Such tools could eventually even compose entire reports including supporting texts and figures of disease maps for each individual feature in a machine learning model. Such reports could thus automatically contextualize each variable with the multitude of available biomedical knowledge in a fully interactive fashion.

The physician could zoom and filter specific aspects of a model on demand. Such a tool could help physicians to understand disease development over time. Individual patient trajectories could then be followed and compared to determine, for example, which intervention is appropriate for which patient and at what time [ 54 ].

Similar concepts have been developed in other contexts, e. for estimating the in-vivo fitness landscape experienced by HIV-1 under drug selective pressure [ 55 ]. The development of such methods and software systems will be a major effort and will likely require a substantial text analytical and software engineering component.

However, such systems could greatly facilitate the communication between computational scientists and physicians and help make complex machine learning models more interpretable. Machine learning models are typically neither mechanistic nor causal. They largely capture non-linear correlations between predictor variables and clinical outcomes and are thus often criticized for being black boxes.

The main advantage of modern machine learning approaches is that they neither require a detailed prior understanding of cause—effect relationships nor of detailed mechanisms. The main limitation is the difficulty to interpret them see previous Section. A major question thus relates to how far machine learning methods could evolve into more causal models in the future.

Causal graphical models causal Bayesian networks in particular constitute an established framework for causal reasoning [ 56 ]. They provide a compact mathematical and visual representation of a multivariate distribution, and more importantly, they allow to make predictions of the system under unseen interventions e.

a new treatment or a gene knockout. Under appropriate assumptions, causal graphical models can be learned from observational data [ 57 , 58 , 59 ]. In doing so, it is also possible to incorporate background knowledge or to allow for hidden or unmeasured confounders.

We refer to [ 60 ] for a review paper. Causal graph learning methods may play an increasingly important role in the future in identifying predictor variables with causal influence on clinical outcomes [ 61 ] and may thus help to move towards a causal interpretation of predictor variables in a machine learning model [ 62 ].

However, there are non-trivial challenges that need to be addressed, such as dealing with violations of assumptions, high computational costs and non-linear relationships [ 63 ].

Despite the increasing availability of massive datasets, the predictive power of most of the available disease models does not yet satisfy the requirements for clinical practice.

One of the reasons is that, in principle, predictive disease models must cover all relevant biotic and abiotic mechanisms driving disease progression in individual patients. Although the primary disease-driving mechanisms are often aberrations at the molecular level, such as mutations in the genome, disease progression is affected by the robustness of the overall system.

However, biological systems have established a multitude of repair mechanisms to compensate for the effects of molecular aberrations, thus introducing feedback loops and non-linear interactions into the system [ 64 ].

Overall, disease progression is a process affected by a multitude of highly diverse mechanisms across biological hierarchies, which are differently expressed in individual patients. Thus, a disease model, designed for applications in precision medicine in clinics, must in principle integrate three conceptual layers:.

A core disease model CDM represents only the known intra- and inter-cellular processes that are the key drivers of the disease in an average patient.

The CDM must be adapted to the individual patient and their specific medical history and environment, such as genetic variations, co-morbidities or physiology, by environment adaption models EAM. The EAM must provide an individualization of the parameters controlling the CDM, eventually combined with an individualized re-structuring of the CDM, e.

Monitoring models must be developed to describe how clinically accessible outcome measurements representing the disease evolution are linked to the CDM. Today, fully mechanistic models exist for a series of disease-driving core processes at the molecular and cell population level [ 65 ]. However, broader application of mechanistic modelling to implement the CDM for complex diseases is hampered by insufficient knowledge of the interaction of the core disease-driving mechanisms across scales.

Even worse, the relevant mechanisms for EAM and monitoring models are almost never completely known. Altogether, it thus seems unlikely that fully mechanistic models will play a dominant role in personalized medicine in the near future. While machine learning models are not harmed by insufficient biomedical knowledge, they are often criticized for their black-box character.

Hybrid modelling, also named grey-box or semi-parametric modelling, is an integrative approach combining available mechanistic and machine learning-based sub-models into a joint computational network. The nodes represent model components and the edges their interaction.

First combinations of mechanistic and data-driven models have been developed for chemical and biotech process modelling [ 66 , 67 ]. For example, neural networks have been used to compensate the systematic errors of insufficient mechanistic models, to estimate unobservable parameters in mechanistic models from observable data, or to estimate the interaction between different mechanistic sub-models [ 68 , 69 ].

A further successful example of hybrid modeling comprises learning the drug mechanism of action from data [ 70 , 71 ]. Hybrid models may thus be a way to combine the positive aspects of fully mechanistic and purely data-driven machine learning models. First showcases have demonstrated the potential, but more successful applications are needed.

Moreover, a deeper understanding of the theoretical capabilities of hybrid models as well as their limitations is necessary.

One of the key objectives of personalized medicine is predicting the risk of an individual person to develop a certain disease or, if the disease has already developed, to predict the most suitable therapy.

This also includes predicting the likely course of disease progression. Disease trajectories entail all the hallmarks of a complex system. In this sense, modeling disease trajectories is not fundamentally different from attempts to model and simulate other complex systems such as the climatological, ecological, economic or social systems.

In many of these highly nonlinear, complex systems with thousands or millions of components, involving redundant and intertwined feedback relations, so called critical transitions or catastrophic shifts can be observed.

Such transitions are defined by critical thresholds, sometimes called tipping points at which a system transitions abruptly from one state to another, seem to exist. However, in many of these cases, critical transitions are extremely difficult to predict in advance. For certain diseases, we believe that the concept of critical transitions might also be applicable in the context of personalized medicine.

Tipping points are often observed during the course of acute or chronic disease development. The ability to predict a critical transition of a developing disease before it really happens would be highly desirable and provide very valuable pre-disease biomarkers.

Recently, Liu et al. The idea is that, during the disease trajectory, a subset of genes starts to fluctuate and leads to a destabilization of a possibly high-dimensional attractor state. By measuring the changes in gene correlation in addition to changes in the variation of gene expression, a quantitative index was proposed as an early warning signal for a critical transition.

From a broader perspective, evolutionary principles could help to improve our understanding of human disease [ 73 ]. Evolutionarily conserved control genes are probably highly relevant for the proper functioning of molecular pathways [ 74 ], and evolutionary history of human disease genes reveals phenotypic connections and comorbidities among some diseases [ 75 ].

We are now at the verge of reconstructing the molecular and cellular circuitry of embryogenesis [ 76 ]. In addition, whole-genome next-generation sequencing efforts of hundreds of thousands and soon Millions of patients with common and rare diseases provide us with a rich genotype—phenotype landscape underlying the development and manifestation of human diseases.

Such data provides interesting opportunities to better understand the influence of genomic variants on evolutionarily conserved genomic regions and molecular networks in the context of human diseases. Evolutionary conservation might be relevant for constraining models and simulating human diseases.

Biologically possible and plausible disease trajectories are likely limited by the topological and dynamic upper and lower bounds that are set by the evolutionary history of a disease network.

We need to understand the effects of genetic variation on the resulting phenotypic variation. This requires close cooperation between disciplines striving for an integration of the concepts of ontogeny and phylogeny.

Human diseases must be seen in the light of evolution and models of human diseases need to integrate data, information, and knowledge from developmental biology and embryology. In the era of growing data volumes and ever shrinking costs for data generation, storage, and computation, personalized medicine comes with high promises, which can only be realized with the help of advanced algorithms from data science, particularly machine learning.

Modern machine learning algorithms have the potential of integrating multi-scale, multi-modal, and longitudinal patient data to make relatively accurate predictions, which, in some examples, may even exceed human performance [ 21 ].

Large commercial players that are now entering the field of medicine underline the potential that is widely seen for computational solutions. However, the current hype around AI and machine learning must be contrasted with reality.

While many prediction algorithms for patient stratification have been published over the last decade, only very few approaches have reached clinical practice so far. Major existing bottlenecks discussed in this paper include the 1 lack of sufficient prediction performance due to a lack of signals in the employed data; 2 challenges with model stability and interpretation; 3 a lack of validation of stratification algorithm via prospective clinical trials, which demonstrate benefit compared to standard of care; and 4 general difficulties to implement a continuous maintenance and updating scheme for decision support systems.

In addition, general concerns around data privacy as well as ethical and legal aspects must not be overlooked. There is a need to better manage the partially unrealistic expectations and concerns about data science and AI-based solutions.

In parallel, computational methods must advance in order to provide direct benefit to clinical practice. Current algorithms are far from being able to recommend the right treatment at the right time and dose for each patient. More speculatively, a broader understanding of human disease, incorporating findings from basic research and evolutionary studies, might help the creation of entirely new concepts for simulating human diseases and predicting optimal intervention points.

Overall, the ambition of research towards personalized medicine should be to move from a system analysis perspective such as in molecular biology to a system control view that allows for the planning of optimal medical interventions at the right time and dose on an individualized basis.

Novel computational modeling approaches that go beyond the current machine learning methodology may play an increasing role for that purpose. In this context, it must be emphasized that no algorithm is meant to replace a physician.

Rather, the idea is to provide them a tool at hand, which supports their decisions based on objective, data-driven criteria and the wealth of available biomedical knowledge.

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Download references. We thank Colin Birkenbihl for providing Fig. html within the 7th Framework Programme of the European Union. We thank Schloss Dagstuhl for the support of our meeting. TMP was supported in part by the Intramural Research Program of the National Institutes of Health, National Library of Medicine.

MS was partially supported by the Robert-Bosch Stiftung, Stuttgart, Germany, and the European Commission Horizon UPGx grant UCB Biosciences GmbH, Alfred-Nobel-Str. University of Luxembourg, 6 avenue du Swing, , Belvaux, Luxembourg. Department of Biosciences and Engineering, ETH Zurich, Mattenstr.

Department of Computer Science, University of Memphis, Dunn Hall, Memphis, TN, , USA. Max-Planck-Institute for Informatics, , Saarbrücken, Germany. ETH Zurich, Seminar für Statistik, Rämistrasse , , Zurich, Switzerland. University of Leuven, ESAT, Kasteelpark Arenberg 10, , Leuven, Belgium.

Harvard University, Science Center Suite, Oxford Street, Cambridge, MA, , USA. National Center of Biotechnology Information, National Institute of Health, Rockville Pike, Bethesda, MD, , USA.

Novartis Institutes for Biomedical Research, , Basel, Switzerland. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, College Street, Toronto, ON, M5S 3E1, Canada. RWTH Aachen, Joint Research Center for Computational Biomedicine, Pauwelsstrasse 19, , Aachen, Germany.

Margarete Fischer-Bosch Institute of Clinical Pharmacology, Aucherbachstrasse , , Stuttgart, Germany. University of Regensburg, Institute of Functional Genomics, Am BioPark 9, , Regensburg, Germany. ETH Zurich, NEXUS Personalized Health Technol. Georgia Tech University, Atlantic Drive, Atlanta, GA, , USA.

Institute for Computer Science, University of Bonn, Endenicher Allee 19a, , Bonn, Germany. Pfizer, Worldwide Research and Development, Linkstraße 10, , Berlin, Germany. Faculty of Computer and Information Science, University of Ljubljana, Večna pot , SI, Ljubljana, Slovenia.

University of Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19c, , Bonn, Germany. Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, , Tübingen, Germany. Quantitative Biology Center, University of Tübingen, Auf der Morgenstelle 8, , Tübingen, Germany.

Institute for Translational Bioinformatics, University Medical Center Tübingen, Sand 14, , Tübingen, Germany. University of Tübingen, Departments of Clinical Pharmacology and of Pharmacy and Biochemistry, Tübingen, Germany. You can also search for this author in PubMed Google Scholar.

All authors contributed to the content of this paper. HF guided the draft of the manuscript. All authors have read and approved the content.

Correspondence to Holger Fröhlich. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The latest Futurescan survey data show that most responding organizations are in the middle of this journey. Health care executives will need to know the current experiences among various generational cohorts and how to meet their specific needs, including:.

This unique group wants the best and their demands have transformed areas like senior living, leisure and recreation.

Concierge and luxury health care emerged because baby boomers want high-touch services, Daniels says, noting that this creates a growth opportunity for health care organizations.

Baby boomers have a high degree of confidence in brands that convey quality and are willing to pay for name-brand patient experiences. As a result, the market for destination medicine is likely to grow.

Gen Xers are a silent but emerging demographic for patient acquisition, Kelly says. This cohort, more than any other, is handling multigenerational patient direction for their own children and often their parents. Health care organizations will need to respond by coordinating care in a family-convenient way.

As more care moves into the home setting and family members take on a greater role as caregivers, Gen Xers will need more support from health care organizations. Now the largest living adult generation in the U. They value convenience first and foremost.

They look for care close to where they want to receive it, need to know wait times, want to be able to message providers and find hours and reviews online. Identifying all the locations affiliated with your organization and optimizing your presence on Google Maps has never been more important, Kelly says.

This age bracket has the highest percentage of young adults living at home in the post-World War II era, Daniels notes. They are comfortable with both virtual care and text-based care and are less likely to engage in face-to-face, in-person care. This generation is more willing to seek care for behavioral health issues, particularly among transgender individuals, Daniels says.

Wishart has made many ground-breaking contributions to science. He led the Human Metabolome Project, a multi-university, multi-investigator project that catalogued all of the known metabolites in human tissues and bio-fluids.

The University of Alberta recently recognized Dr. Wishart with the title of Distinguished University Professor and the Alumni Award. The healthcare system is under increasing pressure to deliver high quality care effectively and efficiently.

Population wide advances have been effective at improving health, but new technologies like those employed by Molecular You can transform health management to be tailored to each unique individual. Better matching of therapies to the individual and the disease can significantly improve the safety and efficiency of treatment.

The most effective way to reduce costs and suffering is by preventing disease from happening. Rob Fraser, Co-founder, President and CEO of Molecular You. Fraser further explained.

We are committed to advancing these world-leading technologies to deliver personalized, preventive health management, with the goal of reducing the costs and burden of chronic disease.

Molecular You health assessments provide insights into personal health risks and give practical individualized nutrition and exercise plans to empower clients to manage their own health care goals. Working together with practitioners and corporations, Molecular You has a range of packages to suit health concerns and priorities, that with genetics, can also help to optimize medication efficacy and safety.

What challenges do companies in this market face today? In this webcast, hear a physician and the cofounder of a leading personalized vitamin subscription brand discuss key issues, including the role of artificial intelligence, data protection, and scientific substantiation, as well as how to grow the audience for personalized nutrition products into the future.

Watch for free! And none of it would have been possible absent data. In fact, argues Nathan Price, chief scientific officer, Thorne New York , the convergence of at least three key technologies will drive personalized nutrition in theory and in practice, both now and for the foreseeable future.

While COVID helped break down those barriers, so did the steady proliferation of tech-enabled direct-to-consumer services that have sprung up since. More of those products are landing there by the quarter, too, with many aiming directly at digestive health.

And it makes sense. adults suffer from some kind of gut-health issue, so a large fraction of the population is looking for solutions. survey said they were much more likely to prioritize personalization than they were the year before, and that Comet Market Research placed personalization among its top-three picks for the gut-health market in And where do they get the data from which to tailor those bespoke formulations?

From tests that consumers take often in the comfort of their own homes. Meanwhile, fecal occult blood tests detect amounts of blood in the stool small enough to elude sight. Further stool testing might identify parasites, bacteria, or pathogens that could cause gastrointestinal symptoms while also revealing signs of inflammation or other changes in the digestive tract.

Blood tests also spot infection and inflammation, as well as signs of malabsorption possibly associated with the gut, Price continues. Breath tests that measure exhaled gases such as hydrogen and methane can detect lactose intolerance, small-intestinal bacterial overgrowth, and conditions like fructose malabsorption, while assays of certain biomarkers—enzymes, for instance—open a window onto digestive function.

Price notes as a case in point that low levels of elastase, a pancreatic enzyme that digests proteins, might signal pancreatic insufficiency and the downstream potential for nutrient malabsorption and deficiency.

So in cases where test results show scant presence of beneficial species like Bifidobacterium or Lactobacillus, a probiotic supplement containing those species might be in order.

Which, after all, is the hallmark of personalization. And at-home test methods remain limited. Data analysis poses further obstacles, he continues. And the products themselves are imperfect. Bio-banks are growing with testable material, and artificial intelligence is improving the analysis and comparison of results.

Price predicts more integration among wellness disciplines, too. And in the future, we expect even more personalization. Performance nutrition has something for everybody. Conference Coverage.

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