Transfer Learning for T-Cell Response Prediction

• URL: http://arxiv.org/abs/2403.12117v1
• Date: Mon, 18 Mar 2024 17:32:19 GMT
• Title: Transfer Learning for T-Cell Response Prediction
• Authors: Josua Stadelmaier, Brandon Malone, Ralf Eggeling,
• Abstract summary: We study the prediction of T-cell response for specific given peptides. We show that the danger of inflated predictive performance is not merely theoretical but occurs in practice.
• Score: 0.1874930567916036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the prediction of T-cell response for specific given peptides, which could, among other applications, be a crucial step towards the development of personalized cancer vaccines. It is a challenging task due to limited, heterogeneous training data featuring a multi-domain structure; such data entail the danger of shortcut learning, where models learn general characteristics of peptide sources, such as the source organism, rather than specific peptide characteristics associated with T-cell response. Using a transformer model for T-cell response prediction, we show that the danger of inflated predictive performance is not merely theoretical but occurs in practice. Consequently, we propose a domain-aware evaluation scheme. We then study different transfer learning techniques to deal with the multi-domain structure and shortcut learning. We demonstrate a per-source fine tuning approach to be effective across a wide range of peptide sources and further show that our final model outperforms existing state-of-the-art approaches for predicting T-cell responses for human peptides.

Related papers

• Generalize Drug Response Prediction by Latent Independent Projection for Asymmetric Constrained Domain Generalization [11.649397977546435]
  We propose a novel domain generalization framework, termed panCancerDR, to address this challenge. We conceptualize each cancer type as a distinct source domain, with its cell lines serving as domain-specific samples. Our empirical experiments demonstrate that panCancerDR effectively learns task-relevant features from diverse source domains.
  arXiv  Detail & Related papers  (2025-02-06T12:53:45Z)
• DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction [38.358558338444624]
  We introduce a domain-adaptive peptide-agnostic learning framework DapPep for universal TCR-antigen binding affinity prediction. DapPep consistently outperforms existing tools, showcasing robust generalization capability. It proves effective in challenging clinical tasks such as sorting reactive T cells in tumor neoantigen therapy and identifying key positions in 3D structures.
  arXiv  Detail & Related papers  (2024-11-26T18:06:42Z)
• BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping [64.8477128397529]
  We propose a training-required and training-free test-time adaptation framework. We maintain a light-weight key-value memory for feature retrieval from instance-agnostic historical samples and instance-aware boosting samples. We theoretically justify the rationality behind our method and empirically verify its effectiveness on both the out-of-distribution and the cross-domain datasets.
  arXiv  Detail & Related papers  (2024-10-20T15:58:43Z)
• Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties [5.812284760539713]
  Multi-Peptide is an innovative approach that combines transformer-based language models with Graph Neural Networks (GNNs) to predict peptide properties. Evaluations on hemolysis and nonfouling datasets demonstrate Multi-Peptide's robustness, achieving state-of-the-art 86.185% accuracy in hemolysis prediction. This study highlights the potential of multimodal learning in bioinformatics, paving the way for accurate and reliable predictions in peptide-based research and applications.
  arXiv  Detail & Related papers  (2024-07-02T20:13:47Z)
• Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learning [6.810949980810495]
  scAdaDrug is a novel multi-source domain adaptation model powered by adaptive importance-aware representation learning. Our model achieved state-of-the-art performance in predicting drug response on multiple independent datasets.
  arXiv  Detail & Related papers  (2024-03-08T12:31:03Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning [73.75282761503581]
  We propose DiffTPT, which leverages pre-trained diffusion models to generate diverse and informative new data. Our experiments on test datasets with distribution shifts and unseen categories demonstrate that DiffTPT improves the zero-shot accuracy by an average of 5.13%.
  arXiv  Detail & Related papers  (2023-08-11T09:36:31Z)
• Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding [57.89530563948755]
  This work provides a benchmark analysis of peptide encoding with advanced deep learning models. It serves as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.
  arXiv  Detail & Related papers  (2023-07-17T00:43:33Z)
• Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information [28.4434795102787]
  We propose a graph variational Bayesian causal inference framework to predict a cell's gene expressions under counterfactual perturbations. We leverage information representing biological knowledge in the form of gene regulatory networks to aid individualized cellular response predictions.
  arXiv  Detail & Related papers  (2022-09-30T22:13:57Z)
• Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries [69.53730499849023]
  We show that adversarial examples can be successfully transferred to another independently trained model to induce prediction errors. We propose a deep learning-based pre-processing mechanism, which we refer to as a robust transferable feature extractor (RTFE)
  arXiv  Detail & Related papers  (2022-09-14T21:09:34Z)
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response [49.86828302591469]
  In this paper, we apply transfer learning to the prediction of anti-cancer drug response. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
  arXiv  Detail & Related papers  (2020-05-13T20:29:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.