Technical interview guide

Machine Learning in Computational Biology

Where ML is actually applied in genomics and drug discovery, and the specific data challenges — class imbalance, batch effects, limited labels — that make biological ML harder than typical tabular or image tasks.

Read: 50 min
Practice MCQs: 25
Interview QA: 25
Edition: v4
Editorial status: Reviewed
Relevant for: Computational Biologist

Scope: scikit-learn 1.9 cross-validation, pipeline, evaluation and calibration guidance; DeepVariant, AlphaFold and Enformer source work; TRIPOD+AI, PROBAST+AI, Model Cards and Datasheets guidance reviewed 2026-09-04.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: 2026-10-05 · Written: 2026-10-05 · Reviewed: 2026-10-04

QA-1

Design and defend a biological ML workflow for biological independence.

QA-2

Design and defend a biological ML workflow for prediction target.

QA-3

Design and defend a biological ML workflow for homology leakage.

QA-4

Design and defend a biological ML workflow for genomic window leakage.

QA-5

Design and defend a biological ML workflow for nested model selection.

QA-6

How do you evaluate and prevent batch effects when training representation learning models on single-cell RNA-seq (scRNA-seq) or multi-omics data?

QA-7

How do you handle noisy labels, assay variance, and discordant replicate measurements when training models on high-throughput screening (HTS) or functional genomics data?

QA-8

How do you detect and mitigate technical batch effects and plate artifacts in transcriptomic or imaging phenotypic screens without stripping true biological signal?

QA-9

In extreme class imbalance settings like virtual screening hit identification (e.g., 1 active per 10,000 inactives), how do you choose loss functions and ranking metrics to avoid deceptive evaluation?

QA-10

Design and defend a biological ML workflow for class imbalance.

QA-11

Design and defend a biological ML workflow for ROC and precision-recall.

QA-12

Design and defend a biological ML workflow for threshold selection.

QA-13

Design and defend a biological ML workflow for probability calibration.

QA-14

Design and defend a biological ML workflow for calibration shift.

QA-15

Design and defend a biological ML workflow for missing data.

QA-16

Design and defend a biological ML workflow for uncertainty.

QA-17

Design and defend a biological ML workflow for feature attribution.

QA-18

Design and defend a biological ML workflow for sequence models.

QA-19

Design and defend a biological ML workflow for genomics models.

QA-20

Design and defend a biological ML workflow for external validation.

QA-21

Design and defend a biological ML workflow for subgroup evaluation.

QA-22

Design and defend a biological ML workflow for data documentation.

QA-23

Design and defend a biological ML workflow for model documentation.

QA-24

Design and defend a biological ML workflow for deployment monitoring.

QA-25