, treatment is for the symptoms, but not for the disease cause, and patients often continue to progress with more symptoms. Prediabetes is the early stage of diabetes and provides a good time window for intervention and prevention. However, with few symptoms, prediabetes is usually ignored without any treatment. Obviously, it is far from ideal to rely on the traditional medical system for diabetes healthcare. As a result, the medical system must be transformed from a reactive approach to a proactive approach. Root cause analysis and personalized intervention should be conducted for patients with prediabetes. Based on systems medicine, also known as P4 medicine, with a predictive, preventive, personalized, and participatory approach, new medical system is expected to significantly promote the prevention and treatment of chronic diseases such as prediabetes and diabetes. Many studies have shown that the occurrence and development of diabetes is closely related to gut microbiota. However, the relationship between diabetes and gut microbiota has not been fully elucidated. This review describes the research on the relationship between gut microbiota and diabetes and some exploratory trials on the interventions of prediabetes based on P4 medicine model. Furthermore, we also discussed how these findings might influence the diagnosis, prevention and treatment of diabetes in the future, thereby to improve the wellness of human beings.

This review will focus on a systems medicine approach to neonatal abstinence syndrome (NAS). Systems medicine utilizes information gained from the application of "omics" technology and bioinformatics (1). The omic approaches we will emphasize include genomics, epigenomics, proteomics, and metabolomics. The goals of systems medicine are to provide clinically relevant and objective insights into disease diagnosis, prognosis, and stratification as well as pharmacological strategies and evidence-based individualized clinical guidance. Despite the increasing incidence of NAS and its societal and economic costs, there has been only a very modest emphasis on utilizing a systems medicine approach, and this has been primarily in the areas of genomics and epigenomics. As detailed below, proteomics and metabolomics hold great promise in advancing our knowledge of NAS and its treatment. Metabolomics, in particular, can provide a quantitative assessment of the exposome, which is a comprehensive picture of both internal and external environmental factors affecting health.

We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.

Understanding the biological and clinical impact of copy number aberrations (CNA) in cancer remains an unmet challenge. Genetic amplification of chromosome 1q (chr1q-amp) is a major CNA conferring adverse prognosis in several cancers, including the blood cancer, multiple myeloma (MM). Although several chr1q genes portend high-risk MM disease, the underpinning molecular aetiology remains elusive. Here we integrate patient multi-omics datasets with genetic variables to identify 103 adverse prognosis genes in chr1q-amp MM. Amongst these, the transcription factor PBX1 is ectopically expressed by genetic amplification and epigenetic activation of its own preserved 3D regulatory domain. By binding to reprogrammed super-enhancers, PBX1 directly regulates critical oncogenic pathways, whilst in co-operation with FOXM1, activates a proliferative gene signature which predicts adverse prognosis across multiple cancers. Notably, pharmacological disruption of the PBX1-FOXM1 axis, including with a novel PBX1 inhibitor is selectively toxic against chr1q-amp cancer cells. Overall, our systems medicine approach successfully identifies CNA-driven oncogenic circuitries, links them to clinical phenotypes and proposes novel CNA-targeted therapy strategies in cancer.SignificanceWe provide a comprehensive systems medicine strategy to unveil oncogenic circuitries and inform novel precision therapy decisions against CNA in cancer. This first clinical multi-omic analysis of chr1q-amp in MM identifies a central PBX1-FOXM1 regulatory axis driving high-risk prognosis, as a novel therapeutic target against chr1q-amp in cancer.

Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.

Extracting knowledge from disparate biomedical literature can play an important role in the discovery of disease mechanisms and remedial therapies. This paper explores a hybrid semantics-based knowledge synthesis and discovery methodology that integrates approaches from Literature Based Discovery (LBD), Systems Medicine, and Knowledge Graphs to analyze published biomedical literature and discover potential causal associations between risk factors and Non-Communicable Diseases (NCDs). This paper presents a knowledge synthesis and discovery framework to (a) mine biomedical literature to identify semantic associations between risk factors and NCDs, and (b) represent them as a knowledge graph that outlines the multi-causal associations between underlying risk factors and NCDs. We employ a novel ranking algorithm that considers direct and indirect relation-based methods, augmented by semantic relatedness, to discover causal associations between risk factors and a targeted condition—in this case breast cancer. The novelty of our work is the use of breast cancer-specific embeddings in combination with graph-based metrics to quantitatively evaluate semantic association based on causality. We evaluate the performance of our breast cancer-specific word embedding model by utilizing information retrieval methods and manually curated breast cancer relations. Results confirm that (a) our cancer-specific word embedding model out-performs non-disease-specific models with respect to retrieval of breast cancer relations, and (b) our method generates valid causal knowledge about causal risk and protective factors related to breast cancer. Our present study focuses on breast cancer, however, our method is adaptable to other NCDs.

Precision medicine represents a potentially powerful means to alleviate the growing burden of chronic respiratory diseases. To realise its potential, however, we need a systems level understanding of how biological events (signalling pathways, cell-cell interactions, tissue mechanics) integrate across multiple spatial and temporal scales to give rise to pathology. This can be achieved most practically in silico: a paradigm that offers tight control over model parameters and rapid means of testing and generating mechanistic hypotheses. Patient-specific computational models that can enable identification of pathological mechanisms unique to patients' (omics, physiological, and anatomical) profiles and, therefore, personalised drug targets represent a major milestone in precision medicine. Current patient-based models in literature, especially medical devices, cardiac modelling, and respiratory medicine, rely mostly on (partial/ordinary) differential equations and have reached relatively advanced level of maturity. In respiratory medicine, patient-specific simulations mainly include subject scan-based lung mechanics models that can predict pulmonary function, but they treat the (sub)cellular processes as "black-boxes". A recent advance in simulating human airways at a cellular level to make clinical predictions raises the possibility of linking omics and cell level data/models with lung mechanics to understand respiratory pathology at a systems level. This is significant as this approach can be extended to understanding pathologies in other organs as well. Here, I will discuss ways in which computational models have already made contributions to personalised healthcare and how the paradigm can expedite clinical uptake of precision medicine strategies. I will mainly focus on an agent-based, asthmatic virtual patient that predicted the impact of multiple drug pharmacodynamics at the patient level, its potential to develop efficacious precision medicine strategies in respiratory medicine, and the regulatory and ethical challenges accompanying the mainstream application of such models.

In current radiation oncology practice, treatment protocols are prescribed based on the average outcomes of large clinical trials, with limited personalization and without adaptations of dose or dose fractionation to individual patients based on their individual clinical responses. Predicting tumor responses to radiation and comparing predictions against observed responses offers an opportunity for novel treatment evaluation. These analyses can lead to protocol adaptation aimed at the improvement of patient outcomes with better therapeutic ratios. We foresee the integration of mathematical models into radiation oncology to simulate individual patient tumor growth and predict treatment response as dynamic biomarkers for personalized adaptive radiation therapy (RT).

Chronic airflow limitation is the common denominator of patients with chronic obstructive pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of individual patients based on the degree of lung function impairment, nor does the degree of airflow limitation allow guidance regarding therapies. Over the last decades, understanding of the factors contributing to the heterogeneity of disease trajectories, clinical presentation, and response to existing therapies has greatly advanced. Indeed, diagnostic assessment and treatment algorithms for COPD have become more personalized. In addition to the pulmonary abnormalities and inhaler therapies, extra-pulmonary features and comorbidities have been studied and are considered essential components of comprehensive disease management, including lifestyle interventions. Despite these advances, predicting and/or modifying the course of the disease remains currently impossible, and selection of patients with a beneficial response to specific interventions is unsatisfactory. Consequently, non-response to pharmacologic and non-pharmacologic treatments is common, and many patients have refractory symptoms. Thus, there is an ongoing urgency for a more targeted and holistic management of the disease, incorporating the basic principles of P4 medicine (predictive, preventive, personalized, and participatory). This review describes the current status and unmet needs regarding personalized medicine for patients with COPD. Also, it proposes a systems medicine approach, integrating genetic, environmental, (micro)biological, and clinical factors in experimental and computational models in order to decipher the multilevel complexity of COPD. Ultimately, the acquired insights will enable the development of clinical decision support systems and advance personalized medicine for patients with COPD.

Systems biomedicine consists in the integration of biosciences, medicine and computer sciences. Systems biomedicine is supposed to allow a holistic approach to the human subject and its disease states. This paper outlines the basic concepts and open issues in this field and provides an outlook for the integration of medical imaging procedures in the growing area of systems biomedicine. The terms “Systems biomedicine”, “Systems medicine” were used for bibliographic search in Pubmed and Web of sciences. Most relevant papers were selected for inclusion in this paper; a synthesis of the papers is presented. An integration of methods is required to best exploit the potential of the multi-‘omics biobanks, in which imaging biomarker data represent an added value. To obtain such integration, imaging biomarker data from different “systems” should be in a manageable format. The recent evolution of AI and the hardware improvements by parallel and fast computing are bringing us towards a new age of molecular and morphologic imaging. Although there will always be a qualitative aspect to imaging, AI and quantitative metrics will supplement and complement the current “human” methods of interpretation of imaging data in a holistic approach to individual patient management.

For a research project on mass spectrometry, a streamlined, harmonized and robust analytical pipeline is built to predict tumor recurrence. By means of standardization all steps from sample collection, analysis, proteome, and metabolome analysis are harmonized. Challenges like non-central identificators and distributed data are overcome with a centralized high-performant IT-platform in combination with a pseudonymization service and harmonization.

There is a clinical need to identify children with poor asthma control as early as possible, to optimize treatment and/or to find therapeutic alternatives. Here, we present the “Systems Pharmacology Approach to Uncontrolled Pediatric Asthma” (SysPharmPediA) study, which aims to establish a pediatric cohort of moderate-to-severe uncontrolled and controlled patients with asthma, to investigate pathophysiological mechanisms underlying uncontrolled moderate-to-severe asthma in children on maintenance treatment, using a multi-omics systems medicine approach. In this multicenter observational case–control study, moderate-to-severe asthmatic children (age, 6–17 years) were included from four European countries (Netherlands, Germany, Spain, and Slovenia). Subjects were classified based on asthma control and number of exacerbations. Demographics, current and past patient/family history, and clinical characteristics were collected. In addition, systems-wide omics layers, including epi(genomics), transcriptomics, microbiome, proteomics, and metabolomics were evaluated from multiple samples. In all, 145 children were included in this cohort, 91 with uncontrolled (median age = 12 years, 43% females) and 54 with controlled asthma (median age = 11.7 years, 37% females). The two groups did not show statistically significant differences in age, sex, and body mass index z-score distribution. Comprehensive information and diverse noninvasive biosampling procedures for various omics analyses will provide the opportunity to delineate underlying pathophysiological mechanisms of moderate-to-severe uncontrolled pediatric asthma. This eventually might reveal novel biomarkers, which could potentially be used for noninvasive personalized diagnostics and/or treatment.

Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in “Personalized Medicine” and “Innovative Biomedical Technologies”) could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.

Triple-negative breast cancer (TNBC) is a heterogeneous subtype of breast cancers with poor prognosis. The etiology of triple-negative breast cancer (TNBC) is involved in various biological signal cascades and multifactorial aberrations of genetic, epigenetic and microenvironment. New therapeutic for TNBC is urgently needed because surgery and chemotherapy are the only available modalities nowadays. A better understanding of the molecular mechanisms would be a great challenge because they are triggered by cascade signaling pathways, genetic and epigenetic regulations, and drug–target interactions. This would allow the design of multi-molecule drugs for the TNBC and non-TNBC. In this study, in terms of systems biology approaches, we proposed a systematic procedure for systems medicine design toward TNBC and non-TNBC. For systems biology approaches, we constructed a candidate genome-wide genetic and epigenetic network (GWGEN) by big databases mining and identified real GWGENs of TNBC and non-TNBC assisting with corresponding microarray data by system identification and model order selection methods. After that, we applied the principal network projection (PNP) approach to obtain the core signaling pathways denoted by KEGG pathway of TNBC and non-TNBC. Comparing core signaling pathways of TNBC and non-TNBC, essential carcinogenic biomarkers resulting in multiple cellular dysfunctions including cell proliferation, autophagy, immune response, apoptosis, metastasis, angiogenesis, epithelial-mesenchymal transition (EMT), and cell differentiation could be found. In order to propose potential candidate drugs for the selected biomarkers, we designed filters considering toxicity and regulation ability. With the proposed systematic procedure, we not only shed a light on the differences between carcinogenetic molecular mechanisms of TNBC and non-TNBC but also efficiently proposed candidate multi-molecule drugs including resveratrol, sirolimus, and prednisolone for TNBC and resveratrol, sirolimus, carbamazepine, and verapamil for non-TNBC.

Juvenile idiopathic arthritis (JIA) encompasses a heterogeneous group of inflammatory disorders linked by chronic joint inflammation [1]. Stratifying patients in a logical manner would improve the ...

Pneumonia and acute lung injury cause the highest mortality of all infectious disease, and tremendous medical challenges, e.g. due to newly emerging pathogens and increasing rates of antimicrobial resistance. Increasingly performant computer systems have been used for over a decade to model complex biomedical systems, thereby establishing a field of research that is closely intertwined with conventional biomedical diagnostics and basic research. The application of mathematical modeling and bioinformatics to unravel the patho-mechanisms of the diverse forms of pneumonia ranges from epidemiological modeling of pneumonia and deep learning to systems biology and bioinformatics. While the first two focus on predicting population-wide disease progression and establishment of patterns in data that inform about predictive value, the latter pair aims to comprehend complex systems by modeling them on the basis of high-dimensional data gained from multi-omics technologies. The interdisciplinary challenge resides in applying the right mathematical system to a well-selected and robust set of clinical and experimental data, and to iteratively refine the algorithms by validation experiments. Ideally, this approach will yield computational systems that support decision-making in disease prevention and diagnosis, as well as allow to extract crucial data from large high-throughput datasets, with the ultimate goal to reduce the socio-economic burden posed by pneumonia and other major diseases.

Simple Summary Radio-sensitizing effects of moderate or mild hyperthermia (heating up tumor cells up to 41–43 °C) in combination with radiotherapy (thermoradiotherapy) have been evaluated for decades. However, how this combination might modulate an anti-tumor immune response is not well known. To investigate the dynamic behavior of immune–tumor ecosystems in different scenarios, a model representing an artificial adaptive immune system in silico is used. Such a model may be far removed from the real situation in the patient, but it could serve as a laboratory to investigate fundamental principles of dynamics in such systems under well-controlled conditions and it could be used to generate and refine hypothesis supporting the design of clinical trials. Regarding the results of the presented computer simulations, the main effect is governed by the cellular radio-sensitization. In addition, the application of hyperthermia during the first radiotherapy fractions seems to be more effective. Abstract There is some evidence that radiotherapy (RT) can trigger anti-tumor immune responses. In addition, hyperthermia (HT) is known to be a tumor cell radio-sensitizer. How HT could enhance the anti-tumor immune response produced by RT is still an open question. The aim of this study is the evaluation of potential dynamic effects regarding the adaptive immune response induced by different combinations of RT fractions with HT. The adaptive immune system is considered as a trainable unit (perceptron) which compares danger signals released by necrotic or apoptotic cell death with the presence of tumor- and host tissue cell population-specific molecular patterns (antigens). To mimic the changes produced by HT such as cell radio-sensitization or increase of the blood perfusion after hyperthermia, simplistic biophysical models were included. To study the effectiveness of the different RT+HT treatments, the Tumor Control Probability (TCP) was calculated. In the considered scenarios, the major effect of HT is related to the enhancement of the cell radio-sensitivity while perfusion or heat-based effects on the immune system seem to contribute less. Moreover, no tumor vaccination effect has been observed. In the presented scenarios, HT boosts the RT cell killing but it does not fundamentally change the anti-tumor immune response.

In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

Pathologic conditions in which the body׳s own response to an insult is the most damaging aspect of the disease, such as in auto-immunity and sepsis, have seen researchers and clinicians struggle to find therapeutic targets for decades. Large clinical cohorts and advanced molecular biology are key elements in the search for new medications. Methodology with increasing resolution is constantly being developed to identify new subsets of cells embedded in the disease framework and to map their complex gene expression profile, secretome and epigenome. Big data that is thus being generated require advanced computational methods in order to distil knowledge from them, and in order to use them as the basis for modeling approaches. Models need to integrate the spatial, temporal and organizational domains in order to reflect biology with a certain degree of fidelity. The ultimate goal of the combined efforts of clinical studies, molecular biology with cutting-edge technology and comprehensive mathematical modeling is the bench-to-bedside translation of knowledge to reduce the socio-economic burden of disease.

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson’s disease. The review offers valuable insights and informs the research in DL and SM., This publication is based upon work from COST Action Open Multiscale Systems Medicine (OpenMultiMed, CA15120), supported by COST (European Cooperation in Science and Technology). COST is funded by the Horizon 2020 Framework Programme of the European Union. HZ and HYW are also supported by the MetaPlat(690998), SenseCare(690862) and STOP(823978) projects funded by H2020 RISE programme. FC and PT acknowledge the support of H2020 project iPC “individualized Paediatric Cure” (826121). Participation of V.S. in OpenMultiMed is supported by the Czech Ministry of Education, Youth and Sports (project LTC18074). JLM. thanks Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre (ESTG/IPP); and Centro de Recursos Naturais e Ambiente, Instituto Superior Técnico (CERENA/IST) within the support of FCT-Fundação para a Ciência e a Tecnologia through the strategic project FCT-UIDB/04028/2020. MZ acknowledges the Spanish State Research Agency, through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711) and the funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (851255). The Northern Ireland Centre for Stratified Medicine has been financed by a grant awarded to AJ Bjourson under the European Union Regional Development Fund (ERDF) EU Sustainable Competitiveness Programme for Northern Ireland & the Northern Ireland Public Health Agency (HSC R&D). TSR also acknowledges funding from PHA R&D Division and the Western Health & Social Care.

This article is a revised version of our proposal for the establishment of the legal concept of risk-adjusted prevention in the German healthcare system to regulate access to risk-reduction measures for persons at high and moderate genetic cancer risk (Meier et al. Risikoadaptierte Pravention’. Governance Perspective fur Leistungsanspruche bei genetischen (Brustkrebs-)Risiken, Springer, Wiesbaden, 2018). The German context specifics are summarized to enable the source text to be used for other country-specific healthcare systems. Establishing such a legal concept is relevant to all universal and free healthcare systems similar to Germany’s. Disease risks can be determined with increasing precision using bioinformatics and biostatistical innovations (‘big data’), due to the identification of pathogenic germ line mutations in cancer risk genes as well as non-genetic factors and their interactions. These new technologies open up opportunities to adapt therapeutic and preventive measures to the individual risk profile of complex diseases in a way that was previously unknown, enabling not only adequate treatment but in the best case, prevention. Access to risk-reduction measures for carriers of genetic risks is generally not regulated in healthcare systems that guarantee universal and equal access to healthcare benefits. In many countries, including Austria, Denmark, the UK and the US, entitlement to benefits is essentially linked to the treatment of already manifest disease. Issues around claiming benefits for prophylactic measures involve not only evaluation of clinical options (genetic diagnostics, chemoprevention, risk-reduction surgery), but the financial cost and—from a social ethics perspective—the relationship between them. Section 1 of this chapter uses the specific example of hereditary breast cancer to show why from a medical, social-legal, health-economic and socio-ethical perspective, regulated entitlement to benefits is necessary for persons at high and moderate risk of cancer. Section 2 discusses the medical needs of persons with genetic cancer risks and goes on to develop the healthy sick model which is able to integrate the problems of the different disciplines into one scheme and to establish criteria for the legal acknowledgement of persons at high and moderate (breast cancer) risks. In the German context, the social-legal categories of classical therapeutic medicine do not adequately represent preventive measures as a regular service within the healthcare system. We propose risk-adjusted prevention as a new legal concept based on the heuristic healthy sick model. This category can serve as a legal framework for social law regulation in the case of persons with genetic cancer risks. Risk-adjusted prevention can be established in principle in any healthcare system. Criteria are also developed in relation to risk collectives and allocation (Sects. 3, 4, 5).

The Systems Biology Graphical Notation (SBGN) is a community standard developed to reduce ambiguity in the visual representation of biomolecular networks by providing a standardized description of sets of symbols to use. SBGN comprises three distinct but complementary languages: Process Description (PD), Entity Relationship (ER), and Activity Flow (AF). SBGN captures distinct levels of details. SBGN PD is well-suited for representing detailed sequential biochemical mechanisms, such as metabolic pathways. SBGN AF shows cascades of influences between the activities carried out by biomolecular entities (e.g., stimulation and inhibition) in signaling pathways and regulatory networks. SBGN ER represents independent interactions between features of biological entities, thereby avoiding the complexity of represented biological states and interactions. SBGN has become a well-established standard in systems biology and systems medicine. Numerous visualization tools support SBGN, which is increasingly used in many popular biological databases as well as in scientific publications. This article aims at providing an overview of SBGN and its current applications in the field of biomedicine.

The approach to system analysis of bile duct dysfunctions based on analysis of multi-omics data of cholangiocytes is considered. There is suggested that changes in intercellular interactions in tissues of the bile duct cause phenotypic manifestations of the cholangiopathies in the changes in cholangiocyte regulation, which can be evaluated by analysis of changes in the molecular composition of the bile.

Hepatitis B Virus (HBV) infection is a major cause of morbidity and mortality worldwide. However, poor understanding of its pathogenesis often gives rise to intractable immune escape and prognosis recurrence. Thus, a valid systematic approach based on big data mining and genome-wide RNA-seq data is imperative to further investigate the pathogenetic mechanism and identify biomarkers for drug design. In this study, systems biology method was applied to trim false positives from the host/pathogen genetic and epigenetic interaction network (HPI-GEN) under HBV infection by two-side RNA-seq data. Then, via the principal network projection (PNP) approach and the annotation of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, significant biomarkers related to cellular dysfunctions were identified from the core cross-talk signaling pathways as drug targets. Further, based on the pre-trained deep learning-based drug-target interaction (DTI) model and the validated pharmacological properties from databases, i.e., drug regulation ability, toxicity, and sensitivity, a combination of promising multi-target drugs was designed as a multiple-molecule drug to create more possibility for the treatment of HBV infection. Therefore, with the proposed systems medicine discovery and repositioning procedure, we not only shed light on the etiologic mechanism during HBV infection but also efficiently provided a potential drug combination for therapeutic treatment of Hepatitis B.

Among the genetic and environmental contributors to dilated cardiomyopathy (DCM), the pathogenesis of drug-induced DCM is heterogeneous, poorly understood, and difficult to predict. In this study, transcriptomic data from two recently published datasets were used to identify genes which are differentially expressed in DCM compared to cardiac tissue from non-failing donors. DCM-associated genes were then mapped using gene set enrichment analysis to associated biochemical pathways, gene ontology functions, and protein-protein interactions, creating a DCM-associated biological perturbation network. Corresponding multi-scale pharmacologic activity networks for over 9000 drugs were produced using public repositories of known drug-protein interactions. Drugs were then rank-ordered based on multi-scale statistical significance of concordance with the DCM perturbation network while accounting for direction of effect using a causal analysis algorithm. A predictive algorithm using overrepresentation analysis was developed using the SIDER database of adverse drug reactions (ADRs) to formulate ADR-protein and ADR-pathway associations, which were then compared to the drug-DCM association networks produced by our model. In addition to a demonstrated concordance between the rank lists for each dataset, this platform prioritized multiple drugs and metabolites which are known to be associated with DCM, such as doxorubicin, risperidone, and 4-hydroxynonenal, as well as associated mechanisms such as bile acid excess and inhibition of DNA topoisomerase II and mitochondrial complex III. Multi-scale concordance of drug and DCM networks highlighted the involvement of heat shock proteins and associated angiotensin II activity, supporting current evidence that its inhibition may be cardioprotective to drug-induced DCM. Our approach also recapitulated the roles of drugs, genes, and pathways that hold prognostic and therapeutic significance for DCM, such as the abilities of sulforaphane and fenofibrate to prevent DCM in animal models of type 2 diabetes. This platform can be used to predict a wide array of adverse drug reactions and their underlying mechanisms based on bioactivity data.

The rapidly increasing adaptation of Big data technologies in biomedicine, has introduced a revolution in and medical research practice. Trending high-throughput data analysis techniques, have converted the appearance of the biological system to acquire idolization methods for complicated diseases. Majority of the acquired Big-data models govern the materialization of illustrating medicine. This transformation aims at quantification of the period of P4 medicine that will then progressively be more predictive, personalized, pre-emptive, and participatory. It layouts a track to modernize antiseptic methods for the patient’s concern center. P4 medicine besides being a scientific face of systems medicine has two highlighted purposes first of which is to evaluate wellness, while the other is, to identify and expose disease. Patients are major operators in the cognizance of P4 medicine as they directly get engaged with a medically familiar network that helps them boost their health. This article will discuss the maturity in big data planning and correlated challenges in biomedicine.

Ovarian cancer is the leading cause of death from gynecologic malignancies. Cancer stem cells (CSC) seem to play a crucial role in tumor metastasis, recurrence, and chemoresistance. Therefore, CSCs offer significant potential for developing therapeutic targets and to understand tumor recurrence and chemoresistance mechanisms. In the present study, our aim was the identification of the gene group in ovarian CSCs (O-CSCs) and the potential of the resultant gene group in ovarian cancer prognosis. Two different microarray data sets were analyzed by comparing gene expression levels between O-CSCs and cancer samples. The O-CSC co-expression network was reconstructed and its modules were identified. According to the analysis results, 74 mutual DEGs were identified. The O-CSC-specific co-expression network included 32 nodes and 95 edges (network density: 19%), while the co-expression network in cancer samples was reconstructed with 74 nodes and 1066 edges (network density: 39%). Understanding of the molecular mechanism and signatures of O-CSCs should provide valuable insight into chemotherapy resistance and recurrence of ovarian tumors. A highly connected 12 gene module in O-CSC samples of BAMB1, NFKB12, EZR, TNFAIP3, C1orf86, PMAIP1, GEM, KHDRBS3, FILIP1, FGFR2, TGFBR3 and PEG10, (network density: 67%) was identified. Prognostic performance of these genes was evaluated independently using six ovarian cancer datasets (n = 1933 patient samples) via survival analysis. These co-expressed genes were determined as prognostic targets in ovarian cancer. Through literature search validation, five genes (C1orf86, PMAIP1, FILIP1, NFKB12 and PEG10) suggested as novel molecular targets in ovarian cancer. The presented prognostic biomarkers here provide a resource for the understanding of tumor recurrence and chemoresistance and may facilitate critical research directions and development of new prognostic and therapeutic strategies for ovarian cancer.CSCs: cancer stem cells; O-CSCs: ovarian CSCs; FACS: fluorescence-activated cell sorting; SP: side population; MP: main population; TFs: transcription factors.

Background. Iranian traditional medicine (ITM) is a holistic medical system that uses a wide range of medicinal substances to treat disease. Reorganization and standardization of the data on ITM concepts is a necessity for optimal use of this rich source. In an initial step towards this goal, we created a database of ITM materia medica. Main Body. Primarily based on Makhzan al-Advieh, which is the most recent encyclopedia of materia medica in ITM with the largest number of monographs, a database of natural medicinal substances was created using both text mining methods and manual editing. UNaProd, a Universal Natural Product database for materia medica of ITM, is currently host to 2696 monographs, from herbal to animal to mineral compounds in 16 diverse attributes such as origin and scientific name. Currently, systems biology, and more precisely systems medicine and pharmacology, can be an aid in providing rationalizations for many traditional medicines and elucidating a great deal of knowledge they can offer to guide future research in medicine. Conclusions. A database of materia medica is a stepping stone in creating a systems pharmacology platform of ITM that encompasses the relationships between the drugs, their targets, and diseases. UNaProd is hyperlinked to IrGO and CMAUP databases for Mizaj and molecular features, respectively, and it is freely available at http://jafarilab.com/unaprod/.

Background Heart failure (HF) is a poly-aetiological syndrome with large heterogeneity regarding clinical presentation, pathophysiology, clinical outcome and response to therapy. The MyoVasc study (NCT04064450) is an epidemiological cohort study investigating the development and progression of HF. Methods The primary objective of the study is (a) to improve the understanding of the pathomechanisms of HF across the full spectrum of clinical presentation, (b) to investigate the current clinical classifications of HF, and (c) to identify and characterize homogeneous subgroups regarding disease development using a systems-oriented approach. Worsening of HF, that is, the composite of transition from asymptomatic to symptomatic HF, hospitalization due to HF, or cardiac death, was defined as the primary endpoint of the study. During a six-year follow-up period, all study participants receive a highly standardized, biannual five-hour examination in a dedicated study centre, including detailed cardiovascular phenotyping and biobanking of various biomaterials. Annual follow-up examinations are conducted by computer-assisted telephone interviews recording comprehensively the participants´ health status, including subsequent validation and adjudication of adverse events. Results In total, 3289 study participants (age range: 35 to 84 years; female sex: 36.8%) including the full range of HF stages were enrolled from 2013 to 2018. Approximately half of the subjects (n=1741) presented at baseline with symptomatic HF (i.e. HF stage C/D). Among these, HF with preserved ejection fraction was the most frequent phenotype. Conclusions By providing a large-scale, multi-dimensional biodatabase with sequential, comprehensive medical-technical (sub)clinical phenotyping and multi-omics characterization (i.e. genome, transcriptome, proteome, lipidome, metabolome and exposome), the MyoVasc study will help to advance our knowledge about the heterogeneous HF syndrome by a systems-oriented biomedicine approach. Trial registration ClinicalTrials.gov; NCT04064450.

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

Sjogren's syndrome (SS) is an autoimmune inflammatory disorder of the exocrine glands, particularly affecting lacrimal and salivary glands. Hallmark symptoms are dry mouth and dry eye, often in conjunction with general symptoms such as malaise and fatigue. Lymphomas could develop in 5%–10% of the patients. As SS is a rather complex syndrome with many features, the one patient being diagnosed with SS may suffer from a different complex of complaints than another SS patient and may thus be in need of a different treatment approach. To better classify SS patients and to personalize their treatment, many clinicians and researchers are currently working on efforts to refine classification of SS patients, to ease the diagnostic work-up of SS, and to better understand the etiopathogenesis of SS. Latter knowledge is essential to understand the course of the disease. This way clinicians will be able to identify patients who are at risk of developing SS or lymphomas, can intervene at an early stage of the disease to prevent damage to, e.g., the glands, and can personalize treatment with, e.g., biologicals.

In this thesis we identified several biomarkers that can predict diabetic kidney disease (DKD) progression and drug response. The identified biomarkers belong to multiple molecular pathways such as: inflammation, ECM degradation, fibrosis, energy metabolism and vascular function. The multiple pathways identified in this thesis indicate that DKD is a heterogeneous disease with a complex underlying pathophysiology. In addition, they provide insights in the underlying molecular mechanisms for how the drugs examined in this thesis may confer long-term kidney protection and they may even aid in identifying new drug targets for patients with DKD. Furthermore, the discovered and validated biomarkers and biomarker panels may pave the way for a personalized treatment approach and inform best (drug) treatment choices for individual patients.

Introduction: Multiple sclerosis (MS) is a chronic disorder of the central nervous system with an untreatable late progressive phase. Molecular maps of different stages of brain lesion evolution in patients with progressive multiple sclerosis (PMS) are missing but critical for understanding disease development and to identify novel targets to halt progression. Materials and Methods: The MS Atlas database comprises comprehensive high-quality transcriptomic profiles of 98 white matter (WM) brain samples of different lesion types (normal-appearing WM [NAWM], active, chronic active, inactive, remyelinating) from ten progressive MS patients and 25 WM areas from five non-neurological diseased cases. Results: We introduce the first MS brain lesion atlas (msatlas.dk), developed to address the current challenges of understanding mechanisms driving the fate on a lesion basis. The MS Atlas gives means for testing research hypotheses, validating biomarkers and drug targets. It comes with a user-friendly web interface, and it fosters bioinformatic methods for de novo network enrichment to extract mechanistic markers for specific lesion types and pathway-based lesion type comparison. We describe examples of how the MS Atlas can be used to extract systems medicine signatures and demonstrate the interface of MS Atlas. Conclusion: This compendium of mechanistic PMS WM lesion profiles is an invaluable resource to fuel future MS research and a new basis for treatment development.

Periodontal medicine has seen tremendous advances during the era of molecular medicine started in the 1950s. Findings of this research are now being complemented by systems biology and systems medicine approaches to studying human well-being and chronic disturbances in multiorgan systems. While the integration of “omics” technologies to investigate, diagnose, and tailor therapy for periodontal disease is in its infancy, multiple lines of evidence support the feasibility of personalized periodontal medicine. The path to identify compound markers of disease activity and therapeutic targets using integrated “omics” is now established. This chapter provides an overview of the advances in our understanding of periodontal diseases and discusses current concepts within the framework of systems medicine.

Metabolism is a set of chemical reactions that convert nutrients into energy and building blocks for growth or remove toxic end products. Most reactions are catalysed by specialised proteins, called enzymes, which are encoded in our genes. If such a gene is mutated, it may produce an inactive enzyme and lead to diseases. These are called inborn errors of metabolism. All the chemical reactions in our body, together with their enzymes, form a functional blueprint of an individual. This can be translated into mathematical terms. Eventually, we may create mathematical models that include patient-specific information. Such a “digital twin” can then be used to analyse the molecular blueprint of an individual patient, and to discover potential new biomarkers and targets for therapies. In her PhD thesis, Agnieszka Wegrzyn describes several ways to optimise and apply various computational modelling approaches for the analysis of patient data and disease mechanisms. She predicts new biomarkers for Refsum disease (a disease in which the food component phytanic acid is not degraded) and for vitamin B2 deficiency. Also, she presents a new model for brain metabolism in phenylketonuria patients, linking diet with serotonin and dopamine metabolism. Based on this model, she proposes a diet optimisation to reduce neurological symptoms in these patients. This model can also be applied to other neurodegenerative diseases, such as Alzheimer’s or Parkinson’s Disease, depression, or autism.

As the field of integrative and functional nutrition took root, practitioners needed a concrete model to help them gain a deep understanding of this approach. The ability to connect, synthesize, and apply information coherently using an integrated, whole systems-based lens to nutrition care led to the development of the Integrative and Functional Medical Nutrition Therapy (IFMNT) Radial. This conceptual framework, which illustrates practice components for critical thinking and clinical investigation, depicts the multi-dimensional aspects of a systems-based nutrition assessment in delivering medical nutrition therapy (MNT). The term “medical nutrition therapy” is defined as “nutritional diagnostic, therapy, and counseling services for the purpose of disease management, which are furnished by a registered dietitian or nutrition professional.” The counseling component of MNT has been described as a “supportive process to set priorities, establish goals and create individualized action plans which acknowledge and foster responsibility for self-care.” The Radial depicts a person-centered process that allows for the evaluation of complex interactions and interrelationships. The Radial purposefully integrates the evidence-based Nutrition Care Process (NCP) for clinicians to apply an integrative and functional methodology to provide high-quality nutrition care. Recent updates to the IFMNT Radial (referred to as the Radial) were based on emerging data on nutritional science, systems medicine, omics technologies, and the microbiome.

Many doctors face the challenge of patients with common diseases who do not respond to certain treatments with important clinical implications. This is likely due to alterations in the patient's genomic profile. In the era of precision medicine, high-throughput techniques (omics) allow to identify group of patients who respond or do not respond to a specific treatment. This chapter will provide a general overview of the benefit of functional omics in medicine.