深度OC

DeepOC 是BioModel Classifier的核心- 使用深度神经网络自动分类生物模型的 python 应用程序。DeepOC 为基于本体的模型分类提供了一些非常底层的功能，这使我们能够适应任何其他项目。

安装

pip install deepoc

用法

首先，您需要一个地面实况数据集，它是模型的字典及其对应本体的列表

{
    "model_1": ["GO:00001", "GO:00003", "GO:00002"],
    "model_2: ["GO:00004", "GO:00002"]
}

要生成数据集并训练 DNN 模型：

ground_truth = ...
train_file = "path/to/your train csv file"
test_file = "path/to/your test csv file"
val_file = "path/to/your val csv file"
features = deepoc.build_features(ground_truth)

# Picking the first 300 features  
selected_features = [feature['feature'] for idx, feature in enumerate(features) if idx < 300]

train, test, val = deepoc.generate_dataset(ground_truth, features, classes)

# Writing dataset to file  
deepoc.write_dataset_to_file(train, train_file)  
deepoc.write_dataset_to_file(test, test_file)  
deepoc.write_dataset_to_file(val, val_file)

# Configure DNN model to use Gradient Descent optimizer, 1 hidden layer with 150 nodes, learning rate of 0.001 and dropout rate of 0.5
classifier = DeepOCClassifier(workspace, 'GD', [150], 0.001, train_file, test_file, classes, 0.5)
# Train the model with 3000 epoch, validate every 10 epochs and batch size of 16
classifier.train_dll_model(3000, 10, 16)

# Validate the result:
for record in val:
    model = record['model']
    predict_result = classifier.predict(record)  
    logger.info('Model %s: %s', model, predict_result)

更多示例可以在测试文件夹中找到。

基于除基因本体之外的任何本体对模型进行分类

要使该库与其他类型的本体一起使用，请根据您的本体实现OntologyService并在https://bitbucket.org/biomodels/deepoc/src/master/deepoc/ontology/init .py实例化您的对象

开发者

• 武图

接触

许可

生物模型分类器源代码在 GNU Affero 通用公共许可证下分发。
请阅读 license.txt 以获取有关软件可用性和分发的信息。