The repository houses the IntegratedLearner R package for multi-omics prediction and classification. Binary, continuous, and survival outcomes are supported through a single high-level interface.

IntegratedLearner requires the following R package: devtools (for installation only). Please install it before installing IntegratedLearner, which can be done as follows (execute from within a fresh R session):

install.packages("devtools")
library(devtools)

Once the dependencies are installed, IntegratedLearner can be loaded using the following command:

devtools::install_github("himelmallick/IntegratedLearner")
library(IntegratedLearner)

• Supports both PCL and MAE input modes
• Supports binary, continuous, and survival outcomes
• Supports early, late, and intermediate fusion in one interface
• Integrates with SuperLearner for non-survival models (SL.*)
• Uses mlr3/mlr3proba ecosystem for survival models (surv.*)
• Visualization using built-in plotting
• Built-in layer weights and feature-importance outputs for interpretability
• Nested cross-validation to estimate prediction performance
• Multicore and multinode parallelization for scalability (Not yet available)

The package vignette demonstrates binary, continuous, survival, PCL, and MAE workflows. This vignette can be viewed online here.

IntegratedLearner provides an integrated machine learning framework to 1) consolidate predictions by borrowing information across several longitudinal and cross-sectional omics data layers, 2) decipher the mechanistic role of individual omics features that can potentially lead to new sets of testable hypotheses, and 3) quantify uncertainty of the integration process. Three integration paradigms are supported: early, late, and intermediate.

For non-survival outcomes, late fusion proceeds by 1) fitting a machine learning algorithm (base_learner) per layer and 2) combining layer-wise cross-validated predictions using a meta model (meta_learner). A common default is BART as base learner (base_learner = "SL.BART") with SL.nnls.auc as the meta-learner.

For survival outcomes, IntegratedLearner dispatches to the survival engine (ILsurv) and supports mlr3 survival learners (for example, surv.coxph, surv.coxboost, surv.ranger) with configurable late-fusion weighting (COX/IBS) and optional intermediate fusion.

For non-survival tasks, learners should use the SL. prefix (for example, SL.randomForest, SL.BART, SL.glmnet). See the SuperLearner user manual for additional options.

# PCL mode (binary/continuous/survival)
IntegratedLearner(
  PCL_train = pcl_train,
  PCL_valid = pcl_valid,        # optional
  folds = 5,
  base_learner = "SL.randomForest",
  meta_learner = "SL.nnls.auc",
  family = binomial()
)

# MAE mode (binary/continuous/survival)
IntegratedLearner(
  MAE_train = mae_train,
  MAE_valid = mae_valid,        # optional
  experiment = c("taxonomy", "pathway"),
  assay.type = c("relative_abundance", "pathway_abundance"),
  folds = 5,
  base_learner = "surv.coxph",
  weight_method = "COX"
)

• MAE_train / MAE_valid: MultiAssayExperiment inputs for training and optional validation.
• PCL_train / PCL_valid: List inputs (feature_table, sample_metadata, feature_metadata) for training and optional validation.
• experiment: Selected MAE experiment names/indices (optional; defaults to all in MAE_train).
• assay.type: Assay name per selected MAE experiment.
• na.rm: Logical; drop features containing missing values after extraction/prep.
• folds: Integer. Number of folds for cross-validation. Default is 5.
• seed: Integer seed for reproducibility. Default is 1234.
• base_learner: Non-survival uses SL.* learners; survival uses supported surv.* learners.
• meta_learner: Meta learner for non-survival late fusion. Default is "SL.nnls.auc".
• run_concat: Logical; include early-fusion (concatenated) model for non-survival.
• run_stacked: Logical; include late-fusion stacked model for non-survival.
• family: Typically gaussian() or binomial() for non-survival. Survival is auto-detected from metadata or family.
• verbose: Logical progress flag.
• ...: Additional backend parameters. For survival, this includes options such as weight_method, do_early_fusion, and intermediate_learners.

Supported model families:

• Non-survival: any available SuperLearner SL.* model.
• Survival: surv.rfsrc, surv.ranger, surv.coxboost, surv.bart, surv.coxph, surv.glmnet.

Supported fusion modules:

• Non-survival: single-layer + early (run_concat) + late (run_stacked).
• Survival: single-layer + early (do_early_fusion) + late weighted fusion (COX/IBS) + intermediate (intermediate_learners).

For continuous/binary fits (IL_conbin path):

• SL_fits: Fitted SuperLearner objects (layer-wise, stacked, concatenated as applicable).
• model_fits: Extracted learner objects.
• X_train_layers, Y_train, yhat.train: training inputs and predictions.
• X_test_layers, Y_test, yhat.test: validation inputs and predictions (if validation provided).
• weights: Layer weights in stacked model (meta_learner = "SL.nnls.auc" and run_stacked = TRUE).
• AUC.train/AUC.test (binomial) or R2.train/R2.test (gaussian).
• feature_importance_signed: Global signed feature importance.
• feature_importance_signed_by_layer: Per-layer signed feature importance.

For survival fits (ILsurv path):

• train_out$single: Single-layer metrics.
• train_out$early: Early-fusion metrics (if enabled).
• train_out$late: Late-fusion metrics and learned layer weights (train_out$late$weights).
• train_out$intermediate: Intermediate learner metrics.
• valid_out$...: Validation analogs of single/early/late/intermediate outputs (if validation provided).
• train_out$late$combined_importance and (if available) train_out$early$combined_importance: survival feature-importance outputs.

If you use IntegratedLearner in your work, please cite the following:

Mallick H et al. (2024). An Integrated Bayesian Framework for Multi-omics Prediction and Classification. Statistics in Medicine 43(5):983–1002.

We are happy to troubleshoot any issues with the package. Please contact the maintainer via email or open an issue in the GitHub repository.

We are currently in the process of submitting IntegratedLearner to Bioconductor. Likewise, please keep an eye out for a future release of IntegratedLearner as an R/Bioconductor package while this repository remains the development version of the package.