Machine Learning Analysis Services — Leakage-Aware Model Development with DOME- and TRIPOD+AI-Aligned Reporting

Key facts

Key terms: Machine learning (ML) learns patterns from data without hand-coded rules. Supervised learning trains on labeled outcomes (e.g., responder vs non-responder). Cross-validation repeatedly splits data to estimate generalization. Data leakage shares information between training and test sets, inflating performance (Bernett et al., 2024). AUROC (area under the receiver operating characteristic curve) measures classifier discrimination. C-index (concordance index) measures survival-model discrimination. A foundation model is pretrained on large datasets and fine-tuned for downstream tasks. A transformer is an attention-based neural architecture used in models such as scGPT (Cui et al., 2024).

What is machine learning?

Machine learning learns predictive or clustering patterns from high-dimensional biological data—often when features (genes, proteins, variants) outnumber samples. Supervised algorithms adjust parameters from labeled outcomes; unsupervised methods find structure without labels. The core question is: which features or combinations predict outcome, treatment response, or patient subgroup? Adoption is accelerating: scGPT was pretrained on more than 33 million cells and fine-tuned for annotation, integration, and perturbation prediction (Cui et al., 2024). A 2023 systematic review screened 682 PubMed articles (2017–2023) and critically reviewed 30 cancer biomarker ML studies, highlighting reporting heterogeneity and data-leakage risks (Al-Tashi et al., 2023).

What machine learning analysis can answer

Published examples of biological questions machine learning can address:

• Which gene signature predicts hepatocellular carcinoma overall survival? Feng et al. (2023) combined scRNA-seq and bulk RNA-seq to derive an 11-gene NK-cell-related signature from 77 ML algorithms, validated across TCGA, GEO, and ICGC cohorts with concordance-index benchmarking.
• Which transcriptomic features classify breast cancer for diagnosis and prognosis? Mirza et al. (2023) applied seven ML methods to 701 samples from 11 GEO datasets, identifying a nine-gene diagnostic signature and an eight-gene prognostic signature validated by qRT-PCR.
• Which T cell states precede anti-PD-1 response in NSCLC? Liu et al. (2022) profiled 47 biopsies from 36 patients before and after PD-1 therapy, linking precursor exhausted T cell expansion to response—informing candidate features for immunotherapy stratification models.
• How do genetic or chemical perturbations shift cell-state programs? Cui et al. (2024) showed scGPT—a generative pretrained transformer on single-cell data—predicts perturbation responses after fine-tuning, enabling transfer learning when labeled perturbation data are limited.

Services included in this category

Pepkio's machine learning category covers biomarker discovery and predictive modeling—each with a dedicated spoke page for inputs, validation design, tools, and deliverables.

What Pepkio delivers

Every project returns validated model outputs, interpretability artifacts, and reproducible code—not slide summaries alone.

Non-standard outcomes, multi-omics fusion, or foundation-model fine-tuning are scoped at kickoff.

How the analysis works — step by step

1. 1. Scope outcome, cohort, and validation plan

   Confirm the prediction target, inclusion criteria, endpoint type (binary, multiclass, survival), and whether an independent external cohort exists (Walsh et al., 2021; Collins et al., 2024).

   Tools and outputs

   Output: signed scope with primary and secondary metrics

2. 2. Validate inputs and harmonize metadata

   Match sample IDs; audit missingness, duplicate patients, and batch structure.

   Tools and outputs

   Output: sample_manifest.csv and feature_qc_summary.csv

3. 3. Preprocess and normalize features

   Apply transforms and scaling; use batch correction only when batch is not confounded with the outcome (Whalen et al., 2022).

   Tools and outputs

   Tools used: scikit-learn ColumnTransformer; ComBat or limma when scoped

   Output: analysis-ready feature matrix

4. 4. Audit for data leakage

   Apply the seven guiding questions from Bernett et al. (2024) before splitting.

   Tools and outputs

   Output: leakage_audit.md

5. 5. Partition data with nested cross-validation

   Use patient-level nested cross-validation to separate tuning from performance estimation (Walsh et al., 2021).

   Tools and outputs

   Tools used: scikit-learn CV splitters; scikit-survival for censored outcomes

   Output: fold assignment table

6. 6. Select features and train candidate models

   Compare regularized linear models, tree ensembles, and survival forests with simpler baselines (Greener et al., 2022). Fine-tune scGPT when single-cell transfer learning is scoped (Cui et al., 2024).

   Tools and outputs

   Output: candidate model list

7. 7. Evaluate performance and calibration

   Report discrimination and calibration on held-out or external data; avoid claiming clinical utility without independent validation (Whalen et al., 2022).

   Tools and outputs

   Output: model_performance_summary.csv

8. 8. Interpret features and biological context

   Annotate selected features with pathways and cell types; document where interpretability limits causal claims (Chen et al., 2024).

   Tools and outputs

   Output: enrichment tables and interpretation memo

9. 9. Package deliverables and draft Methods

   Assemble figures, models, DOME table, scripts, environment locks, README, and Methods draft.

   Tools and outputs

   Output: final deliverable bundle

Tools and standards we use

Pepkio pins software versions at kickoff and cites primary references in the Methods draft. Representative tools across biomarker and prediction projects:

Validation reporting follows DOME (Walsh et al., 2021) and TRIPOD+AI when clinical prediction reporting is scoped (Collins et al., 2024). Feature annotation uses GENCODE v44 (human) unless a project requires a custom reference. Studies with p >> n typically require regularization and nested cross-validation rather than single train/test splits (Whalen et al., 2022).

Common challenges — and how we handle them

Researchers often struggle with data leakage, overfitting, incomplete reporting, unstable feature selection, and opaque model outputs. Pepkio addresses each with documented validation design and interpretability deliverables.

Data leakage inflates reported performance

Illicit information sharing between training and test sets is common in high-dimensional biological data and can make models fail in real application (Bernett et al., 2024). Pepkio completes a leakage audit before splitting and uses patient-level partitions throughout.

p >> n overfitting with small cohorts

When features outnumber samples, complex models memorize noise (Whalen et al., 2022). Pepkio applies regularization, nested cross-validation, and reports simpler baselines alongside complex models so reviewers can assess incremental value.

Incomplete ML reporting

A 2023 systematic review of 30 cancer biomarker ML studies noted data leakage and inconsistent evaluation practices among common limitations (Al-Tashi et al., 2023). Pepkio includes a DOME summary table in every deliverable and a TRIPOD+AI checklist when clinical prediction reporting is scoped.

Unstable feature selection across resamples

Features selected in one split may not replicate in another, weakening biomarker claims. Pepkio reports selection frequency across cross-validation folds and prioritizes stable signatures over single-split lists.

Black-box models without interpretability

Deep models can discriminate well but obscure biological mechanism (Chen et al., 2024). Pepkio delivers SHAP plots and coefficient tables and documents where interpretability does not support causal inference.

Common questions

Related services

• Transcriptomics — Generate expression feature matrices and differential-expression contrasts as ML inputs.
• Proteomics — Protein abundance features and orthogonal validation of transcript-level predictors.
• Genomics — Variant, CNV, and structural features for germline or somatic prediction models.
• Statistical analysis — Experimental design, power estimation, and covariate planning before committing to ML.
• Bioinformatics consulting — Feasibility assessment, cohort-size guidance, and validation planning before model development.