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Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions
2020
Nature Machine Intelligence
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We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. ...
The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. ...
In this Article, we proposed a collaborative framework that enables integration of prior knowledge of cell signalling pathways in a machine learning algorithm to improve the predictions and robustness ...
doi:10.1038/s42256-020-00232-8
pmid:33294774
pmcid:PMC7720904
fatcat:kptdfh4mgjahjojfccqrhhbvbi
Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power
[article]
2020
bioRxiv
pre-print
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We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. ...
The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. ...
In this article, we proposed a collaborative framework that enables integration of prior knowledge of cell signaling pathways in a machine learning algorithm to increase the predictive power and robustness ...
doi:10.1101/2020.02.26.967232
fatcat:nlgxcvgk7jbjpk6sf55gremfsa
Solving Immunology?
2017
Trends in immunology
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acquisition, data-driven modeling and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies. ...
Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. ...
We apologize for the absence of the discussion of many approaches and of many important references due to space and citation constraints. ...
doi:10.1016/j.it.2016.11.006
pmid:27986392
pmcid:PMC5695553
fatcat:frk2t64wsnbilm4ojuuzcmxozi
Overcoming Immunological Challenges Limiting Capsid-Mediated Gene Therapy With Machine Learning
2021
Frontiers in Immunology
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Here we outline how machine learning, advances in viral immunology, and high-throughput measurements can enable engineering of a new generation of de-immunized capsids beyond the antigenic landscape of ...
Recent advances in high-throughput DNA synthesis, multiplexing and sequencing technologies have accelerated engineering of improved capsid properties such as production yield, packaging efficiency, biodistribution ...
be integrated with auxiliary input (e.g., domain knowledge) to propose a batch of new sequences (generator model). ...
doi:10.3389/fimmu.2021.674021
pmid:33986759
pmcid:PMC8112259
fatcat:kw6i56outzevdhqqjzxdcrk4ti
Opportunities and Challenges in Democratizing Immunology Datasets
2021
Frontiers in Immunology
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Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence ...
We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more. ...
CyTOF Machine learning Identify latent CMV infection using a deep learning model Gielis et al. (39) 31849987 TCR sequencing Machine learning Predict antigen specificity using a machine learning model Berry ...
doi:10.3389/fimmu.2021.647536
pmid:33936065
pmcid:PMC8086961
fatcat:x5yp4fhgzfdabjmlqjr6spzuly
easier: interpretable predictions of antitumor immune response from bulk RNA-seq data
[article]
2021
bioRxiv
pre-print
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We have recently presented a novel approach that integrates transcriptomics data with biological knowledge to study tumors at a more holistic level. ...
Validated in four different solid cancers, our approach outperformed the state-of-the-art methods to predict response to ICB. ...
We recently developed a novel approach that uses machine learning to combine transcriptomics data with prior information on TME, to derive system-based signatures that provide a mechanistic understanding ...
doi:10.1101/2021.11.26.470099
fatcat:owgm2ofuevbfphcz2odrcv6fxe
Modeling-Enabled Systems Nutritional Immunology
2016
Frontiers in Nutrition
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Emerging global mechanistic insights in the field of nutritional immunology cannot be gained through reductionist methods alone or by analyzing a single nutrient at a time. ...
We propose to investigate nutritional immunology as a massively interacting system of interconnected multistage and multiscale networks that encompass hidden mechanisms by which nutrition, microbiome, ...
using advanced machine-learning methods. ...
doi:10.3389/fnut.2016.00005
pmid:26909350
pmcid:PMC4754447
fatcat:3xomgnn7pzhavmlrcqruyvgwta
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
2019
npj Digital Medicine
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The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this ...
Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems ...
knowledge into machine learning. ...
doi:10.1038/s41746-019-0193-y
pmid:31799423
pmcid:PMC6877584
fatcat:uhgdhq7rjffqnboydb3e6d2tuu
From hype to reality: data science enabling personalized medicine
2018
BMC Medicine
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While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. ...
The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective ...
CB has been partially supported by the IMI project AETIONOMY (https://www.aetionomy.eu/en/vision.html) within the 7th Framework Programme of the European Union. ...
doi:10.1186/s12916-018-1122-7
pmid:30145981
pmcid:PMC6109989
fatcat:wfdozg4funaz5d4nn34bn5i4r4
Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction
2019
Scientific Data
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This performance provides an 8% improvement over the Probability of Survival (PS14) outcome measure and a 13% improvement over the New Injury Severity Score (NISS) for identifying patients at risk of MODS ...
We developed a pipeline using Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods that were able to identify 3 physiological features (decrease in neutrophil CD62L and CD63 ...
Our pipeline 29 is composed of a variety of statistical machine learning modules described below. Feature selection module. ...
doi:10.1038/s41597-019-0337-6
pmid:31857590
pmcid:PMC6923383
fatcat:4bkuozdkxfbdzciglmx5hjbnae
Predictive Markers of Immunogenicity and Efficacy for Human Vaccines
2021
Vaccines
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Identifying reliable predictive markers of immunogenicity can help to select and develop promising vaccine candidates during early preclinical studies and can lead to improved, personalized, vaccination ...
Vaccination aims at reaching sterilizing immunity, however assessing vaccine efficacy is still challenging and underscores the need for a better understanding of immune protective responses. ...
Conflicts of Interest: A.-S.B. is the recipient of Sanofi Innovation Award (iAward program), Europe 2020, on Trained Immunity-Inducing Vaccines. The remaining authors declare no conflict of interest. ...
doi:10.3390/vaccines9060579
pmid:34205932
pmcid:PMC8226531
fatcat:rxmwkmsg2zbanjncffuxkmcpte
Toward computational modelling on immune system function
2020
BMC Bioinformatics
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of digital patients to machine learning applied to predict type-2 diabetes risk. ...
The workshop has continued its growing tendency, with a total of 18 accepted papers that have been presented in a full day workshop. ...
The full contents of the supplement are available at https ://bmcbi oinfo rmati cs.biome dcent ral.com/artic les/suppl ement s/volum e-21-suppl ement -17. ...
doi:10.1186/s12859-020-03897-5
pmid:33308137
fatcat:nkcsqmolsjholihix7ajahg2ry
Challenges in Personalized Nutrition and Health
2018
Frontiers in Nutrition
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A comprehensive systems-wide mechanistic understanding of the interplay between nutrition and health benefits requires the knowledge of network dynamics in the context of health, pre-disease, and disease ...
The computational pipeline included mechanistic ordinary DE based models (33) with stochastic simulations and an ensemble of advanced ML methods. ...
doi:10.3389/fnut.2018.00117
pmid:30555829
pmcid:PMC6281760
fatcat:xd52ejz2rree3lb5fadd74dqcq
Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires
[article]
2019
arXiv
pre-print
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A long-standing dream of immunoengineers has been, therefore, to mechanistically understand how the immune system sees, reacts and remembers antigens. ...
Our review indicates that the merger of fundamental immunology, computational immunology and digital-biotechnology minimizes black box engineering, thereby advancing both immunological knowledge and as ...
361, 362 into future machine learning architectures in an effort to improve signal detection and decrease the influence of bystander noise. ...
arXiv:1904.04105v2
fatcat:4dxyjc2aevd5zeue2cjlhgkoju
Computational modeling of metabolism in microbial communities on a genome-scale
2021
Current Opinion in Systems Biology
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In addition, we review computational tools using GEMs for the design of microbial communities and recent efforts to integrate GEMs and machine learning for predicting interspecies interactions. ...
Computational simulation methods using GEMs of metabolism A GEM of metabolism is a compilation of all metabolic reactions that an organism can carry out as encoded by its genome and an additional ad hoc ...
Acknowledgments This work was supported by the faculty start-up funding by the Mucosal Immunology and Biology Research Center at Massachusetts General Hospital to ARZ and a Harvard's Museum of Comparative ...
doi:10.1016/j.coisb.2021.04.001
fatcat:2c6cmplj5rhhvj6hhvlmtxo5rq
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