Singapore, Singapore
Singapore, Singapore

Biomax Informatics is a Munich-based software company specializing in research software for bioinformatics. Biomax was founded in 1997 and has its roots in the Munich Information Center for Protein Sequences . The company's customer base consists of companies and research organizations in the areas of drug discovery, diagnostics, fine chemicals, food and plant production. In addition to exclusive software tools, Biomax Informatics provides services and curated knowledge bases.In September 2007, Biomax Informatics acquired the Viscovery software business of the Austrian data mining specialist Eudaptics Software.Biomax Informatics and Sophic Systems Alliance Inc. participate in the Cancer Gene Data Curation Project with the National Cancer Institute . This project maintains a public data set of cancer-related genes and drugs. This data set has been integrated with the NCI's caBIO domain model which is part of the CaBIG Integrative Cancer Research workspace. This Cancer Gene Index can be obtained separately from an NCI web site. Wikipedia.

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Grant
Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: HEALTH.2013.2.3.3-1 | Award Amount: 31.38M | Year: 2014

Far from receding, the threats posed by infections with epidemic potential grow ever greater. Although Europe has amongst the best healthcare systems in the world, and also the worlds supreme researchers in this field, we lack co-ordination and linkage between networks that is required to respond fast to new threats. This consortium of consortia will streamline our response, using primary and secondary healthcare to detect cases with pandemic potential and to activate dynamic rapid investigation teams that will deploy shared resources across Europe to mitigate the impact of future pandemics on European health, infrastructure and economic integrity. If funded, PREPARE will transform Europes response to future severe epidemics or pandemics by providing infrastructure, co-ordination and integration of existing clinical research networks, both in community and hospital settings. It represents a new model of collaboration and will provide a one-stop shop for policy makers, public health agencies, regulators and funders of research into pathogens with epidemic potential. It will do this by mounting interepidemic (peace time) patient oriented clinical trials in children and in adults, investigations of the pathogenesis of relevant infectious diseases and facilitate the development of sophisticated state-of-the-art near-patient diagnostics. We will develop pre-emptive solutions to ethical, administrative, regulatory and logistical bottlenecks that prevent a rapid response in the face of new threats. We will provide education and training not only to the members of the network, but also to external opinion leaders, funders and policy makers thereby streamlining our future response. By strengthening and integrating interepidemic research networks, PREPARE will enable the rapid coordinated deployment of Europes elite clinical investigators, resulting in a highly effective response to future outbreaks based on solid scientific advances.


Grant
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2009.5.3 | Award Amount: 15.53M | Year: 2011

The airways diseases asthma and chronic obstructive pulmonary disease affect over 400 million people world-wide and cause considerable morbidity and mortality. Airways disease costs the European Union in excess of 56 billion per annum. Current therapies are inadequate and we do not have sufficient tools to predict disease progression or response to current or future therapies. Our consortium, Airway Disease PRedicting Outcomes through Patient Specific Computational Modelling (AirPROM), brings together the exisiting clinical consortia (EvA FP7, U-BIOPRED IMI and BTS Severe Asthma), and expertise in physiology, radiology, image analysis, bioengineering, data harmonization, data security and ethics, computational modeling and systems biology. We shall develop an integrated multi-scale model building upon existing models. This airway model will be comprised of an integrated micro-scale and macro-scale airway model informed and validated by omic data and ex vivo models at the genome-transcriptome-cell-tissue scale and by CT and functional MRI imaging coupled to detailed physiology at the tissue-organ scale utilising Europes largest airway disease cohort. Validation will be undertaken cross-sectionally, following interventions and after longitudinal follow-up to incorporate both spatial and temporal dimensions. AirPROM has a comprehensive data management platform and a well-developed ethico-legal framework. Critically, AirPROM has an extensive exploitation plan, involving at its inception and throughout its evolution those that will develop and use the technologies emerging from this project. AirPROM therefore will bridge the critical gaps in our clinical management of airways disease, by providing validated models to predict disease progression and response to treatment and the platform to translate these patient-specific tools, so as to pave the way to improved, personalised management of airways disease.


Grant
Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: HEALTH.2010.2.4.5-1 | Award Amount: 15.66M | Year: 2010

Causes explaining the epidemic of IgE-associated (allergic) diseases are unclear. MeDALL (Mechanisms of the Development of Allergy) aims at generating novel knowledge on mechanisms of allergy initiation, in particular in childhood. To understand how a complex network of genetic and environmental factors leads to complex allergic phenotypes, a novel stepwise, large and integrative translational approach is needed. MeDALL includes experts in allergy, epidemiology, genetics, immunology, biology, animal models, biochemistry and systems biology combining strengths of ongoing EU projects. Classical phenotypes (expert-based) and novel phenotypes of allergy (hypothesis-free statistical modelling) are compared. Population-based data are collected from a cross-sectional study (Karelia) and existing birth-cohorts followed using a common protocol. IgE to foods and inhalants are tested using component-resolved diagnosis across Europe in populations. Biomarker profiles (fingerprints) are extensively assessed using epigenetics, targeted proteomics and unbiased transcriptomics in a subsample of the study population. Those associated with allergic phenotypes are validated in large study populations. Relevant fingerprints are combined into network biomarker phenotype handprints using a systems biology approach and validated in a sufficiently powered sample. Animal studies and in vitro human immunology reinforce the validation. This information coupled with classical and novel phenotypes characterize environmental protective and susceptibility factors of allergy and risk groups. Results are fitted into new integrative complex mathematical models to establish suitable biomarkers for early diagnosis, prevention and targets for therapy of allergy-associated diseases such as asthma and atopic dermatitis. Ethics and gender are considered. MeDALL aims at improving health of European citizens, Europe competitiveness and innovative capacity while addressing global health issues.


Grant
Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: NMP.2012.1.3-1 | Award Amount: 12.95M | Year: 2013

The NanoMILE project is conceived and led by an international elite of scientists from the EU and US with the aim to establish a fundamental understanding of the mechanisms of nanomaterial interactions with living systems and the environment, and uniquely to do so across the entire life cycle of nanomaterials and in a wide range of target species. Identification of critical properties (physico-chemical descriptors) that confer the ability to induce harm in biological systems is key to allowing these features to be avoided in nanomaterial production (safety by design). Major shortfalls in the risk analysis process for nanomaterials are the fundamental lack of data on exposure levels and the environmental fate and transformation of nanomaterials, key issues that this proposal will address, including through the development of novel modelling approaches. A major deliverable of the project will be a framework for classification of nanomaterials according to their impacts, whether biological or environmental, by linking nanomaterial-biomolecule interactions across scales (sub-cellular to ecosystem) and establishing the specific biochemical mechanisms of interference (toxicity pathway).


Grant
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2012.2.1.1-3 | Award Amount: 7.86M | Year: 2012

Users of NGS technologies, producing large and numerous distinct types of omics data, demands statistical methods to combat data and knowledge fragmentation and inappropriate procedures for data analysis. Yet, the current a gap between the available tools for analysis of a single omics data-type versus the requirement of biomedical scientists to understand the integrated results derived from several omics data-types, threatens to further increase due to the accelerated capacity of data production. STATegra will therefore improve and develop new statistical methods enabling accurate collection and integration of omics data while providing user-friendly packaging of STATegra tools targeting biomedical scientists. To close the gap between the present sub-optimal utilization of omics data and the power of statistics, STATegra develops statistical methods targeting efficient experimental design, data gathering, missing data, noisy data, current knowledge, meta-analysis and integrative data analysis. Importantly, STATegra facilitates understanding and use of omics data by forcing abstract concepts including knowledge, design, dirty data, visualization, causality and integration to be embedded in a real yet prototypical biomedical context. STATegra is positioned to ensure that the collective output of the statistical STATegra methods is relevant and subject to statistical and experimental validation and iteration. STATegra mimics a user-driven IT development strategy, by involving real biomedical users as beta-testers. To deliver beyond current exploratory tools, our consortium accumulates the necessary strong statistical, technological, and molecular expertise. The strong lead by research intensive SMEs, with proven track-record in software deployment, translates STATegra to a wide existing community base. STATegra accelerates the production of relevant statistical tools impacting a broad community of biomedical scientists in research, industry, and healthcare.


Grant
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2013.2.2.1-2 | Award Amount: 6.32M | Year: 2013

The theme of this collaborative project is development and application of neuroimaging and bioinformatics tools to study lipid metabolism as a common pathogenic link between psychotic disorders and its metabolic co-morbidities. The overall objective is to identify, prioritize and evaluate multi-modal blood and neuroimaging markers with diagnostic potential for prediction and monitoring of psychotic disorders and associated metabolic co-morbidities. We aim to (1) optimise a multidisciplinary approach for combining positron emission tomography (PET) and magnetic resonance imaging (MRI) with metabolomics approaches, (2) develop a PET-method for exploring endocannabinoid pathways in early psychosis in longitudinal study setting, and (3) develop combined PET-MRI biomarker methodology for psychiatric disorders by studying neurotransmitter interactions with multiple PET scan and MRI sequences. The balance of two or more neurotransmitter systems may function as a novel biomarker in these disorders. The expected impacts are (1) etiopathogenic understanding, (2) new validated multi-modal markers for early disease detection and monitoring, (3) new tools for the identification of subjects who may benefit from specific treatment (4) discovery of new avenues for disease prevention and therapy, and (5) new tools and processes for applying brain imaging in personalised medicine. The consortium brings together clinicians, researchers and industry partners in the domains of psychiatry, neuroimaging, metabolic research, systems biology and bioinformatics.


Grant
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2009.5.3 | Award Amount: 4.57M | Year: 2011

Synergy will develop a simulation environment and a decision-support system aiming at enabling deployment of systems medicine. The three core elements are a knowledge base (KB), an inference engine (IE), and a graphical visualisation environment (GVE). The project focuses on patients with chronic obstructive pulmonary disease (COPD).The KB will include five well established physiological models addressing: 1) Central and peripheral O2 transport and utilization, 2) Pulmonary gas exchange, 3) Regional-lung heterogeneities in ventilation and perfusion, 4) Skeletal muscle bioenergetics, and 5) Mitochondrial reactive oxygen species (ROS) generation. These models will be written in systems biology markup language (SBML) and vertically integrated. Ontologies will be used as the default knowledge-representation system. The KB will include multi-level data from experimental studies (BioBridge), data from a multicentre longitudinal study on COPD phenotyping (PAC-COPD) and public datasets.The IE will enable to explore associations over the KB, perform transversal multi-scale model integration and related simulations including interactions among O2-availability/O2-utilization, ROS generation, systemic inflammation and abnormal tissue remodelling.The Web-based GVE will facilitate relevant simulations in a more intuitive way with respect to the state of the art, addressing two main user profiles: bio-researchers and clinicians.The focus will be on underlying mechanisms of COPD phenotypes associated with poor prognosis. Disease model validation and refinement will be done using a well-established, large dataset (ECLIPSE) together with experimental studies designed to test in silico generated hypotheses. Besides the use of the simulation environment by bio-researchers for optimal experimental design, the Synergy platform will be a relevant decision-support tool for integrated healthcare strategies aiming at modulating the evolution of COPDs.


Grant
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2012.2.1.2-1 | Award Amount: 3.93M | Year: 2012

Lung transplantation (LT) is the standard of care for selected patients with chronic respiratory failure. Chronic lung allograft dysfunction (CLAD) (i.e. Bronchiolitis obliterans syndrome (BOS) and Restrictive Allograft Syndrome (RAS)) represents a major health risk for LT recipients, requiring the use of heavy treatments and possible retransplantation. Observed in almost 50% of patients after 5 years post LT, it is currently impossible to predict the appearance of CLAD before the onset of first symptoms. This project aims to develop the SysCLAD model which will allow to predict, within the 1st year post LT, the recipients at risk of developing CLAD by 3 years post LT. Building upon available data from the cohort of lung transplantation (COLT, recruited since mid-2009), this project will integrate new LT recipients to form the European cohort of lung transplantation (ECOLT). The SysCLAD prediction tool will be based on a mathematical model developed through a system biology approach integrating both clinical and biological data collected from a total of 400 LT recipients. The model will be validated on the first 200 LT recipients (3 years follow-up at project start) and refined using the new set of 200 LT data with 3 years follow-up by 2014. The aim is to identify and validate the signature of CLAD both at the clinical and molecular levels to allow for an early recognition and specific interventions in patients at risk of CLAD. The implementation of the model is expected to significantly improve the cost-effectiveness of post-LT treatments, limit the risk of graft rejection in LT recipients and, ultimately lead to an improved quality of life and a prolonged life expectancy of patients following LT. Finally, the SysCLAD model holds further great promises in the context of other chronic bronchial inflammatory diseases of major incidence such as severe asthma and Chronic Obstructive Pulmonary Disease (COPD) to predict decline in lung function.

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