Academic literature on the topic 'Novel Python package'
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Journal articles on the topic "Novel Python package"
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Biová, Jana, Nicholas Dietz, Yen On Chan, Trupti Joshi, Kristin Bilyeu, and Mária Škrabišová. "AccuCalc: A Python Package for Accuracy Calculation in GWAS." Genes 14, no. 1 (January 1, 2023): 123. http://dx.doi.org/10.3390/genes14010123.
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The genome-wide association study (GWAS) is a popular genomic approach that identifies genomic regions associated with a phenotype and, thus, aims to discover causative mutations (CM) in the genes underlying the phenotype. However, GWAS discoveries are limited by many factors and typically identify associated genomic regions without the further ability to compare the viability of candidate genes and actual CMs. Therefore, the current methodology is limited to CM identification. In our recent work, we presented a novel approach to an empowered “GWAS to Genes” strategy that we named Synthetic phenotype to causative mutation (SP2CM). We established this strategy to identify CMs in soybean genes and developed a web-based tool for accuracy calculation (AccuTool) for a reference panel of soybean accessions. Here, we describe our further development of the tool that extends its utilization for other species and named it AccuCalc. We enhanced the tool for the analysis of datasets with a low-frequency distribution of a rare phenotype by automated formatting of a synthetic phenotype and added another accuracy-based GWAS evaluation criterion to the accuracy calculation. We designed AccuCalc as a Python package for GWAS data analysis for any user-defined species-independent variant calling format (vcf) or HapMap format (hmp) as input data. AccuCalc saves analysis outputs in user-friendly tab-delimited formats and also offers visualization of the GWAS results as Manhattan plots accentuated by accuracy. Under the hood of Python, AccuCalc is publicly available and, thus, can be used conveniently for the SP2CM strategy utilization for every species.
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STAUDT, CHRISTIAN L., ALEKSEJS SAZONOVS, and HENNING MEYERHENKE. "NetworKit: A tool suite for large-scale complex network analysis." Network Science 4, no. 4 (December 2016): 508–30. http://dx.doi.org/10.1017/nws.2016.20.
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AbstractWe introduce NetworKit, an open-source software package for analyzing the structure of large complex networks. Appropriate algorithmic solutions are required to handle increasingly common large graph data sets containing up to billions of connections. We describe the methodology applied to develop scalable solutions to network analysis problems, including techniques like parallelization, heuristics for computationally expensive problems, efficient data structures, and modular software architecture. Our goal for the software is to package results of our algorithm engineering efforts and put them into the hands of domain experts. NetworKit is implemented as a hybrid combining the kernels written in C++ with a Python frontend, enabling integration into the Python ecosystem of tested tools for data analysis and scientific computing. The package provides a wide range of functionality (including common and novel analytics algorithms and graph generators) and does so via a convenient interface. In an experimental comparison with related software, NetworKit shows the best performance on a range of typical analysis tasks.
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Bodory, Hugo, Hannah Busshoff, and Michael Lechner. "High Resolution Treatment Effects Estimation: Uncovering Effect Heterogeneities with the Modified Causal Forest." Entropy 24, no. 8 (July 28, 2022): 1039. http://dx.doi.org/10.3390/e24081039.
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There is great demand for inferring causal effect heterogeneity and for open-source statistical software, which is readily available for practitioners. The mcf package is an open-source Python package that implements Modified Causal Forest (mcf), a causal machine learner. We replicate three well-known studies in the fields of epidemiology, medicine, and labor economics to demonstrate that our mcf package produces aggregate treatment effects, which align with previous results, and in addition, provides novel insights on causal effect heterogeneity. For all resolutions of treatment effects estimation, which can be identified, the mcf package provides inference. We conclude that the mcf constitutes a practical and extensive tool for a modern causal heterogeneous effects analysis.
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Jadoul, Yannick, Diandra Duengen, and Andrea Ravignani. "Parselmouth for bioacoustics: Integrating Praat into the Python scientific ecosystem." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A29. http://dx.doi.org/10.1121/10.0010550.
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As collected datasets become larger and computational analyses become ever more complex, the efficient processing of bioacoustical data is a crucial problem to tackle. Often, during data exploration and analysis, different research software packages need to be flexibly combined in a script. A typical example of such a multi-faceted workflow is the extraction of acoustic parameters from a recording, which are then plotted and tested for statistical significance. Parselmouth is an open-source Python library for Praat, a widely used acoustics and phonetics software package implementing acoustic algorithms and analyses regularly adopted in bioacoustics research. Parselmouth's goal is to provide a full-fledged Python library that integrates efficiently into the larger Python ecosystem. This way, it not only simplifies the application of Praat’s functionality within a typical data analysis workflow but also enables the creation of new experimental tools. Parselmouth’s contribution to bioacoustics research can be highlighted through concrete examples of studies we have conducted, e.g., on vocal flexibility in seals. Moreover, the integration of Praat’s functionality into a general-purpose programming language allows for novel, more complex experimental setups: for example, the integration of Parselmouth into a custom-created software tool permits live-monitoring and instantly evaluating the vocal development during animal training.
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Romano, Joseph D., Trang T. Le, William La Cava, John T. Gregg, Daniel J. Goldberg, Praneel Chakraborty, Natasha L. Ray, Daniel Himmelstein, Weixuan Fu, and Jason H. Moore. "PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods." Bioinformatics 38, no. 3 (October 22, 2021): 878–80. http://dx.doi.org/10.1093/bioinformatics/btab727.
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Abstract Motivation Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that provide rapid access to many of these datasets through a standardized, user-friendly interface that integrates well with popular data science workflows. Results This release of PMLB (Penn Machine Learning Benchmarks) provides the largest collection of diverse, public benchmark datasets for evaluating new machine learning and data science methods aggregated in one location. v1.0 introduces a number of critical improvements developed following discussions with the open-source community. Availability and implementation PMLB is available at https://github.com/EpistasisLab/pmlb. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.
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Dallilar, Y., S. von Fellenberg, M. Bauboeck, P. T. de Zeeuw, A. Drescher, F. Eisenhauer, R. Genzel, et al. "Flaremodel: An open-source Python package for one-zone numerical modelling of synchrotron sources." Astronomy & Astrophysics 658 (February 2022): A111. http://dx.doi.org/10.1051/0004-6361/202142458.
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Synchrotron processes, the radiative processes associated with the interaction of energetic charged particles with magnetic field, are of interest in many areas in astronomy, from the interstellar medium to extreme environments near compact objects. Consequently, observations of synchrotron sources carry information on the physical properties of the sources themselves and those of their close vicinity. In recent years, novel observations of such sources with multi-wavelength collaborations reveal complex features and peculiarities, especially near black holes. Exploring the nature of these sources in more detail necessitates numerical tools complementary to analytical one-zone modelling efforts. In this paper, we introduce an open-source Python package tailored to this purpose, FLAREMODEL. The core of the code consists of low-level utility functions to describe physical processes relevant to synchrotron sources, which are written in C for performance and parallelised with OpenMP for scalability. The Python interface provides access to these functions and built-in source models are provided as a guidance. At the same time, the modular design of the code and the generic nature of these functions enable users to build a variety of source models applicable to many astrophysical synchrotron sources. We describe our methodology and the structure of our code along with selected examples demonstrating capabilities and options for future modelling efforts.
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Bucur, C. "Artificial intelligence driven speed controller for DC motor in series." Scientific Bulletin of Naval Academy XIV, no. 2 (December 15, 2021): 83–88. http://dx.doi.org/10.21279/1454-864x-21-i2-007.
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Recently a lot of work have been done to implement artificial intelligence controllers in the field of electrical motors. This paper presents a novel speed controller, developed through Reinforcement learning techniques, applied to series dc motors. We emphasize the ease of developed controller in available off the shelf hardware for industrial use. We used the open- source Python package gym-electric-motor [1] for environment setup, pytorch framework for developing the controller and .NET for performance evaluation.
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Gruenstaeudl, Michael. "annonex2embl: automatic preparation of annotated DNA sequences for bulk submissions to ENA." Bioinformatics 36, no. 12 (March 30, 2020): 3841–48. http://dx.doi.org/10.1093/bioinformatics/btaa209.
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Abstract Motivation The submission of annotated sequence data to public sequence databases constitutes a central pillar in biological research. The surge of novel DNA sequences awaiting database submission due to the application of next-generation sequencing has increased the need for software tools that facilitate bulk submissions. This need has yet to be met with the concurrent development of tools to automate the preparatory work preceding such submissions. Results The author introduce annonex2embl, a Python package that automates the preparation of complete sequence flatfiles for large-scale sequence submissions to the European Nucleotide Archive. The tool enables the conversion of DNA sequence alignments that are co-supplied with sequence annotations and metadata to submission-ready flatfiles. Among other features, the software automatically accounts for length differences among the input sequences while maintaining correct annotations, automatically interlaces metadata to each record and displays a design suitable for easy integration into bioinformatic workflows. As proof of its utility, annonex2embl is employed in preparing a dataset of more than 1500 fungal DNA sequences for database submission. Availability and implementation annonex2embl is freely available via the Python package index at http://pypi.python.org/pypi/annonex2embl. Supplementary information Supplementary data are available at Bioinformatics online.
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Heybrock, Simon, Owen Arnold, Igor Gudich, Daniel Nixon, and Neil Vaytet. "Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python." Journal of Neutron Research 22, no. 2-3 (October 20, 2020): 169–81. http://dx.doi.org/10.3233/jnr-190131.
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scipp is heavily inspired by the Python library xarray. It enriches raw NumPy-like multi-dimensional arrays of data by adding named dimensions and associated coordinates. Multiple arrays are combined into datasets. On top of this, scipp introduces (i) implicit handling of physical units, (ii) implicit propagation of uncertainties, (iii) support for histograms, i.e., bin-edge coordinate axes, which exceed the data’s dimension extent by one, and (iv) support for event data. In conjunction these features enable a more natural and more concise user experience. The combination of named dimensions, coordinates, and units helps to drastically reduce the risk for programming errors. The core of scipp is written in C++ to open opportunities for performance improvements that a Python-based solution would not allow for. On top of the C++ core, scipp’s Python components provide functionality for plotting and content representations, e.g., for use in Jupyter Notebooks. While none of scipp’s concepts in isolation is novel per-se, we are not aware of any project combining all of these aspects in a single coherent software package.
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Reininghaus, Maximilian, and Ralf Ulrich. "CORSIKA 8 – Towards a modern framework for the simulation of extensive air showers." EPJ Web of Conferences 210 (2019): 02011. http://dx.doi.org/10.1051/epjconf/201921002011.
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Current and future challenges in astroparticle physics require novel simulation tools to achieve higher precision and more flexibility. For three decades the FORTRAN version of CORSIKA served the community in an excellent way. However, the effort to maintain and further develop this complex package is getting increasingly difficult. To overcome existing limitations, and designed as a very open platform for all particle cascade simulations in astroparticle physics, we are developing CORSIKA 8 based on modern C++ and Python concepts. Here, we give a brief status report of the project.
Book chapters on the topic "Novel Python package"
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Nanni, Luca. "Computational Inference of DNA Folding Principles: From Data Management to Machine Learning." In Special Topics in Information Technology, 79–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_7.
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AbstractDNA is the molecular basis of life and would total about three meters if linearly untangled. To fit in the cell nucleus at the micrometer scale, DNA has, therefore, to fold itself into several layers of hierarchical structures, which are thought to be associated with functional compartmentalization of genomic features like genes and their regulatory elements. For this reason, understanding the mechanisms of genome folding is a major biological research problem. Studying chromatin conformation requires high computational resources and complex data analyses pipelines. In this chapter, we first present the PyGMQL software for interactive and scalable data exploration for genomic data. PyGMQL allows the user to inspect genomic datasets and design complex analysis pipelines. The software presents itself as a easy-to-use Python library and interacts seamlessly with other data analysis packages. We then use the software for the study of chromatin conformation data. We focus on the epigenetic determinants of Topologically Associating Domains (TADs), which are region of high self chromatin interaction. The results of this study highlight the existence of a “grammar of genome folding” which dictates the formation of TADs and boundaries, which is based on the CTCF insulator protein. Finally we focus on the relationship between chromatin conformation and gene expression, designing a graph representation learning model for the prediction of gene co-expression from gene topological features obtained from chromatin conformation data. We demonstrate a correlation between chromatin topology and co-expression, shedding a new light on this debated topic and providing a novel computational framework for the study of co-expression networks.