Abstract: Methods and systems for vaccine generation include determining genome sequence for target. A tumorigenicity score is determined for neoantigens of the target using a variance analysis in a linear random effects model. The neoantigens are ranked based on the tumorigenicity score. A vaccine is generated based on the ranked neoantigens.

Abstract: Systems and methods for particularly t-cell receptor complex optimization with reinforcement learning. Classifiers using variational information bottleneck with attention of experts (AVIB classifiers) can be fine-tuned for different representations of desired t-cell receptor (TCR) sequences for a patient. Proximal policy optimization (PPO) models can be trained with reinforcement learning using the AVIB classifiers as reward functions to achieve higher affinity in generating interaction sequences for the desired TCR sequences through automated decision making. The interaction sequences can be clustered based on k-mer profiles to select the interaction sequences having highest binding scores in each cluster as final sequences. A biological functional potency of the final sequences can be validated.

Abstract: Methods and systems for patient stratification include learning interdependent biomarkers as integrated time-series machine learning models. A disease stage is identified for a patient based on collected biomarker data. A treatment for the patient is performed based on the identified disease stage and a predicted future response of the patient.

Abstract: Methods and systems for tailored treatment include embedding a T-cell receptor (TCR) sequence and embedding an epitope sequence. The embedded TCR sequence and the embedded epitope sequence are processed with a discriminator to generate a multi-class label. The multi-class label is classified to generate a binary binding prediction. A treatment is generated based on the binary binding prediction.

Abstract: The present disclosure relates to medical and health decision making and, more particularly, to treatment based on tumor clonality estimates. Methods and systems include analyzing genotypes of a tumor to identify clonality sub-types present in the tumor, using a machine learning model that is trained to learn a multilevel evolutionary process or genetic algorithm, by using a recursive Wasserstein objective to output the clonal sub-types, an ancestral structure, and a fitness model. A treatment is generated, tailored to the tumor using the clonal sub-types, and subclonal properties predicted by the model, such as subclone fitness.

Abstract: Systems and methods for t-cell receptor complex optimization using quantum variational autoencoders. Mixed-state t-cell receptor (TCR) embeddings and mixed-state major histocompatibility complex peptide (pMHC) embeddings can be generated by embedding input TCR sequences and input pMHC sequences, respectively, using a quantum variational autoencoder (QVAE). A combinatorial optimization of the mixed-state TCR embeddings while fixing the mixed-state pMHC embeddings can be performed using a machine learning-based predictor. TCR sequences from the mixed-state TCR embeddings and the mixed-state pMHC embeddings, after the combinatorial optimization, can be decoded using the QVAE to generate an optimized TCR sequence. The optimized TCR sequence can be synthesized as a synthetic compound for downstream tasks.

Abstract: Methods and systems for peptide binding prediction include predicting a three-dimensional (3D) structure of a peptide and a major histocompatibility (MHC) complex to generate a graph. The 3D structure is refined by pruning edges of the graph having a distance between the peptide and the MHC complex that is below a threshold value. Models for MHC-I and MHC-II binding prediction are trained, including Bayesian reweighting of data for the MHC-II binding prediction, using the pruned graph.

Abstract: A method for graph analysis includes identifying trainable control parameters of a graph refinement function. Sample graph refinements of an input graph are generated, using control parameters sampled from a variational distribution. Graph refinement control parameters associated with a sample graph refinement that has a highest performance score are selected when used to train a graph neural network. Graph analysis is performed on the input graph using the selected graph refinement parameters to produce a refined graph on new test samples. An action is performed responsive to the graph analysis.