15 papers · ranked by Valyu relevance
Knut Rand, Ivar Grytten, Milena Pavlovic, Chakravarthi Kanduri + 1 more
Python is a popular and widespread programming language for scientific computing, in large part due to the powerful array programming library NumPy, which makes it easy to write clean, vectorized and efficient code for handling large datasets. A challenge with using array programming for biological data is that the…
Vasudha Jha, Robert H. Cudmore
Brightest path tracing is a widely used image processing technique in several fields including biology, geography, and geology. However, despite the availability of many image processing libraries in Python, few offer an out-of-the-box implementation of a bright-est path tracing algorithm. This paper presents a Python…
Authors not listed
With the rapid growth of chemical data and information, there is an increasing need for chemistry undergraduates to master Python tools for analyzing large chemical datasets and extracting key or feature information. Currently, more than 100,000 types of metal-organic frameworks (MOFs), as the material recently awarded…
Authors not listed
Electrochemiluminescence (ECL) is a vital analytical technique widely used in immunosensing and emerging applica-tions in biological imaging. Traditional ECL simulations rely on finite element methods, which provide valuable insights into reaction dynamics and spatial distribution of species. However, such methods are…
Mauro Silberberg, Henning Hermjakob, Rahuman S. Malik-Sheriff, Hernán E. Grecco
Chemical Reaction Networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level modelling of CRNs enables various simulation approaches, including deterministic and stochastic methods. However, existing Python tools for CRN modelling…
Dimitar Georgiev, Simon Vilms Pedersen, Ruoxiao Xie, Álvaro Fernández-Galiana + 2 more
Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardisation, and the…
Endre Bakken Stovner, Pål Sætrom
Complex genomic analyses often use sequences of simple set operations like intersection, overlap, and nearest on genomic intervals. These operations, coupled with some custom programming, allow a wide range of analyses to be performed. To this end, we have written PyRanges, a data structure for representing and…
Alex M. Ascension, Marcos J. Araúzo-Bravo
Big Data analysis is a discipline with a growing number of areas where huge amounts of data is extracted and analyzed. Parallelization in Python integrates Message Passing Interface via mpi4py module. Since mpi4py does not support parallelization of objects greater than 2^31^ bytes, we developed BigMPI4py, a Python…
Rıza Özçelik, Laura van Weesep, Sarah de Ruiter, Francesca Grisoni
In this work, we introduce peptidy -- a lightweight Python library that facilitates converting peptides (expressed as aminoacid sequences) to numerical representations suited to machine learning. peptidy is free from external dependencies, integrates seamlessly into modern Python environments, and supports a range of…
Boris Yamrom, Yoon-ha Lee, Steven Marks, Lubomir Chorbadjiev + 2 more
Snakemake is one of the most popular workflow management systems, particularly in biological sciences. Snakemake workflows are highly portable, scalable, and transparent. Moreover, they enable the painless reproduction of published results and adaption to similar data processing and analysis projects. Here we present…
Tyler Kolisnik, Faeze Keshavarz-Rahaghi, Rachel Purcell, Adam Smith + 1 more
Random Forest models are widely used in genomic data analysis and can offer insights into complex biological mechanisms, particularly where features influence the target in interactive, non-linear, or non-additive ways. Currently, some of the most efficient random forest methods, in terms of computational speed, are…
Oliver Lee, Malte Gather, Eli Zysman-Colman
We describe a new tool for the efficient management of computational chemistry. Digichem is a program that automates and simplifies nearly the entire computational pipeline, including large-scale batch submission of calculations, analysis and results parsing, the generation of 3D density plots and 2D graphs of…
Esben Bjerrum, Tobias Rastemo, Ross Irwin, Christos Kannas + 1 more
Recent years have seen a large interest in using the Simplified Molecular Input Line Entry System (SMILES) chemical language as input for deep learning architectures solving chemical tasks. Many successful applications have been demonstrated within de novo molecular design, quantitative structure-activity relationship…
Shirin Faraji, Johannes Ehrmaier, Maximilian Menger
Here, PySurf is introduced as an innovative code framework, which is specifically designed for rapid prototyping and development tasks for data-science applications in computational chemistry. To illustrate the potential of the framework, a code for nonadiabatic surface-hopping simulations based on the Landau-Zener…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…