Biological machines

Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treatment of type 2 diabetes, obesity and their associated complications. Candidates must have high potency at both receptors, but it is unclear whether the limited experimental data available can be used to train models that accurately predict the activity at both receptors of new peptide variants. Here we use peptide sequence data labelled with in vitro potency at human GCGR and GLP-1R to train several models, including a deep multi-task neural-network model using multiple loss optimization...

Nanomaterials hold great promise for improving our society, and it is crucial to understand their effects on biological systems in order to enhance their properties and ensure their safety. However, the lack of consistency in experimental reporting, the absence of universally accepted machine-readable metadata standards, and the challenge of combining such standards hamper the reusability of previously produced data for risk assessment. Fortunately, the research community has responded to these challenges by developing minimum reporting standards that address several of these issues...

Today, more than 70 carbon pricing schemes have been implemented around the globe, but their contributions to emissions reductions remains a subject of heated debate in science and policy. Here we assess the effectiveness of carbon pricing in reducing emissions using a rigorous, machine-learning assisted systematic review and meta-analysis. Based on 483 effect sizes extracted from 80 causal ex-post evaluations across 21 carbon pricing schemes, we find that introducing a carbon price has yielded immediate and substantial emission reductions for at least 17 of these policies, despite the low level of prices in most instances...

Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechanics (QM) methods in crystallographic refinement, showed promise in improving the structural quality or even correcting the structure of biomacromolecules. However, vast computational costs and complex quantum mechanics/molecular mechanics (QM/MM) setups limit QR applications. Here we incorporate robust machine learning potentials (MLPs) in multiscale ONIOM(QM:MM) schemes to describe the core parts (e...

Prefiltration before chromatographic analysis is critical in the monitoring of environmental micropollutants (MPs). However, in an aqueous matrix, such monitoring often leads to out-of-specification results owing to the loss of MPs on syringe filters. Therefore, this study investigated the loss of seventy MPs on eight different syringe filters by employing Random Forest, a machine learning algorithm. The results indicate that the loss of MPs during filtration is filter specific, with glass microfiber and polytetrafluoroethylene filters being the most effective (< 20%) compared with nylon (> 90%) and others (regenerated-cellulose, polyethersulfone, polyvinylidene difluoride, cellulose acetate, and polypropylene)...

Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations...

The number of mitotic cells is an important indicator of grading invasive breast cancer. It is very challenging for pathologists to identify and count mitotic cells in pathological sections with naked eyes under the microscope. Therefore, many computational models for the automatic identification of mitotic cells based on machine learning, especially deep learning, have been proposed. However, converging to the local optimal solution is one of the main problems in model training. In this paper, we proposed a novel multilevel iterative training strategy to address the problem...

Mutagenicity assessment plays a pivotal role in the safety evaluation of chemicals, pharmaceuticals, and environmental compounds. In recent years, the development of robust computational models for predicting chemical mutagenicity has gained significant attention, driven by the need for efficient and cost-effective toxicity assessments. In this paper, we proposed AMPred-CNN, an innovative Ames mutagenicity prediction model based on Convolutional Neural Networks (CNNs), uniquely employing molecular structures as images to leverage CNNs' powerful feature extraction capabilities...

The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis...

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression...

Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey reovirus, tentatively named turkey arthritis reovirus (TARV) and turkey hepatitis reovirus (THRV), are linked to tenosynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis reoviruses are causing significant economic losses to the turkey industry...

Gastric cancer is a prevalent and deadly malignancy, and the response to immunotherapy varies among patients. This study aimed to develop a prognostic model for gastric cancer patients and investigate immune escape mechanisms using deep machine learning and single-cell sequencing analysis. Data from public databases were analysed, and a prediction model was constructed using 101 algorithms. The high-AIDPS group, characterized by increased AIDPS expression, exhibited worse survival, genomic variations and immune cell infiltration...

Accurate prediction of a new compound's pharmacokinetic (PK) profile is pivotal for the success of drug discovery programs. An initial assessment of PK in preclinical species and humans is typically performed through allometric scaling and mathematical modeling. These methods use parameters estimated from in vitro or in vivo experiments, which although helpful for an initial estimation, require extensive animal experiments. Furthermore, mathematical models are limited by the mechanistic underpinning of the drugs' absorption, distribution, metabolism, and elimination (ADME) which are largely unknown in the early stages of drug discovery...

This study aims to enhance the prognosis prediction of Head and Neck Squamous Cell Carcinoma (HNSCC) by employing artificial intelligence (AI) to analyse CDKN2A gene expression from pathology images, directly correlating with patient outcomes. Our approach introduces a novel AI-driven pathomics framework, delineating a more precise relationship between CDKN2A expression and survival rates compared to previous studies. Utilizing 475 HNSCC cases from the TCGA database, we stratified patients into high-risk and low-risk groups based on CDKN2A expression thresholds...

BACKGROUND: Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics. METHODS: We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates...

BACKGROUND: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have emerged as a valuable advancement, providing more accurate predictions and unveiling new engineering targets compared to models lacking enzyme constraints. In 2022, a stoichiometric GEM, iDL1450, was reconstructed for the industrially significant fungus Myceliophthora thermophila...

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network...

Accurate diagnosis and treatment assessment of liver fibrosis face significant challenges, including inherent limitations in current techniques like sampling errors and inter-observer variability. Addressing this, our study introduces a novel machine learning (ML) framework, which integrates Light Gradient Boosting Machine (LightGBM) and Multivariate Imputation by Chained Equations (MICE) to enhance liver status assessment using biomechanical markers. Building upon our previously established multiscale mechanical characteristics in fibrotic and treated livers, this framework employs Gaussian Bayesian optimization for post-imputation, significantly improving classification performance...

As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown pathogenesis. This is due to their similar clinical presentations and imaging manifestations. That misdiagnosis can significantly increase the occurrence of adverse events. Delayed or incorrect treatment is one of the most important causes of MS progression. Therefore, the development of a practical diagnostic imaging aid could significantly reduce the risk of misdiagnosis and improve patient prognosis...

Analyzing the environmental factors affecting benthic communities in coastal areas is crucial for uncovering key factors that require conservation action. Here, we collected benthic and environmental (physical-chemical-historical and land-based) data for 433 transects in Taiwan. Using a k-means approach, five communities dominated by crustose coralline algae, turfs, stony corals, digitate, or bushy octocorals were first delineated. Conditional random forest models then identified physical, chemical, and land-based factors (e...