scholarly journals DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning

2020 ◽  
Author(s):  
Wei Hao Khoong
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Preprocessing ◽  
Weighted Distance ◽  
Open Source License ◽  
Classification Tasks ◽  
Python Package

In this paper, we introduce deboost, a Python library devoted to weighted distance ensembling of predictions for regression and classification tasks. Its backbone resides on the scikit-learn library for default models and data preprocessing functions. It offers flexible choices of models for the ensemble as long as they contain the predict method, like the models available from scikit-learn. deboost is released under the MIT open-source license and can be downloaded from the Python Package Index (PyPI) at https://pypi.org/project/deboost. The source scripts are also available on a GitHub repository at https://github.com/weihao94/DEBoost.

scholarly journals Glycowork: A Python package for glycan data science and machine learning

2021 ◽  
Author(s):  
Luc Thomès ◽  
Rebekka Burkholz ◽  
Daniel Bojar
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Science ◽  
Biological Processes ◽  
Biological Sequence ◽  
Learning Models ◽  
Related Data ◽  
Strong Focus ◽  
Python Package ◽  
Machine Learning Models

AbstractAs a biological sequence, glycans occur in every domain of life and comprise monosaccharides that are chained together to form oligo- or polysaccharides. While glycans are crucial for most biological processes, existing analysis modalities make it difficult for researchers with limited computational background to include information from these diverse and nonlinear sequences into standard workflows. Here, we present glycowork, an open-source Python package that was designed for the processing and analysis of glycan data by end users, with a strong focus on glycan-related data science and machine learning. Glycowork includes numerous functions to, for instance, automatically annotate glycan motifs and analyze their distributions via heatmaps and statistical enrichment. We also provide visualization methods, routines to interact with stored databases, trained machine learning models, and learned glycan representations. We envision that glycowork can extract further insights from any glycan dataset and demonstrate this with several workflows that analyze glycan motifs in various biological contexts. Glycowork can be freely accessed at https://github.com/BojarLab/glycowork/.

scholarly journals Minimum Viable Model Estimates for Machine Learning Projects

2020 ◽  
Author(s):  
John Hawkins
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Predictive Model ◽  
Management System ◽  
Technical Difficulty ◽  
Business Case ◽  
Performance Characteristics ◽  
Learning Projects ◽  
Viable Model ◽  
Python Package

Prioritization of machine learning projects requires estimates of both the potential ROI of the business case and the technical difficulty of building a model with the required characteristics. In this work we present a technique for estimating the minimum required performance characteristics of a predictive model given a set of information about how it will be used. This technique will result in robust, objective comparisons between potential projects. The resulting estimates will allow data scientists and managers to evaluate whether a proposed machine learning project is likely to succeed before any modelling needs to be done. The technique has been implemented into the open source application MinViME (Minimum Viable Model Estimator) which can be installed via the PyPI python package management system, or downloaded directly from the GitHub repository. Available at https://github.com/john-hawkins/MinViME.

scholarly journals Vaxrank: A computational tool for designing personalized cancer vaccines

2017 ◽  
Author(s):  
Alexander Rubinsteyn ◽  
Isaac Hodes ◽  
Julia Kodysh ◽  
Jeffrey Hammerbacher
Keyword(s):  
Open Source ◽  
Cancer Immunotherapy ◽  
Cancer Vaccines ◽  
Tumor Antigens ◽  
Computational Tool ◽  
Therapeutic Vaccines ◽  
Open Source License ◽  
Hla Type ◽  
Personalized Cancer ◽  
Python Package

AbstractTherapeutic vaccines targeting mutant tumor antigens (“neoantigens”) are an increasingly popular form of personalized cancer immunotherapy. Vaxrank is a computational tool for selecting neoantigen vaccine peptides from tumor mutations, tumor RNA data, and patient HLA type. Vaxrank is freely available at www.github.com/openvax/vaxrank under the Apache 2.0 open source license and can also be installed from the Python Package Index.

Machine Learning-Based Detection of Open Source License Exceptions

2017 ◽  
Author(s):  
Christopher Vendome ◽  
Mario Linares-Vasquez ◽  
Gabriele Bavota ◽  
Massimiliano Di Penta ◽  
Daniel German ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source License

scholarly journals SimpleSBML: A Python package for creating and editing SBML models

2015 ◽  
Author(s):  
Caroline Cannistra ◽  
Kyle Medley ◽  
Herbert M. Sauro
Keyword(s):  
Open Source ◽  
Simple Extension ◽  
New Model ◽  
Link Type ◽  
Open Source License ◽  
Technical Report ◽  
New Models ◽  
Python Package ◽  
Sbml Format

AbstractSummaryIn this technical report we describe a simple extension to python-libSBML that allows users of Python to more easily construct SBML based models. The most commonly used package for constructing SBML models in Python is python-libSBML based on the C/C++ library libSBML. python-libSBML supports a comprehensive set of model types, but is difficult for new users to learn and requires long scripts to create even the simplest models. We present SimpleSBML, a package that allows users to add species, parameters, reactions, events, and rules to a libSBML model with only one command for each. Models can be exported to SBML format, and SBML files can be imported and converted to SimpleSBML commands that creates each element in a new model. This allows users to create new models and edit existing models for use with other software.Accessibility and ImplementationSimpleSBML is publicly available and licensed under the liberal Apache 2.0 open source license. It supports SBML levels 2 and 3. Its only dependency is libSBML. It is supported on Windows and Mac OS X. All code has been deposited at the GitHub site https://github.com/sys-bio/[email protected] or [email protected]

scholarly journals Building A Platform for Machine Learning Operations from Open Source Frameworks

2020 ◽  
Vol 53 (5) ◽  
pp. 704-709
Author(s):  
Yan Liu ◽  
Zhijing Ling ◽  
Boyu Huo ◽  
Boqian Wang ◽  
Tianen Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source

scholarly journals SynBiopython: an open-source software library for Synthetic Biology

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Jing Wui Yeoh ◽  
Neil Swainston ◽  
Peter Vegh ◽  
Valentin Zulkower ◽  
Pablo Carbonell ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Open Source ◽  
Open Source Software ◽  
Development Projects ◽  
Software Library ◽  
Current State ◽  
Starting Point ◽  
Common Problems ◽  
Data Tracking ◽  
Python Package

Abstract Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.

scholarly journals Selection of Suitable Machine Learning Algorithms for Classification Tasks in Reverse Logistics

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 272-277
Author(s):  
Hannah Lickert ◽  
Aleksandra Wewer ◽  
Sören Dittmann ◽  
Pinar Bilge ◽  
Franz Dietrich
Keyword(s):  
Machine Learning ◽  
Reverse Logistics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Tasks ◽  
Selection Of
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