5,268 research outputs found
Interoperability in the OpenDreamKit Project: The Math-in-the-Middle Approach
OpenDreamKit --- "Open Digital Research Environment Toolkit for the
Advancement of Mathematics" --- is an H2020 EU Research Infrastructure project
that aims at supporting, over the period 2015--2019, the ecosystem of
open-source mathematical software systems. From that, OpenDreamKit will deliver
a flexible toolkit enabling research groups to set up Virtual Research
Environments, customised to meet the varied needs of research projects in pure
mathematics and applications.
An important step in the OpenDreamKit endeavor is to foster the
interoperability between a variety of systems, ranging from computer algebra
systems over mathematical databases to front-ends. This is the mission of the
integration work package (WP6). We report on experiments and future plans with
the \emph{Math-in-the-Middle} approach. This information architecture consists
in a central mathematical ontology that documents the domain and fixes a joint
vocabulary, combined with specifications of the functionalities of the various
systems. Interaction between systems can then be enriched by pivoting off this
information architecture.Comment: 15 pages, 7 figure
SigTree: A Microbial Community Analysis Tool to Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree.
Microbial community analysis experiments to assess the effect of a treatment intervention (or environmental change) on the relative abundance levels of multiple related microbial species (or operational taxonomic units) simultaneously using high throughput genomics are becoming increasingly common. Within the framework of the evolutionary phylogeny of all species considered in the experiment, this translates to a statistical need to identify the phylogenetic branches that exhibit a significant consensus response (in terms of operational taxonomic unit abundance) to the intervention. We present the R software package SigTree, a collection of flexible tools that make use of meta-analysis methods and regular expressions to identify and visualize significantly responsive branches in a phylogenetic tree, while appropriately adjusting for multiple comparisons
Many bioinformatics programming tasks can be automated with ChatGPT
Computer programming is a fundamental tool for life scientists, allowing them
to carry out many essential research tasks. However, despite a variety of
educational efforts, learning to write code can be a challenging endeavor for
both researchers and students in life science disciplines. Recent advances in
artificial intelligence have made it possible to translate human-language
prompts to functional code, raising questions about whether these technologies
can aid (or replace) life scientists' efforts to write code. Using 184
programming exercises from an introductory-bioinformatics course, we evaluated
the extent to which one such model -- OpenAI's ChatGPT -- can successfully
complete basic- to moderate-level programming tasks. On its first attempt,
ChatGPT solved 139 (75.5%) of the exercises. For the remaining exercises, we
provided natural-language feedback to the model, prompting it to try different
approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the
exercises. These findings have important implications for life-sciences
research and education. For many programming tasks, researchers no longer need
to write code from scratch. Instead, machine-learning models may produce usable
solutions. Instructors may need to adapt their pedagogical approaches and
assessment techniques to account for these new capabilities that are available
to the general public.Comment: 13 pages, 4 figures, to be submitted for publicatio
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