Noninvasive, label-free image approaches to predict multimodal molecular markers in pluripotency assessment.

Manufacturing regenerative medicine requires continuous monitoring of pluripotent cell culture and quality assessment while eliminating cell destruction and contaminants. In this study, we employed a novel method to monitor the pluripotency of stem cells through image analysis, avoiding the traditionally used invasive procedures. This approach employs machine learning algorithms to analyze stem cell images to predict the expression of pluripotency markers, such as OCT4 and NANOG, without physically interacting with or harming cells.

Discovering and exploring the hidden diversity of human gut viruses using highly enriched virome samples.

Viruses are an abundant and crucial component of the human microbiome, but accurately discovering them via metagenomics is still challenging. Currently, the available viral reference genomes poorly represent the diversity in microbiome samples, and expanding such a set of viral references is difficult. As a result, many viruses are still undetectable through metagenomics even when considering the power of metagenomic assembly and binning, as viruses lack universal markers.

DTox: A deep neural network-based in visio lens for large scale toxicogenomics data.

With the advancement of large-scale omics technologies, particularly transcriptomics data sets on drug and treatment response repositories available in public domain, toxicogenomics has emerged as a key field in safety pharmacology and chemical risk assessment. Traditional statistics-based bioinformatics analysis poses challenges in its application across multidimensional toxicogenomic data, including administration time, dosage, and gene expression levels. Motivated by the visual inspection workflow of field experts to augment their efficiency of screening significant genes to derive meaningful insights, together with the ability of deep neural architectures to learn the image signals, we developed DTox, a deep neural network-based in visio approach.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.

Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms.

Auxiliary self-supervision to metric learning for music similarity-based retrieval and auto-tagging.

In the realm of music information retrieval, similarity-based retrieval and auto-tagging serve as essential components. Similarity-based retrieval involves automatically analyzing a music track and fetching analogous tracks from a database. Auto-tagging, on the other hand, assesses a music track to deduce associated tags, such as genre and mood.

The Evolution, Assembly, and Dynamics of Marine Holobionts.

The holobiont concept (i.e., multiple living beings in close symbiosis with one another and functioning as a unit) is revolutionizing our understanding of biology, especially in marine systems.

Nicotinamide mononucleotide (NMN) alleviates the poly(I:C)-induced inflammatory response in human primary cell cultures.

NMN is the direct precursor of nicotinamide adenine dinucleotide (NAD+) and is considered as a key factor for increasing NAD+ levels and mitochondrial activity in cells. In this study, based on transcriptome analysis, we showed that NMN alleviates the poly(I:C)-induced inflammatory response in cultures of two types of human primary cells, human pulmonary microvascular endothelial cells (HPMECs) and human coronary artery endothelial cells (HCAECs). Major inflammatory mediators, including IL6 and PARP family members, were grouped into coexpressed gene modules and significantly downregulated under NMN exposure in poly(I:C)-activated conditions in both cell types.

eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio-temporal architectures of cells.

Background: Bioinformatics capability to analyze spatio-temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided.

Human immune and gut microbial parameters associated with inter-individual variations in COVID-19 mRNA vaccine-induced immunity.

COVID-19 mRNA vaccines induce protective adaptive immunity against SARS-CoV-2 in most individuals, but there is wide variation in levels of vaccine-induced antibody and T-cell responses. However, the mechanisms underlying this inter-individual variation remain unclear. Here, using a systems biology approach based on multi-omics analyses of human blood and stool samples, we identified several factors that are associated with COVID-19 vaccine-induced adaptive immune responses.

Social impact and governance of AI and neurotechnologies.

Advances in artificial intelligence (AI) and brain science are going to have a huge impact on society. While technologies based on those advances can provide enormous social benefits, adoption of new technologies poses various risks. This article first reviews the co-evolution of AI and brain science and the benefits of brain-inspired AI in sustainability, healthcare, and scientific discoveries.

Toxicity Analysis of Pentachlorophenol Data with a Bioinformatics Tool Set.

Rapid progress in technologies opened the new era of computer-leaded analytics, leaving humans more space for experimental design and decision making. Here we demonstrate the machine learning analysis workflow represented by spectral clustering, elucidation of evolutionary conserved transcription regulation, and network analysis using reverse engineering. Analysis of genes induced by the Pentachlorophenol toxic chemical revealed two subnetworks, one orchestrated by Interferon and another by Nuclear receptor factor 2 (NRF2) gene.

The role of the endolithic alga Ostreobium spp. during coral bleaching recovery.

In this study, we explore how the Caribbean coral Orbicella faveolata recovers after bleaching, using fragments from 13 coral colonies exposed to heat stress (32 °C) for ten days. Biological parameters and coral optical properties were monitored during and after the stress. Increases in both, the excitation pressure over photosystem II (Qm) and pigment specific absorption (a*) were observed in the stressed corals, associated with reductions in light absorption at the chlorophyll a red peak (D) and symbiont population density.

Outracing champion Gran Turismo drivers with deep reinforcement learning.

Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block opponents while operating their vehicles at their traction limits. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the non-linear control challenges of real race cars while also encapsulating the complex multi-agent interactions.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources.

Elucidating gene expression adaptation of phylogenetically divergent coral holobionts under heat stress.

As coral reefs struggle to survive under climate change, it is crucial to know whether they have the capacity to withstand changing conditions, particularly increasing seawater temperatures. Thermal tolerance requires the integrative response of the different components of the coral holobiont (coral host, algal photosymbiont, and associated microbiome). Here, using a controlled thermal stress experiment across three divergent Caribbean coral species, we attempt to dissect holobiont member metatranscriptome responses from coral taxa with different sensitivities to heat stress and use phylogenetic ANOVA to study the evolution of gene expression adaptation.

Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease.

Background: Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets.

Effects of maoto (ma-huang-tang) on host lipid mediator and transcriptome signature in influenza virus infection.

Maoto, a traditional kampo medicine, has been clinically prescribed for influenza infection and is reported to relieve symptoms and tissue damage. In this study, we evaluated the effects of maoto as an herbal multi-compound medicine on host responses in a mouse model of influenza infection. On the fifth day of oral administration to mice intranasally infected with influenza virus [A/PR/8/34 (H1N1)], maoto significantly improved survival rate, decreased viral titer, and ameliorated the infection-induced phenotype as compared with control mice.

LimeMap: a comprehensive map of lipid mediator metabolic pathways.

Lipid mediators are major factors in multiple biological functions and are strongly associated with disease. Recent lipidomics approaches have made it possible to analyze multiple metabolites and the associations of individual lipid mediators. Such systematic approaches have enabled us to identify key changes of biological relevance.

SBML Level 3: an extensible format for the exchange and reuse of biological models.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose.

Author Correction: COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A Geometric Clustering Tool (AGCT) to robustly unravel the inner cluster structures of time-series gene expressions.

Systems biology aims at holistically understanding the complexity of biological systems. In particular, nowadays with the broad availability of gene expression measurements, systems biology challenges the deciphering of the genetic cell machinery from them. In order to help researchers, reverse engineer the genetic cell machinery from these noisy datasets, interactive exploratory clustering methods, pipelines and gene clustering tools have to be specifically developed.