Transcriptional Regulatory Networks across Species : Evolution, Inference, and Refinement

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Adaptive projected variational quantum dynamics

Giuseppe Carleo, David Linteau, Stefano Barison

We propose an adaptive quantum algorithm to prepare accurate variational time evolved wave functions. The method is based on the projected variational quantum dynamics (pVQD) algorithm, that performs a global optimization with linear scaling in the number ...

Amer Physical Soc2024

Molecular hypergraph neural networks

Philippe Schwaller, Junwu Chen

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

Aip Publishing2024

Bacterial diversity dominates variable macrophage responses of tuberculosis patients in Tanzania

Jacques Fellay, Zhi Ming Xu

The Mycobacterium tuberculosis complex (MTBC) comprises nine human-adapted lineages that differ in their geographical distribution. Local adaptation of specific MTBC genotypes to the respective human host population has been invoked in this context. We aim ...

Nature Portfolio2024

Multi-well plate lid for single-step pooling of 96 samples for high-throughput barcode-based sequencing

Bart Deplancke, Daniel Migliozzi, Riccardo Dainese, Daniel Alpern, Gilles Weder, Mustafa Demir, Dariia Gudkova, Hüseyin Baris Atakan

High-throughput transcriptomics is of increasing fundamental biological and clinical interest. The generation of molecular data from large collections of samples, such as biobanks and drug libraries, is boosting the development of new biomarkers and treatm ...

Springer2024

Towards an Understanding of Hydraulic Sensitivity: Graph Theory Contributions to Water Distribution Analysis

Jérôme Chenal

Water distribution systems (WDSs) are complex networks with numerous interconnected junctions and pipes. The robustness and reliability of these systems are critically dependent on their network structure, necessitating detailed analysis for proactive leak ...

MDPI2024

Ancestral genome reconstruction enhances transposable element annotation by identifying degenerate integrants

Didier Trono, Evaristo Jose Planet Letschert, Wayo Matsushima

Growing evidence indicates that transposable elements (TEs) play important roles in evolution by providing genomes with coding and non-coding sequences. Identification of TE-derived functional elements, however, has relied on TE annotations in individual s ...

2024

Environment-dependent epistasis increases phenotypic diversity in gene regulatory networks

Florence Gauye, Florian Baier

Mutations to gene regulatory networks can be maladaptive or a source of evolutionary novelty. Epistasis con-founds our understanding of how mutations affect the expression patterns of gene regulatory networks, a chal-lenge exacerbated by the dependence of ...

AMER ASSOC ADVANCEMENT SCIENCE2023

Impact of phylogeny on structural contact inference from protein sequence data

Anne-Florence Raphaëlle Bitbol, Umberto Lupo, Nicola Dietler

Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino acid usage at contacting sites. Because homologous proteins share a common ance ...

ROYAL SOC2023

Statistical learning quantifies transposable element-mediated cis-regulation

Didier Trono, Evaristo Jose Planet Letschert, Julien Léonard Duc, Cyril David Son-Tuyên Pulver, Alexandre Coudray, Julien Paul André Pontis, Delphine Yvette L Grun, Shaoline Sheppard, Raphaël Gérard Théodore Michel Marie de Deloÿe et Fourcade de Fondeville

Background: Transposable elements (TEs) have colonized the genomes of most metazoans, and many TE-embedded sequences function as cis-regulatory elements (CREs) for genes involved in a wide range of biological processes from early embryo- genesis to innate ...

2023

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Rachid Guerraoui, Andrei Kucharavy, Matteo Monti

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

Assoc Computing Machinery2023

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