This prediction tool was built on the basis of known promoter methylation of human small cell lung cancer cell coefficients. The primary goal was to predict all promoter 5mC sites in human small cell lung cancer cell lines.

This predictor program runs on windows10, pycharm and python3.7.4 when it is built. If you want to run in linux environment, please change the address part of the program to your required location.

absl-py==0.8.1
alabaster==0.7.12
anaconda-client==1.7.2
anaconda-navigator==1.9.7
anaconda-project==0.8.3
asn1crypto==1.0.1
astor==0.8.1
astroid==2.3.1
astropy==3.2.1
atomicwrites==1.3.0
attrs==19.2.0
Babel==2.7.0
backcall==0.1.0
backports.functools-lru-cache==1.6.1
backports.os==0.1.1
backports.shutil-get-terminal-size==1.0.0
backports.tempfile==1.0
backports.weakref==1.0.post1
beautifulsoup4==4.8.0
biopython==1.74
bitarray==1.0.1
bkcharts==0.2
bleach==3.1.0
bokeh==1.3.4
boto==2.49.0
Bottleneck==1.2.1
cachetools==3.1.1
certifi==2019.9.11
cffi==1.12.3
chardet==3.0.4
Click==7.0
cloudpickle==1.2.2
clyent==1.2.2
colorama==0.4.1
comtypes==1.1.7
conda==4.8.0
conda-build==3.18.9
conda-package-handling==1.6.0
conda-verify==3.4.2
contextlib2==0.6.0
cryptography==2.7
cycler==0.10.0
Cython==0.29.13
cytoolz==0.10.0
dask==2.5.2
decorator==4.4.0
defusedxml==0.6.0
distributed==2.5.2
docutils==0.15.2
entrypoints==0.3
et-xmlfile==1.0.1
fastcache==1.1.0
filelock==3.0.12
Flask==1.1.1
fsspec==0.5.2
future==0.18.2
gast==0.2.2
gevent==1.4.0
glob2==0.7
google-auth==1.8.2
google-auth-oauthlib==0.4.1
google-pasta==0.1.8
greenlet==0.4.15
grpcio==1.25.0
h5py==2.9.0
HeapDict==1.0.1
html5lib==1.0.1
idna==2.8
imageio==2.6.0
imagesize==1.1.0
importlib-metadata==0.23
ipykernel==5.1.2
ipython==7.8.0
ipython-genutils==0.2.0
ipywidgets==7.5.1
isort==4.3.21
itsdangerous==1.1.0
jdcal==1.4.1
jedi==0.15.1
Jinja2==2.10.3
joblib==0.13.2
json5==0.8.5
jsonschema==3.0.2
jupyter==1.0.0
jupyter-client==5.3.3
jupyter-console==6.0.0
jupyter-core==4.5.0
jupyterlab==1.1.4
jupyterlab-server==1.0.6
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
keyring==18.0.0
kiwisolver==1.1.0
lazy-object-proxy==1.4.2
libarchive-c==2.8
llvmlite==0.29.0
locket==0.2.0
lxml==4.4.1
Markdown==3.1.1
MarkupSafe==1.1.1
matplotlib==3.1.1
mccabe==0.6.1
menuinst==1.4.16
mistune==0.8.4
mkl-fft==1.0.14
mkl-random==1.1.0
mkl-service==2.3.0
mock==3.0.5
more-itertools==7.2.0
mpmath==1.1.0
msgpack==0.6.1
multipledispatch==0.6.0
navigator-updater==0.2.1
nbconvert==5.6.0
nbformat==4.4.0
networkx==2.3
nltk==3.4.5
nose==1.3.7
notebook==6.0.1
numba==0.45.1
numexpr==2.7.0
numpy==1.16.5
numpydoc==0.9.1
oauthlib==3.1.0
olefile==0.46
openpyxl==3.0.0
opt-einsum==3.1.0
packaging==19.2
pandas==0.25.1
pandocfilters==1.4.2
parso==0.5.1
partd==1.0.0
path.py==12.0.1
pathlib2==2.3.5
patsy==0.5.1
pep8==1.7.1
pickleshare==0.7.5
Pillow==6.2.0
pkginfo==1.5.0.1
pluggy==0.13.0
ply==3.11
prometheus-client==0.7.1
prompt-toolkit==2.0.10
protobuf==3.11.2
psutil==5.6.3
py==1.8.0
pyasn1==0.4.8
pyasn1-modules==0.2.7
pycodestyle==2.5.0
pycosat==0.6.3
pycparser==2.19
pycrypto==2.6.1
pycurl==7.43.0.3
pyflakes==2.1.1
Pygments==2.4.2
pylint==2.4.2
pyodbc==4.0.27
pyOpenSSL==19.0.0
pyparsing==2.4.2
pyreadline==2.1
pyrsistent==0.15.4
PySocks==1.7.1
pytest==5.2.1
pytest-arraydiff==0.3
pytest-astropy==0.5.0
pytest-doctestplus==0.4.0
pytest-openfiles==0.4.0
pytest-remotedata==0.3.2
python-dateutil==2.8.0
pytils==0.3
pytz==2019.3
PyWavelets==1.0.3
pywin32==223
pywinpty==0.5.5
PyYAML==5.1.2
pyzmq==18.1.0
QtAwesome==0.6.0
qtconsole==4.5.5
QtPy==1.9.0
requests==2.22.0
requests-oauthlib==1.3.0
rope==0.14.0
rsa==4.0
ruamel-yaml==0.15.46
scikit-image==0.15.0
scikit-learn==0.21.3
scipy==1.3.1
seaborn==0.9.0
Send2Trash==1.5.0
simplegeneric==0.8.1
singledispatch==3.4.0.3
six==1.12.0
snowballstemmer==2.0.0
sortedcollections==1.1.2
sortedcontainers==2.1.0
soupsieve==1.9.3
Sphinx==2.2.0
sphinxcontrib-applehelp==1.0.1
sphinxcontrib-devhelp==1.0.1
sphinxcontrib-htmlhelp==1.0.2
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.2
sphinxcontrib-serializinghtml==1.1.3
sphinxcontrib-websupport==1.1.2
spyder==3.3.6
spyder-kernels==0.5.2
SQLAlchemy==1.3.9
statsmodels==0.10.1
sympy==1.4
tables==3.5.2
tblib==1.4.0
tensorboard==2.0.0
tensorflow==2.0.0
tensorflow-estimator==2.0.0
termcolor==1.1.0
terminado==0.8.2
testpath==0.4.2
tools==0.1.9
toolz==0.10.0
tornado==6.0.3
tqdm==4.36.1
traitlets==4.3.3
unicodecsv==0.14.1
urllib3==1.24.2
wcwidth==0.1.7
webencodings==0.5.1
Werkzeug==0.16.0
widgetsnbextension==3.5.1
win-inet-pton==1.1.0
win-unicode-console==0.5
wincertstore==0.2
wrapt==1.11.2
xlrd==1.2.0
XlsxWriter==1.2.1
xlwings==0.15.10
xlwt==1.3.0
zict==1.0.0
zipp==0.6.0

pip install -r requirements.txt

After decompressing the iPromoter-5mC_main_program.rar file, change the relevant code according to the comment line at the end of the main_predictor.py file. The relevant result file is directly generated locally, which can realize large data volume, efficient and fast related experiments.

all_negative.fasta and all_positive.fasta is the original data file.
Generate_dataset.py and Generate_traindata.py can generate relevant experimental data.
create_submodeldata.py , get_submodels.py and funsion_test.py Realize model generation, cross-validation and other related work.
dependent_test.py can complete the test task of independent testset.

traindata.rar contains the data required for the training set.
Independent_testset.rar contains the data required by the test set.
model.rar contains the resulting final submodels.