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add attentiondta

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baselines/AttentionDTA_BIBM/.gitattributes 0 → 100644
# Auto detect text files and perform LF normalization
* text=auto
baselines/AttentionDTA_BIBM/DTA_model.py 0 → 100644
import tensorflow as tf
SMI_DIM = 65
PRO_DIM = 21
FILTERNUM = 32
SMI_FILTER_SIZE = [4,6,8]
PRO_FILTER_SIZE = [4,8,12]
EMBEDDING_DIM = 128
OUTPUT_NODE = 1
FC_SIZE = [1024, 1024, 512]
def variable_summaries(var, name):
with tf.name_scope("summaries"):
tf.summary.histogram(name, var)
mean = tf.reduce_mean(var)
tf.summary.scalar("mean/" + name, mean)
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar("stddev/" + name, stddev)
def inference(smi_tensor, pro_tensor, regularizer=None,
keep_prob=1, trainlabel=False):
with tf.variable_scope('smi_embedding', reuse=tf.AUTO_REUSE):
smi_wordembedding = tf.get_variable(
"smi_word_embedding", [SMI_DIM, EMBEDDING_DIM])
smi_embedding = tf.nn.embedding_lookup(smi_wordembedding, smi_tensor)
pro_wordembedding = tf.get_variable(
"pro_word_embedding", [PRO_DIM, EMBEDDING_DIM])
pro_embedding = tf.nn.embedding_lookup(pro_wordembedding, pro_tensor)
with tf.variable_scope('drug_conv'):
conv1_weights = tf.get_variable(
"weight1", [SMI_FILTER_SIZE[0], EMBEDDING_DIM, FILTERNUM],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv2_weights = tf.get_variable(
"weight2", [SMI_FILTER_SIZE[1], FILTERNUM, FILTERNUM * 2],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv3_weights = tf.get_variable(
"weight3", [SMI_FILTER_SIZE[2], FILTERNUM * 2, FILTERNUM * 3],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv1_biases = tf.get_variable(
"bias1", [FILTERNUM], initializer=tf.constant_initializer(0.1))
conv2_biases = tf.get_variable(
"bias2", [FILTERNUM * 2], initializer=tf.constant_initializer(0.1))
conv3_biases = tf.get_variable(
"bias3", [FILTERNUM * 3], initializer=tf.constant_initializer(0.1))
variable_summaries(conv1_weights, 'W1')
variable_summaries(conv2_weights, 'W2')
variable_summaries(conv3_weights, 'W3')
variable_summaries(conv1_biases, 'b1')
variable_summaries(conv2_biases, 'b2')
variable_summaries(conv3_biases, 'b3')
smi_conv1 = tf.nn.relu(
tf.nn.bias_add(
tf.nn.conv1d(
smi_embedding,
conv1_weights,
stride=1,
padding='VALID'),
conv1_biases))
smi_conv1 = tf.nn.relu(
tf.nn.bias_add(
tf.nn.conv1d(
smi_conv1,
conv2_weights,
stride=1,
padding='VALID'),
conv2_biases))
smi_conv1 = tf.nn.relu(
tf.nn.bias_add(
tf.nn.conv1d(
smi_conv1,
conv3_weights,
stride=1,
padding='VALID'),
conv3_biases))
# drug_feature = tf.squeeze(
# tf.nn.pool(
# smi_conv1,
# window_shape=[85],
# pooling_type="MAX",
# padding='VALID'),
# 1)
with tf.variable_scope('protein_conv'):
conv1_weights = tf.get_variable(
"weight", [PRO_FILTER_SIZE[0], EMBEDDING_DIM, FILTERNUM],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv2_weights = tf.get_variable(
"weight1", [PRO_FILTER_SIZE[1], FILTERNUM, FILTERNUM * 2],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv3_weights = tf.get_variable(
"weight2", [PRO_FILTER_SIZE[2], FILTERNUM * 2, FILTERNUM * 3],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv1_biases = tf.get_variable("bias", [FILTERNUM], initializer=tf.constant_initializer(0.1))
conv2_biases = tf.get_variable("bias1", [FILTERNUM * 2], initializer=tf.constant_initializer(0.1))
conv3_biases = tf.get_variable("bias2", [FILTERNUM * 3], initializer=tf.constant_initializer(0.1))
pro_conv1 = tf.nn.relu(
tf.nn.bias_add(tf.nn.conv1d(pro_embedding, conv1_weights, stride=1, padding='SAME'), conv1_biases))
pro_conv1 = tf.nn.relu(
tf.nn.bias_add(tf.nn.conv1d(pro_conv1, conv2_weights, stride=1, padding='SAME'), conv2_biases))
pro_conv1 = tf.nn.relu(
tf.nn.bias_add(tf.nn.conv1d(pro_conv1, conv3_weights, stride=1, padding='SAME'), conv3_biases))
# pro_pool = tf.nn.pool(pro_conv3, window_shape=[1179], pooling_type="MAX", padding='VALID')
if regularizer is not None:
tf.add_to_collection('losses', regularizer(conv1_weights))
tf.add_to_collection('losses', regularizer(conv2_weights))
tf.add_to_collection('losses', regularizer(conv3_weights))
variable_summaries(conv1_weights, 'W1')
variable_summaries(conv2_weights, 'W2')
variable_summaries(conv3_weights, 'W3')
variable_summaries(conv1_biases, 'b1')
variable_summaries(conv2_biases, 'b2')
variable_summaries(conv3_biases, 'b3')
with tf.variable_scope("attention_layer", reuse=tf.AUTO_REUSE):
weights = tf.get_variable("weight", [smi_conv1.get_shape()[2], pro_conv1.get_shape()[2]],
initializer=tf.truncated_normal_initializer(stddev=0.1))
if regularizer is not None:
tf.add_to_collection('losses', regularizer(weights))
biases = tf.get_variable("bias",
[pro_conv1.get_shape()[2]],
initializer=tf.constant_initializer(0.1))
atten1 = tf.nn.relu(tf.einsum('ajk,ki->aji', smi_conv1, weights))+biases
atten2 = tf.nn.relu(tf.einsum('ajk,ki->aji', pro_conv1, weights))+biases
variable_summaries(weights, "DT_att_weight")
variable_summaries(biases, "DT_att_biases")
alph = tf.nn.tanh(
tf.einsum('aji,aik->ajk', atten1, tf.transpose(atten2, [0, 2, 1])))
alphdrug = tf.nn.tanh(tf.reduce_sum(alph, 2))
alphprotein = tf.nn.tanh(tf.reduce_sum(alph, 1))
alphdrug = tf.tile(tf.expand_dims(alphdrug, 2), [1, 1, smi_conv1.get_shape()[2]])
alphprotein = tf.tile(tf.expand_dims(alphprotein, 2), [1, 1, pro_conv1.get_shape()[2]])
drug_feature = tf.multiply(alphdrug, smi_conv1)
pretein_feature = tf.multiply(alphprotein, pro_conv1)
drug_feature = tf.squeeze(
tf.nn.pool(drug_feature, window_shape=[drug_feature.get_shape()[1]], pooling_type="MAX",
padding='VALID'), 1)
pretein_feature = tf.squeeze(tf.nn.pool(pretein_feature, window_shape=[pretein_feature.get_shape()[1]], pooling_type="MAX", padding='VALID'),1)
with tf.name_scope("concat_layer"):
pair_feature = tf.concat([drug_feature, pretein_feature], 1)
with tf.variable_scope('deep-fc-layer', reuse=tf.AUTO_REUSE):
fc1_weights = tf.get_variable("weight1", [int(pair_feature.get_shape()[1]), FC_SIZE[0]],
initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float32)
fc1_biases = tf.get_variable("bias1",
[FC_SIZE[0]],
initializer=tf.constant_initializer(0.1),
dtype=tf.float32)
fc2_weights = tf.get_variable("weight2", [FC_SIZE[0], FC_SIZE[1]],
initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float32)
fc2_biases = tf.get_variable("bias2",
[FC_SIZE[1]],
initializer=tf.constant_initializer(0.1),
dtype=tf.float32)
fc3_weights = tf.get_variable("weight3", [FC_SIZE[1], FC_SIZE[2]],
initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float32)
fc3_biases = tf.get_variable("bias3",
[FC_SIZE[2]],
initializer=tf.constant_initializer(0.1),
dtype=tf.float32)
if regularizer is not None:
tf.add_to_collection('losses', regularizer(fc1_weights))
tf.add_to_collection('losses', regularizer(fc2_weights))
tf.add_to_collection('losses', regularizer(fc3_weights))
variable_summaries(fc1_weights, 'W1')
variable_summaries(fc2_weights, 'W2')
variable_summaries(fc3_weights, 'W3')
variable_summaries(fc1_biases, 'b1')
variable_summaries(fc2_biases, 'b2')
variable_summaries(fc3_biases, 'b3')
fc = tf.nn.leaky_relu(
tf.matmul(
tf.cast(
pair_feature,
tf.float32),
fc1_weights) +
fc1_biases)
variable_summaries(fc, 'fc1')
# fc = tf.layers.batch_normalization(fc, training=trainlabel)
fc = tf.nn.dropout(fc, keep_prob)
fc = tf.nn.leaky_relu(
tf.matmul(fc, fc2_weights) + fc2_biases)
variable_summaries(fc, 'fc2')
# fc = tf.layers.batch_normalization(fc, training=trainlabel)
fc = tf.nn.dropout(fc, keep_prob)
fc = tf.nn.leaky_relu(
tf.matmul(fc, fc3_weights) + fc3_biases)
variable_summaries(fc, 'fc3')
# fc = tf.layers.batch_normalization(fc, training=trainlabel)
with tf.variable_scope('y-layer', reuse=tf.AUTO_REUSE):
y_weights = tf.get_variable("weight", [FC_SIZE[2], 1],
initializer=tf.truncated_normal_initializer(stddev=0.1), dtype=tf.float32)
y_biases = tf.get_variable(
"bias",
1,
initializer=tf.constant_initializer(5),
dtype=tf.float32)
variable_summaries(y_weights, 'W')
variable_summaries(y_biases, 'b')
if regularizer is not None:
tf.add_to_collection('losses', regularizer(y_weights))
logit = tf.matmul(fc, y_weights) + y_biases
return drug_feature, pretein_feature, logit
baselines/AttentionDTA_BIBM/DTA_test.py 0 → 100644
import tensorflow as tf
import pandas as pd
import numpy as np
import DTA_model as model
import os
MAX_SEQ_LEN = 1200
MAX_SMI_LEN = 100
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score # R square
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
def calculateMSE(X, Y):
in_bracket = []
for i in range(len(X)):
num = Y[i] - X[i]
num = pow(num, 2)
in_bracket.append(num)
all_sum = sum(in_bracket)
MSE = all_sum / len(X)
return MSE
def parser(record):
read_features = {
'drug': tf.FixedLenFeature([MAX_SMI_LEN], dtype=tf.int64),
'protein': tf.FixedLenFeature([MAX_SEQ_LEN], dtype=tf.int64),
'affinity': tf.FixedLenFeature([1], dtype=tf.float32)
}
read_data = tf.parse_single_example(
serialized=record, features=read_features)
# read_data = tf.parse_example(serialized=record, features=read_features)
drug = tf.cast(read_data['drug'], tf.int32)
protein = tf.cast(read_data['protein'], tf.int32)
affinit_y = read_data['affinity']
return drug, protein, affinit_y
def test(file, test_path):
with tf.Graph().as_default() as g:
dataset = tf.data.TFRecordDataset(test_path)
dataset = dataset.map(parser)
dataset = dataset.batch(
batch_size=3)
iterator = dataset.make_initializable_iterator()
drug_to_embeding, proteins_to_embeding, labels_batch \
= iterator.get_next()
_, _, test_label = \
model.inference(
drug_to_embeding,
proteins_to_embeding,
regularizer=None,
keep_prob=1,
trainlabel=0)
mean_squared_eror = tf.losses.mean_squared_error(
test_label, labels_batch)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(iterator.initializer)
ckpt = tf.train.get_checkpoint_state(
"./" + "train" + "/model0/")
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
predictions_eval = []
labels_eval = []
# MSElist = []
try:
while True:
df, pf, p, l, MSE = sess.run(
[drug_to_embeding, proteins_to_embeding, test_label, labels_batch, mean_squared_eror])
predictions_eval.append(p)
labels_eval.append(l)
except tf.errors.OutOfRangeError:
pass
predictions_eval = np.concatenate(predictions_eval)
labels_eval = np.concatenate(labels_eval)
labels_eval.resize([labels_eval.shape[0], 1])
RESULT_PATH = "./results/" + dataname + "/"
if os.path.exists(RESULT_PATH) is False:
os.makedirs(RESULT_PATH)
with open(RESULT_PATH + "test.txt", "w") as f:
for i in predictions_eval:
f.write(str(i[0]) + '\n')
# test_MSE = mean_squared_error(labels_eval, predictions_eval)
# test_MAE = mean_absolute_error(labels_eval, predictions_eval)
# test_R2 = r2_score(labels_eval, predictions_eval)
# print("MSE:", test_MSE, "MAE:", test_MAE, "R2:", test_R2)
print("----------------test over-----------------")
# return test_MSE,test_MAE,test_R2
if __name__ == '__main__':
# dataname = "davis"
# test
dataname = "test"
test_path = "./tfrecord/" + dataname + "/file.tfrecord"
test(dataname, test_path)
# kinase
dataname = "kinase"
test_path = "./tfrecord/" + dataname + "/file.tfrecord"
test(dataname, test_path)
# kinase
dataname = "GPCR"
test_path = "./tfrecord/" + dataname + "/file.tfrecord"
test(dataname, test_path)
# kinase
dataname = "ER"
test_path = "./tfrecord/" + dataname + "/file.tfrecord"
test(dataname, test_path)
# kinase
dataname = "channel"
test_path = "./tfrecord/" + dataname + "/file.tfrecord"
test(dataname, test_path)
baselines/AttentionDTA_BIBM/DTA_train.py 0 → 100644
import tensorflow as tf
import numpy as np
import DTA_model as model
# from tensorflow.python.client import timeline
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "5"
# dataname = "davis"
# dataname = "deepaffinity"
# 5-fold cross-validation
cross_num = 1
LEARNING_RATE_BASE = 0.0001
# REGULARIZATION_RATE = 0.00001
EPOCH = 75
#
# if dataname == "kiba":
# batch_size = 100
# TESTNUM = (118256/5)*4/100
dataname = "train"
batch_size = 64
TESTNUM = (30056/5)*4/100
MAX_SEQ_LEN = 1200
MAX_SMI_LEN = 100
Train_path = "./tfrecord/" + dataname + "/file.tfrecord"
MODEL_SAVE_PATH = "./" + dataname + "/model%d/"
MODEL_NAME = "model.ckpt"
def parser(record):
read_features = {
'drug': tf.FixedLenFeature([MAX_SMI_LEN], dtype=tf.int64),
'protein': tf.FixedLenFeature([MAX_SEQ_LEN], dtype=tf.int64),
'affinity': tf.FixedLenFeature([1], dtype=tf.float32)
}
read_data = tf.parse_single_example(
serialized=record, features=read_features)
drug = tf.cast(read_data['drug'], tf.int32)
protein = tf.cast(read_data['protein'], tf.int32)
affinit_y = read_data['affinity']
return drug, protein, affinit_y
def train(num, train_path):
with tf.variable_scope("input"):
dataset = tf.data.TFRecordDataset(train_path)
dataset = dataset.map(parser)
dataset = dataset.repeat(EPOCH).shuffle(500).batch(
batch_size=batch_size)
train_iterator = dataset.make_initializable_iterator()
train_drug, train_proteins_to_embeding, train_labels_batch\
= train_iterator.get_next()
# regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
_, _, train_label = \
model.inference(
train_drug,
train_proteins_to_embeding,
regularizer=None, keep_prob=0.9, trainlabel=1
)
global_step = tf.Variable(0, trainable=False)
with tf.name_scope("train_loss_function"):
mean_squared_eror = tf.losses.mean_squared_error(
train_label, train_labels_batch)
tf.summary.scalar("mean_squared_eror", mean_squared_eror)
# loss = mean_squared_eror + tf.add_n(tf.get_collection("losses"))
# tf.summary.scalar("loss", loss)
with tf.name_scope("train_step"):
learning_rate = LEARNING_RATE_BASE
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
# train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=global_step)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(
mean_squared_eror, global_step=global_step)
with tf.control_dependencies([train_step]):
train_op = tf.no_op(name='train')
merged = tf.summary.merge_all()
summary_write = tf.summary.FileWriter(
"./" + dataname + "/path/to/log%d" %
num, tf.get_default_graph())
var_list = [var for var in tf.global_variables() if "moving" in var.name]
var_list += tf.trainable_variables()
saver = tf.train.Saver(var_list=var_list, max_to_keep=20)
config = tf.ConfigProto(
log_device_placement=False,
allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess,\
open("./" + dataname + "/path/to/log%d/log.txt" % num, "w") as f:
print("beginning training")
sess.run(
tf.group(
tf.global_variables_initializer(),
tf.local_variables_initializer()))
sess.run(train_iterator.initializer)
step = 0
maxloss = 100
trainMSElist = []
try:
while True:
step += 1
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
#train and test
# trainLosslist = []
# Loss, summary, _, MSE, now_step = sess.run(
# [loss, merged, train_op, mean_squared_eror, global_step],
# options=run_options, run_metadata=run_metadata)
summary, _, MSE, now_step = sess.run(
[merged, train_op, mean_squared_eror, global_step],
options=run_options, run_metadata=run_metadata)
str = "%s-model:%d-step:%d;train_MSE:%g;" % (
dataname, num, now_step, MSE)
f.write(str + "\n")
trainMSElist.append(MSE)
# trainLosslist.append(Loss)
if step % 10 == 0:
# if step % TESTNUM == 0:
summary_write.add_summary(summary, now_step)
summary_write.add_run_metadata(
run_metadata, tag=(
"step%d" %
step), global_step=step)
trainMSE = 0
# trainLoss = 0
for i in range(len(trainMSElist)):
# trainLoss += trainLosslist[i]
trainMSE += trainMSElist[i]
# trainLoss /= len(trainLosslist)
trainMSE /= len(trainMSElist)
# print(
# "%s-model:%d-step:%d;train_Loss:%g;train_MSE:%g." %
# (dataname, num, now_step, trainLoss, trainMSE))
print(
"%s-model:%d-epoch:%d;train_MSE:%g;" %
(dataname, num, now_step, trainMSE))
trainMSElist = []
if trainMSE < maxloss:
saver.save(
sess,
os.path.join(
MODEL_SAVE_PATH %
num,
MODEL_NAME),
global_step=global_step)
maxloss = trainMSE
print("save model")
else:
pass
except tf.errors.OutOfRangeError:
pass
summary_write.close()
def main(argv=None):
for i in range(cross_num):
tf.reset_default_graph()
if os.path.exists(MODEL_SAVE_PATH % i) is False:
os.makedirs(MODEL_SAVE_PATH % i)
print("The No.%d model" % i)
# train_path = Train_path
train(i, Train_path)
if __name__ == '__main__':
tf.app.run()
baselines/AttentionDTA_BIBM/README.md 0 → 100644
# AttentionDTA_BIBM
AttentionDTA: prediction of drug–target binding affinity using attention model.https://ieeexplore.ieee.org/abstract/document/8983125
This repository contains the source code and the data.
## AttentionDTA
<div align="center">
<p><img src="model.jpg" width="600" /></p>
</div>
## Setup and dependencies
Dependencies:
- python 3.6
- tensorflow >=1.9
- numpy
## Resources:
+ README.md: this file.
+ tfrecord: The original data set and data set processing code are saved in this folder.
+ davis_div.txt: Under the 5-fold cross-validation setting, there is a division of the training set and the test set of the davis dataset.
+ kiba_div.txt: Under the 5-fold cross-validation setting, there is a division of the training set and the test set of the kiba dataset.
+ davis_str_all.txt
+ kiba_str_all.txt
+ dataset.py: create data in tfrecord format according to (kiba/davis)_div.txt
+ DTA_train.py: train a AttentionDTA model.
+ DTA_model.py: AttentionDTA model architecture
+ DTA_test.py: test trained models
# Step-by-step running:
## 1. Create data in tfrecord format
python dataset.py
## 2. Train a prediction model
python DTA_train.py
To train a model using training data.
## 3. Predict affinity with trained models
python DTA_test.py
\ No newline at end of file
baselines/AttentionDTA_BIBM/results/ER/eval_results 0 → 100644
RMSE : 1.977229018807965 ; Pearson Correlation Coefficient : 0.13056623322435212
\ No newline at end of file
baselines/AttentionDTA_BIBM/results/ER/test.txt 0 → 100644
5.357305
5.0675197
5.7569113
5.778124
5.6330957
5.7011733
5.74707
5.745891
5.7677283
5.6298275
5.834708
5.626807
5.747061
5.7795863
5.5337734
5.0578804
5.044533
5.116545
5.116936
5.1977515
5.1571093
5.175327
5.306932
5.1647
5.260111
5.3910584
4.749715
5.062382
5.22511
4.8893523
4.896854
4.9255004
5.0114884
4.9694557
4.945339
4.958314
4.979654
5.110323
4.939724
4.9064345
5.7481256
5.2178473
5.2963023
6.1322412
5.7981763
5.466777
5.4514165
4.841126
4.903579
5.809992
5.1097155
4.9852066
5.2520943
5.0755916
4.950823
4.86141
4.8641877
5.0124044
4.8961926
4.9459543
4.91149
5.053131
6.021241
4.8511534
4.8457537
4.7418513
5.0014133
5.0835266
5.032491
6.302972
6.518325
6.3595266
4.914291
5.0036993
5.3551555
5.2690964
4.8253613
5.093188
4.830887
5.290556
5.946444
5.7190695
5.0897026
5.1990857
4.929069
4.704165
4.772109
5.496005
5.521178
5.0173416
5.2495856
4.4665365
4.7818794
4.8992696
4.7708325
4.8471565
4.823306
4.91406
4.8816433
5.3732452
4.527399
4.5776706
4.446348
5.251399
4.636929
5.6276937
4.975101
5.0767593
5.735553
5.126744
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baselines/AttentionDTA_BIBM/results/GPCR/eval_results 0 → 100644
RMSE : 1.8548911640057175 ; Pearson Correlation Coefficient : 0.19903442590966353
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baselines/AttentionDTA_BIBM/results/GPCR/test.txt 0 → 100644
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baselines/AttentionDTA_BIBM/results/channel/eval_results 0 → 100644
RMSE : 1.7273183993658947 ; Pearson Correlation Coefficient : 0.04453206966512105
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baselines/AttentionDTA_BIBM/results/channel/test.txt 0 → 100644
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baselines/AttentionDTA_BIBM/results/kinase/eval_results 0 → 100644
RMSE : 1.756065730513885 ; Pearson Correlation Coefficient : 0.23311044821975194
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baselines/AttentionDTA_BIBM/results/kinase/test.txt 0 → 100644
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baselines/AttentionDTA_BIBM/results/test/eval_results 0 → 100644
RMSE : 1.1846278906245926 ; Pearson Correlation Coefficient : 0.6965380003561024
\ No newline at end of file
baselines/AttentionDTA_BIBM/results/test/test.txt 0 → 100644
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