scholarly journals VeridicalFlow: a Python package for building trustworthy data science pipelines with PCS

2022 ◽  
Vol 7 (69) ◽  
pp. 3895
Author(s):  
James Duncan ◽  
Rush Kapoor ◽  
Abhineet Agarwal ◽  
Chandan Singh ◽  
Bin Yu
Keyword(s):  
Data Science ◽  
Python Package

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 pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Victor da Silva Guerra ◽  
Helder Veras Ribeiro-Filho ◽  
Gabriel Ernesto Jara ◽  
Leandro Oliveira Bortot ◽  
José Geraldo de Carvalho Pereira ◽  
...  
Keyword(s):  
Data Structures ◽  
Data Science ◽  
3D Visualization ◽  
Building Blocks ◽  
Third Party ◽  
Biological Processes ◽  
Homologous Proteins ◽  
Cavity Detection ◽  
Data Intensive ◽  
Python Package

Abstract Background Biomolecular 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. Results pyKVFinder 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. Conclusions We 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.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

About digital transformation of the transport industry in Russia

2020 ◽  
Vol 19 (3) ◽  
pp. 95-105
Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Data Science ◽  
Digital Transformation ◽  
Digital Culture ◽  
Blockchain Technology ◽  
Community Approach ◽  
Non Linear ◽  
Transport Industry ◽  
Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

Data Mining and Statistics in Data Science

2019 ◽  
Vol 5 (30) ◽  
pp. 960-968
Author(s):  
Güner Gözde KILIÇ
Keyword(s):  
Data Mining ◽  
Data Science

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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