clipnet-fold_1

Model Card for CLIPNET

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Model Details

Model Description

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CLIPNET (Convolutionally Learned, Initiation-Predicting NETwork) is an ensembled convolutional neural network that predicts transcription initiation from DNA sequence at single nucleotide resolution.

• Developed by: Adam Youlin He
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• License: mit
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Model Sources [optional]

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• Repository: https://github.com/Danko-Lab/clipnet
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Uses

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Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Hardware

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Software

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Citation [optional]

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Glossary [optional]

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