Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power
Culos, A. E.; Tsai, A. S.; Stanley, N.; Becker, M.; Ghaemi, M. S.; McIlwain, D. R.; Tanada, A.; Fallahzadeh, R.; Nassar, H.; Ganio, E.; Peterson, L.; Han, x.; Stelzer, I.; Ando, K.; Gaudilliere, D.; Phongpreecha, T.; Maric, I.; Chang, A. L.; Shaw, G. M.; Stevenson, D. K.; Bendall, S.; Davis, K.; Fantl, W. C.; Nolan, G. P.; Hastie, T.; Tibshirani, R. j.; Angst, M.; Gaudilliere, B.; Aghaeepour, N.
Show abstract
The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia 95%
- THLANet: A Deep Learning Framework for Predicting TCR-pHLA Binding in Immunotherapy Applications 93%
- An Integrated Approach to the Characterization of Immune Repertoires Using AIMS: An Automated Immune Molecule Separator 93%
Similar papers in this journal
- Artificial neural networks enable genome-scale simulations of intracellular signaling 95%
- Community assessment of methods to deconvolve cellular composition from bulk gene expression 94%
- FastCCC: A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies 94%
Similar papers in this journal
- DAISM-DNNXMBD: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks 94%
- RiskPath : Explainable deep learning for multistep biomedical prediction in longitudinal data 94%
- Federated Learning for multi-omics: a performance evaluation in Parkinson's disease 93%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.