Glycowork: A Python package for glycan data science and machine learning.
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ABSTRACT:
SUBMITTER: Thomes L
PROVIDER: S-EPMC8600276 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundBiomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines.ResultspyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder's capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook.ConclusionsWe introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.
| S-EPMC8685811 | biostudies-literature
Project description:Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.
| S-EPMC8054236 | biostudies-literature
Project description:Microsatellite instability (MSI) is characterized by high degree of polymorphism in microsatellite lengths due to deficiency in mismatch repair (MMR) system. MSI is associated with several tumor types and its status can be considered as an important indicator for patient prognosis. Conventional clinical diagnosis of MSI examines PCR products of a panel of microsatellite markers using electrophoresis (MSI-PCR), which is laborious, costly, and time consuming. We developed MSIpred, a python package for automatic MSI classification using a machine learning technology - support vector machine (SVM). MSIpred computes 22 features characterizing tumor somatic mutational load from mutation data in mutation annotation format (MAF) generated from paired tumor-normal exome sequencing data, subsequently using these features to predict tumor MSI status with a SVM classifier trained by MAF data of 1074 tumors belonging to four types. Evaluation of MSIpred on an independent testing set, MAF data of another 358 tumors, achieved overall accuracy of ?98% and area under receiver operating characteristic (ROC) curve of 0.967. Further analysis on discrepant cases revealed that discrepancies were partially due to misclassification of MSI-PCR. Additional testing of MSIpred on non-TCGA data also validated its good classification performance. These results indicated that MSIpred is a robust pan-tumor MSI classification tool and can serve as a complementary diagnostic to MSI-PCR in MSI diagnosis.
| S-EPMC6277498 | biostudies-other
Project description:By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.
| S-EPMC7453404 | biostudies-literature
Project description:MotivationOntologies contain formal and structured information about a domain and are widely used in bioinformatics for annotation and integration of data. Several methods use ontologies to provide background knowledge in machine learning tasks, which is of particular importance in bioinformatics. These methods rely on a set of common primitives that are not readily available in a software library; a library providing these primitives would facilitate the use of current machine learning methods with ontologies and the development of novel methods for other ontology-based biomedical applications.ResultsWe developed mOWL, a Python library for machine learning with ontologies formalized in the Web Ontology Language (OWL). mOWL implements ontology embedding methods that map information contained in formal knowledge bases and ontologies into vector spaces while preserving some of the properties and relations in ontologies, as well as methods to use these embeddings for similarity computation, deductive inference and zero-shot learning. We demonstrate mOWL on the knowledge-based prediction of protein-protein interactions using the gene ontology and gene-disease associations using phenotype ontologies.Availability and implementationmOWL is freely available on https://github.com/bio-ontology-research-group/mowl and as a Python package in PyPi.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9848046 | biostudies-literature
Project description:Since the first receiver independent exchange format (RINEX) version was released in 1989, it has gone through several versions, making the existing software, such as TEQC, incompatible with certain later versions. This study proposes a new Python package named PyRINEX, which is developed to batch process the most generally used versions of RINEX files, namely 2.0 and 3.0. The proposed package can be used to manage and edit numerous RINEX files as well as perform a data quality check function. PyRINEX can be easily imported into any Python IDE similar to any other open-source Python package, it also makes secondary development easy for users.
| S-EPMC10803049 | biostudies-literature
Project description:Communication constitutes the core of human life. A large portion of our everyday social interactions is non-verbal. Of the sensory modalities we use for non-verbal communication, olfaction (i.e., the sense of smell) is often considered the most enigmatic medium. Outside of our awareness, smells provide information about our identity, emotions, gender, mate compatibility, illness, and potentially more. Yet, body odors are astonishingly complex, with their composition being influenced by various factors. Is there a chemical basis of olfactory communication? Can we identify molecules predictive of psychological states and traits? We propose that answering these questions requires integrating two disciplines: psychology and chemistry. This new field, coined sociochemistry, faces new challenges emerging from the sheer amount of factors causing variability in chemical composition of body odorants on the one hand (e.g., diet, hygiene, skin bacteria, hormones, genes), and variability in psychological states and traits on the other (e.g., genes, culture, hormones, internal state, context). In past research, the reality of these high-dimensional data has been reduced in an attempt to isolate unidimensional factors in small, homogenous samples under tightly controlled settings. Here, we propose big data approaches to establish novel links between chemical and psychological data on a large scale from heterogeneous samples in ecologically valid settings. This approach would increase our grip on the way chemical signals non-verbally and subconsciously affect our social lives across contexts.
| S-EPMC7642605 | biostudies-literature
Project description:PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com.
| S-EPMC8294542 | biostudies-literature
Project description:Single-molecule fluorescence microscopy studies of bacteria provide unique insights into the mechanisms of cellular processes and protein machineries in ways that are unrivalled by any other technique. With the cost of microscopes dropping and the availability of fully automated microscopes, the volume of microscopy data produced has increased tremendously. These developments have moved the bottleneck of throughput from image acquisition and sample preparation to data analysis. Furthermore, requirements for analysis procedures have become more stringent given the demand of various journals to make data and analysis procedures available. To address these issues we have developed a new data analysis package for analysis of fluorescence microscopy data from rod-like cells. Our software ColiCoords structures microscopy data at the single-cell level and implements a coordinate system describing each cell. This allows for the transformation of Cartesian coordinates from transmission light and fluorescence images and single-molecule localization microscopy (SMLM) data to cellular coordinates. Using this transformation, many cells can be combined to increase the statistical power of fluorescence microscopy datasets of any kind. ColiCoords is open source, implemented in the programming language Python, and is extensively documented. This allows for modifications for specific needs or to inspect and publish data analysis procedures. By providing a format that allows for easy sharing of code and associated data, we intend to promote open and reproducible research. The source code and documentation can be found via the project's GitHub page.
| S-EPMC6583990 | biostudies-literature
Project description:BackgroundMachine Learning (ML) has a number of demonstrated applications in protein prediction tasks such as protein structure prediction. To speed further development of machine learning based tools and their release to the community, we have developed a package which characterizes several aspects of a protein commonly used for protein prediction tasks with machine learning.FindingsA number of software libraries and modules exist for handling protein related data. The package we present in this work, PCP-ML, is unique in its small footprint and emphasis on machine learning. Its primary focus is on characterizing various aspects of a protein through sets of numerical data. The generated data can then be used with machine learning tools and/or techniques. PCP-ML is very flexible in how the generated data is formatted and as a result is compatible with a variety of existing machine learning packages. Given its small size, it can be directly packaged and distributed with community developed tools for protein prediction tasks.ConclusionsSource code and example programs are available under a BSD license at http://mlid.cps.cmich.edu/eickh1jl/tools/PCPML/. The package is implemented in C++ and accessible as a Python module.
| S-EPMC4246511 | biostudies-literature