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seqprops

Sequential properties PyPI package

https://github.com/eotovic/seqprops

Science Score: 44.0%

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Last synced: 7 months ago · JSON representation ·

Repository

Sequential properties PyPI package

Basic Info
  • Host: GitHub
  • Owner: eotovic
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 29.3 KB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created almost 4 years ago · Last pushed about 3 years ago
Metadata Files
Readme License Citation

README.md

Sequential properties - peptide representation scheme

This package contains implementation of sequential properties representation scheme from the paper "Sequential properties representation scheme for recurrent neural network based prediction of therapeutic peptides". If you use this package in your work, please cite it as below or use the citation option in the side menu.

Otović, E., Njirjak, M., Kalafatovic, D., & Mauša, G. (2022). Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides. Journal of Chemical Information and Modeling, 62(12), 2961-2972.

You can install this package manually from this repository or from PyPI repository with pip install seqprops

Usage

from seqprops import SequentialPropertiesEncoder encoder = SequentialPropertiesEncoder() encoder.encode(["AA", "HTTA"])

Minimal working example

```` import numpy as np from sklearn.preprocessing import MinMaxScaler from tensorflow import keras from seqprops import SequentialPropertiesEncoder from tensorflow.keras.models import Model from tensorflow.keras.layers import Dense, Input, LSTM

Some input data

sequences = ["AAC", "ACACA", "AHHHTK", "HH"] y = np.array([0, 1, 1, 0])

Encode sequences

encoder = SequentialPropertiesEncoder(scaler=MinMaxScaler(feature_range=(-1, 1))) X = encoder.encode(sequences)

Define a model

modelinput = Input(shape=X.shape[1:], name="input1") x = LSTM(32, unroll=True, name="bilstm")(modelinput) x = Dense(1, activation='sigmoid', name="outputdense")(x) model = Model(inputs=modelinput, outputs=x)

Model training

adamoptimizer = keras.optimizers.Adam() model.compile(loss="binarycrossentropy", optimizer=adam_optimizer) model.fit( X, y, ) ````

Available properties

You can list available properties with: print(encoder.get_available_properties())

To manually select specific properties: encoder.select_properties(['MSWHIM_MSWHIM3', 'tScales_T1'])

For automatic feature selection, the users are referred to function feature_selection and usage example here

Owner

  • Name: Erik Otović
  • Login: eotovic
  • Kind: user
  • Location: Rijeka, Croatia
  • Company: Faculty of Engineering, University of Rijeka

Computer science PhD student at Faculty of Engineering, University of Rijeka.

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Otović"
  given-names: "Erik"
  orcid: "https://orcid.org/0000-0001-5713-5879"
- family-names: "Njirjak"
  given-names: "Marko"
  orcid: "https://orcid.org/0000-0003-0274-4866"
- family-names: "Kalafatovic"
  given-names: "Daniela"
  orcid: "https://orcid.org/0000-0002-9685-1162"
- family-names: "Mauša"
  given-names: "Goran"
  orcid: "https://orcid.org/0000-0002-0643-4577"
title: "Sequential properties peptide representation scheme"
version: 1.0.3
date-released: 2022-09-28
url: "https://github.com/eotovic/seqprops"
preferred-citation:
  type: article
  authors:
  - family-names: "Otović"
    given-names: "Erik"
    orcid: "https://orcid.org/0000-0001-5713-5879"
  - family-names: "Njirjak"
    given-names: "Marko"
    orcid: "https://orcid.org/0000-0003-0274-4866"
  - family-names: "Kalafatovic"
    given-names: "Daniela"
    orcid: "https://orcid.org/0000-0002-9685-1162"
  - family-names: "Mauša"
    given-names: "Goran"
    orcid: "https://orcid.org/0000-0002-0643-4577"
  doi: "10.1021/acs.jcim.2c00526"
  journal: "Journal of Chemical Information and Modeling"
  month: 6
  start: 2961
  end: 2972
  title: "Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides"
  issue: 12
  volume: 62
  year: 2022

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  • Total packages: 1
  • Total downloads:
    • pypi 8 last-month
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  • Total dependent repositories: 1
  • Total versions: 4
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pypi.org: seqprops
  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 8 Last month
Rankings
Dependent packages count: 10.1%
Dependent repos count: 21.5%
Forks count: 29.8%
Average: 31.5%
Stargazers count: 31.9%
Downloads: 64.4%
Maintainers (1)
eotovic
Last synced: 7 months ago

Dependencies

seqprops/seqprops.egg-info/requires.txt pypi
  • scikit-learn *
setup.py pypi
  • dask *
  • distributed *
  • numpy *
  • pandas *
  • scikit-learn *