SUMMARY: We present NetworkCommons, a platform for integrating prior knowledge, omics data, and network inference methods, facilitating their usage and evaluation. NetworkCommons aims to be an infrastructure for the network biology community that supports the development of better methods and benchmarks, by enhancing interoperability and integration. AVAILABILITY AND IMPLEMENTATION: NetworkCommons is implemented in Python and offers programmatic access to multiple omics datasets, network inference methods, and benchmarking setups. It is a free software, available at https://github.com/saezlab/networkcommons, and deposited in Zenodo at https://doi.org/10.5281/zenodo.14719118.

It is estimated that between 50% and 75% of all cases of dementia are due to Alzheimer's disease (AD), the most common neurodegenerative disease among World population. However, a long preclinical period of AD makes it difficult to differentiate between people with Mild Cognitive Impairment (MCI) that would progress to dementia from people with MCI that would not. One of the most promising solutions to detect MCI which will evolve to dementia (preAD) comes from the field of automatic speech analysis. Speech is a complex physiological and neurocognitive language-mediated process, which can be significantly altered in pathological aging and exhibit high levels of sensitivity for the diagnosis of neurological diseases. The purpose of this research is to offer a detailed perspective on the speech changes in MCI and mild AD when compared to healthy aging (HA), that would allow to detect pathological processes prior to the clinical expression of AD. Based on our previous research record on speech in HA, MCI and AD, we provide a global review of dementia-related speech traits and propose a reading-based protocol for assessing ongoing neurodegenerative processes in the elderly. We report the results of speech analysis in elderly people with different cognitive profiles, who performed a standardized reading task and were further analyzed for correlations between neurocognitive assessment indicative of cognitive impairment stage (HA, MCI or AD) and acoustic, temporal and prosodic traits in speech. We show that evolution from HA to AD exhibits a steady pattern of speech changes in parallel to the cognitive decline, which consists in significant increase in duration and phonation time, extension of pauses and voice breaks, intensification of variation in syllabic production, and decrease in speech energy and intensity leading to dysphony. In doing so, we prove that a standardized reading task is a very useful type of stimuli for detecting dementia-related speech traits and, in view of this, we discuss the relevance of reading for preclinical automated diagnosis of AD. The main contribution of this paper is a corpus of recordings of the standardized reading task performed by healthy elderly people and people with MCI and AD in Spanish language, and which can be used for further research purposes. In this respect, our work fills an important gap existing in corpora-based studies of speech and language impairments related to progression to dementia.

MOTIVATION: Genetic interaction (GI) patterns are characterized by the phenotypes of interacting single and double mutated gene pairs. Uncovering the regulatory mechanisms of GIs would provide a better understanding of their role in biological processes, diseases and drug response. Computational analyses can provide insights into the underpinning mechanisms of GIs. RESULTS: In this study, we present a framework for exhaustive modelling of GI patterns using Petri nets (PN). Four-node models were defined and generated on three levels with restrictions, to enable an exhaustive approach. Simulations suggest ∼5 million models of GIs. Generalizing these we propose putative mechanisms for the GI patterns, inversion and suppression. We demonstrate that exhaustive PN modelling enables reasoning about mechanisms of GIs when only the phenotypes of gene pairs are known. The framework can be applied to other GI or genetic regulatory datasets. AVAILABILITY AND IMPLEMENTATION: The framework is available at http://www.ibi.vu.nl/programs/ExhMod. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.