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Challenge

Pioneer Factor Cohesin Chromatin Binding Predictor with PyTorch

Pioneer transcription factors direct cohesin loop extrusion across genomic chromatin boundaries. You will build a deep learning neural network in Python using PyTorch to predict cohesin binding sites from pioneer factor ChIP-seq motifs, achieving an AUROC of 0.91.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Train a PyTorch convolutional model to predict pioneer-factor-guided cohesin chromatin binding from genomic sequence motifs.

Delivery guide

How work is evaluated

Evaluation

Evaluates cohesin binding prediction AUROC and model loss on unseen genomic test chromosomes.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

probability_range_check

Ensure output is a valid probability float between 0 and 1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

auroc

Area under the receiver operating characteristic curve on chromosome 21 test set • target: 0.91 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Build 1D CNN residual neural architectures in PyTorch for genomic sequence processing

  • Process ChIP-seq peak signals and pioneer factor binding motifs into tensor representations

  • Predict chromatin loop anchor sites and cohesin deposition probabilities

Resources and assets

Reference links and supporting material

Dataset notes

ChIP-seq genomic dataset covering 50,000 pioneer factor and cohesin binding region windows across human cell lines.

Challenge setup

Evaluates cohesin binding prediction AUROC and model loss on unseen genomic test chromosomes.

Challenge input

JSON object with dna_sequence string (1000bp)

PyTorch

Mandatory deep learning neural network framework.

Evaluated output

JSON object with binding_probability float

Evaluation checks
  • Ensure output is a valid probability float between 0 and 1
  • Area under the receiver operating characteristic curve on chromosome 21 test set • target: 0.91 • range: 0-1
Configured evaluation evidence
  • Benchmark: GenoDeep-Bench
  • Auroc target: 0.91
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Llama 3.2 · Required
  • Mindgard · Optional
  • LocalAI · Optional

Evaluation contract

  • probability_range_check · Weight 1
  • auroc · Weight 1

Metrics

  • auroc · Target 0.91

Benchmarks

  • GenoDeep-Bench
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with dna_sequence string (1000bp)

    Output: JSON object with binding_probability float

    Execution harness: Python

Evaluation tools

  • PyTorch · Deep Learning Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Pioneer Factor Cohesin Chromatin Binding Predictor with PyTorch