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  1. 1561
    “…RESULTS: Here we report synthaser, a Python package leveraging the NCBI’s conserved domain search tool for remote prediction and classification of fungal megasynth(et)ase domain architectures. …”
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    Online Artículo Texto
    Synthaser: a CD-Search enabled Python toolkit for analysing domain architecture of fungal secondary metabolite megasynth(et)ases
  2. 1562
    Publicado 2021
    “…Software packages were developed and open-sourced through the Python Package Index (PyPi) and the Comprehensive R Archive Network (CRAN). cgmquantify is integrated into the Digital Biomarker Discovery Pipeline and MD2K Cerebral Cortex. …”
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    Cgmquantify: Python and R Software Packages for Comprehensive Analysis of Interstitial Glucose and Glycemic Variability from Continuous Glucose Monitor Data
  3. 1563
    por Isamura, Bienfait K., Lobb, Kevin A.
    Publicado 2022
    “…This short communication reports technical details and preliminary results of a python-based tool (AMADAR) designed to generate any Diels–Alder (DA) transition state geometry (TS) and analyze determined IRC paths in a (quasi-)automated fashion, given the product SMILES. …”
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    Online Artículo Texto
    AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis
  4. 1564
    “…RESULTS: We have implemented i2b2-etl as a Python application that imports ontology and patient data using simplified input file schemas and provides inbuilt record number de-identification and data validation. …”
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    I2b2-etl: Python application for importing electronic health data into the informatics for integrating biology and the bedside platform
  5. 1565
    “…The open-source GReNaDIne Python package is made freely available in a dedicated GitLab repository, as well as in the official third-party software repository PyPI Python Package Index. …”
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    GReNaDIne: A Data-Driven Python Library to Infer Gene Regulatory Networks from Gene Expression Data
  6. 1566
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    Large scale machine learning with Python: learn to build powerful machine learning models quickly and deploy large-scale predictive applications
  7. 1567
    por Cuesta, Hector, Kumar, Sampath
    Publicado 2016
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    Practical data analysis: a practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark
  8. 1568
    por Giridhar, Chetan
    Publicado 2017
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    Automate it!: recipes to upskill your business : put your life on autopilot and make your business processes efficient with the magic and power of Python
  9. 1569
    por Drabas, Tomasz, Lee, Denny, Karau, Holden
    Publicado 2017
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    Learning PySpark: build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0
  10. 1570
    por Gulli, Antonio
    Publicado 2017
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    TensorFlow 1.x deep learning cookbook: over 90 unique recipes to solve artificial-intelligence driven problems with Python
  11. 1571
    por Fontaine, Alan
    Publicado 2018
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    Mastering predictive analytics with scikit-learn and TensorFlow: implement machine learning techniques to build advanced predictive models using Python
  12. 1572
    por Galea, Alex
    Publicado 2018
    “…Applied Data Science with Python and Jupyter teaches you the skills you need for entry-level data science. …”
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    Applied data science with Python and Jupyter: use powerful industry-standard tools to unlock new, actionable insights from your data
  13. 1573
    por Jansen, Stefan
    Publicado 2018
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    Hands-on machine learning for algorithmic trading: design and implement investment strategies based on smart algorithms that learn from data using Python
  14. 1574
    por Pattanayak, Santanu
    Publicado 2019
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    Intelligent projects using Python: 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras
  15. 1575
    “…However, there is limited functionality when studying lists of species that require batch retrieval. To overcome this, a PYTHON package termed Python-Automated Retrieval of TimeTree data (PAReTT) was created to facilitate a biologist-friendly interaction with the TimeTree resource. …”
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    PAReTT: A Python Package for the Automated Retrieval and Management of Divergence Time Data from the TimeTree Resource for Downstream Analyses
  16. 1576
    “…Addressing this need, a flexible, open-source, Python-based tool, synconn_build, capable of generating synthetic building operation data using EnergyPlus as the main building energy simulation engine is introduced. …”
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    Synconn_build: A python based synthetic dataset generator for testing and validating control-oriented neural networks for building dynamics prediction
  17. 1577
    “…A start of enteral nutrition (EN) within 24 hours of onset may reduce the number of infections as compared to the current practice of starting an oral diet and EN if necessary at 3-4 days after admission. METHODS/DESIGN: The PYTHON trial is a randomised controlled, parallel-group, superiority multicenter trial. …”
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    Pancreatitis, very early compared with normal start of enteral feeding (PYTHON trial): design and rationale of a randomised controlled multicenter trial
  18. 1578
    “…Herein, we report the presence of a Borrelia sp. in an Amblyomma varanense collected from Python reticulatus. METHODS: Ticks were collected from snakes, identified to species level and examined by PCR for the presence of Borrelia spp. flaB and 16S rRNA genes. …”
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    Borrelia sp. phylogenetically different from Lyme disease- and relapsing fever-related Borrelia spp. in Amblyomma varanense from Python reticulatus
  19. 1579
    por Lakhujani, Vijay, Badapanda, Chandan
    Publicado 2017
    “…To bypass all these bottlenecks and make this process efficient, we have developed a python based program, prepare_taxa_charts.py, to automate the renaming, cropping and merging of taxonomic pie chart and corresponding legend image into a single, good quality publication ready image. …”
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    prepare_taxa_charts.py: A Python program to automate generation of publication ready taxonomic pie chart images from QIIME
  20. 1580
    “…RESULTS: Here, we present M2R, a Python add-on to cobrapy that allows incorporating information about the gut microbiota metabolism models to human genome-scale metabolic models (GEMs) like RECON3D. …”
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    M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models
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