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EDITORIAL: Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning

Abstract

Not specified

Authors: Ganna Gryn’ova, Tristan Bereau, Carolin Müller, Pascal Friederich, Rebecca C. Wade, Ariane Nunes-Alves, Thereza A. Soares, Kenneth Merz

Date Published: 12th Aug 2024

Publication Type: Journal

EDITORIAL: Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning

Abstract

Not specified

Authors: Ganna Gryn’ova, Tristan Bereau, Carolin Müller, Pascal Friederich, Rebecca C. Wade, Ariane Nunes-Alves, Thereza A. Soares, Kenneth Merz

Date Published: 12th Aug 2024

Publication Type: Journal

Re-Engineering Genomic Tree Sequence Inference Algorithms

Abstract (Expand)

In the field of population genetics, the driving forces of evolution within species can be studied with trees. Along a genome, each tree describes the local ancestries of a small genomic region. Together, … those trees form a tree sequence that describes the ancestry of a population at every site of the sequence. Inferring tree sequences for whole genomes with many haplotype samples is a computationally expensive task, however. The state-of-the-art tool to infer tree sequences is tsinfer, which infers ancestries for human chromosomes from 5000 samples within a few hours. The tool has the capability to parallelize the computation, but we identify a structure in the input data that limits its parallelizability. We propose a novel parallelization scheme aiming to improve scaling at high thread counts, independently of this structure. Furthermore, we propose several optimizations for the inference algorithm, improving cache efficiency and reducing the number of operations per iteration. We provide a proof-of-concept implementation, and compare the computation speed of our implementation and tsinfer. When inferring ancestries for the 1000 Genomes Project, our implementation is consistently faster by a factor of 1.9 to 2.4. Additionally, depending on the choice of parameters, our parallelization scheme scales better between 32 and 96 cores, improving its speed advantage, especially at higher core counts. In phases where our novel parallelization scheme does not apply, our optimizations still improve the runtime by a factor of 2.2. As available genomic data sets are growing rapidly in size, our contribution decreases the computation time and enables better parallelization, allowing the processing of larger data sets in reasonable time frames

Authors: Johannes Hengstler, Lukas Hübner, Alexandros Stamatakis

Date Published: 1st Aug 2024

Publication Type: Master's Thesis

Evolution and final fate of massive post-common-envelope binaries

Abstract

Not specified

Authors: Dandan Wei, Fabian R. N. Schneider, Philipp Podsiadlowski, Eva Laplace, Friedrich K. Röpke, Marco Vetter

Date Published: 1st Aug 2024

Publication Type: Journal

Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases

Abstract

Not specified

Authors: Elaine Zaunseder, Ulrike Mütze, Jürgen G. Okun, Georg F. Hoffmann, Stefan Kölker, Vincent Heuveline, Ines Thiele

Date Published: 1st Aug 2024

Publication Type: Journal

Exploring the range of impacts of helium in the spectra of double detonation models for Type Ia supernovae

Abstract

Not specified

Authors: F. P. Callan, C. E. Collins, S. A. Sim, L. J. Shingles, R. Pakmor, S. Srivastav, J. M. Pollin, S. Gronow, F. K. Roepke, I. R. Seitenzahl

Date Published: 1st Aug 2024

Publication Type: Journal

Evolution and final fate of massive post-common-envelope binaries

Abstract

Not specified

Authors: Dandan Wei, Fabian R. N. Schneider, Philipp Podsiadlowski, Eva Laplace, Friedrich K. Röpke, Marco Vetter

Date Published: 1st Aug 2024

Publication Type: Journal

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