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Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

Anthony Culos, Amy S. Tsai, Natalie Stanley, Martin Becker, Mohammad S. Ghaemi, David R. McIlwain, Ramin Fallahzadeh, Athena Tanada, Huda Nassar, Camilo Espinosa, Maria Xenochristou, Edward Ganio (+19 others)
2020 Nature Machine Intelligence  
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]

Anthony Culos, Amy S. Tsai, Natalie Stanley, Martin Becker, Mohammad S. Ghaemi, David R. Mcilwain, Ramin Fallahzadeh, Athena Tanada, Huda Nassar, Edward Ganio, Laura Peterson, Xiaoyuan Han (+17 others)
2020 bioRxiv   pre-print
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?

Yoram Vodovotz, Ashley Xia, Elizabeth L. Read, Josep Bassaganya-Riera, David A. Hafler, Eduardo Sontag, Jin Wang, John S. Tsang, Judy D. Day, Steven H. Kleinstein, Atul J. Butte, Matthew C. Altman (+2 others)
2017 Trends in immunology  
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/ pmid:27986392 pmcid:PMC5695553 fatcat:frk2t64wsnbilm4ojuuzcmxozi

Overcoming Immunological Challenges Limiting Capsid-Mediated Gene Therapy With Machine Learning

Anna Z Wec, Kathy S Lin, Jamie C Kwasnieski, Sam Sinai, Jeff Gerold, Eric D Kelsic
2021 Frontiers in Immunology  
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

Sanchita Bhattacharya, Zicheng Hu, Atul J. Butte
2021 Frontiers in Immunology  
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]

Óscar Lapuente-Santana, Federico Marini, Arsenij Ustjanzew, Francesca Finotello, Federica Eduati
2021 bioRxiv   pre-print
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

Meghna Verma, Raquel Hontecillas, Vida Abedi, Andrew Leber, Nuria Tubau-Juni, Casandra Philipson, Adria Carbo, Josep Bassaganya-Riera
2016 Frontiers in Nutrition  
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

Mark Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold, Ellen Kuhl
2019 npj Digital Medicine  
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

Holger Fröhlich, Rudi Balling, Niko Beerenwinkel, Oliver Kohlbacher, Santosh Kumar, Thomas Lengauer, Marloes H. Maathuis, Yves Moreau, Susan A. Murphy, Teresa M. Przytycka, Michael Rebhan, Hannes Röst (+8 others)
2018 BMC Medicine  
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 ( 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

Laura Bravo-Merodio, Animesh Acharjee, Jon Hazeldine, Conor Bentley, Mark Foster, Georgios V. Gkoutos, Janet M. Lord
2019 Scientific Data  
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

Matthieu Van Tilbeurgh, Katia Lemdani, Anne-Sophie Beignon, Catherine Chapon, Nicolas Tchitchek, Lina Cheraitia, Ernesto Marcos Lopez, Quentin Pascal, Roger Le Grand, Pauline Maisonnasse, Caroline Manet
2021 Vaccines  
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

Francesco Pappalardo, Giulia Russo, Pedro A. Reche
2020 BMC Bioinformatics  
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 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

Meghna Verma, Raquel Hontecillas, Nuria Tubau-Juni, Vida Abedi, Josep Bassaganya-Riera
2018 Frontiers in Nutrition  
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]

Alex J. Brown, Igor Snapkov, Rahmad Akbar, Milena Pavlović, Enkelejda Miho, Geir K. Sandve, Victor Greiff
2019 arXiv   pre-print
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

Analeigha V. Colarusso, Isabella Goodchild-Michelman, Maya Rayle, Ali R. Zomorrodi
2021 Current Opinion in Systems Biology  
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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