Biological interpretability is a key requirement for the output of microarray data analysis pipelines. The most used pipeline first identifies a gene signature from the acquired measurements and then uses gene enrichment analysis as a tool for functi...
This study introduces a novel framework that simultaneously addresses the challenges of performance accuracy and result interpretability in transcriptomic-data-based classification. Background/objectives: In biological data classification, it is chal...
Metastasis remains a major cause of cancer mortality, making reliable primary–metastatic state prediction from somatic genomic alterations clinically important yet technically difficult. We present TF-GateNet, a biologically constrained neural...
This paper aims to introduce a developed reading of Roederer’s interpretation of pragmatic information as a good candidate for a Unifying Information Concept required for an as-yet-unavailable Science of Information. According to pragmatic informati...
While metabolomics captures the dynamic chemical landscape of biological systems, its inherent high dimensionality and complexity pose significant analytical hurdles. Interpretable Machine Learning (IML) is revolutionizing the field by moving beyond...
RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional mechanisms are time-consuming and labor-intensive, despite extensive research. Moreover, existing m...
Background: Accurately predicting protein druggability is crucial for successful drug development, as it significantly reduces the time and resources required to identify viable drug targets. However, existing methods often face trade-offs between ac...
Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between...
Traditional plant scouting is often a costly and labor-intensive task that requires experienced specialists to diagnose and manage plant stresses. Artificial intelligence (AI), particularly deep learning and computer vision, offers the potential to t...
The 2018 classification of periodontal and peri-implant diseases introduced a multidimensional diagnostic framework integrating staging, grading, and disease extent, representing a major advance over earlier severity-based systems. By incorporating s...
This review summarizes recent stochastic modeling efforts in the theoretical research aimed at interpreting the origins of biological chirality. Stochastic kinetic models, especially those based on the continuous time discrete state approach, have gr...
Bacterial persister cells are a rare subpopulation that survives antibiotic treatment without acquiring heritable resistance. Despite decades of research, concepts of bacterial persistence have evolved alongside experimental methods, yet the mechanis...
Phytosterols are key minor constituents of olive oil, contributing not only to its nutritional and bioactive properties but also to authenticity assessment, quality control, and regulatory classification. In Greek olive oils, particularly extra virgi...
Nitroreductase (NTR)-responsive systems convert the enzymatic reduction of nitroaromatic substrates into detectable signals, molecular retention, supramolecular assembly, or therapeutic activation. Their interpretation is nevertheless complicated by...
The influence of phosphoproteomics sample preparation methods on the biological interpretation of signaling outcome is unclear. Here, we demonstrate a strong bias in phosphorylation signaling targets uncovered by comparing the phosphoproteomes genera...
Variations in the geochemical signatures of fossil brachiopod shells may be due to diagenesis and/or biological processes (i.e., ‘vital effects’). It is critical to characterise them in order to identify reliable shell areas suitable for...
Accurate deconvolution of bulk transcriptomes is essential for characterizing the breast cancer tumor microenvironment (TME), yet existing reference matrices incompletely capture tumor-specific cellular diversity. Here, we developed breast cancer&nda...
Single-cell measurements incorporate invaluable information regarding the state of each cell and its underlying regulatory mechanisms. The popularity and use of single-cell measurements are constantly growing. Despite the typically large number of co...
Autoimmune diseases arise from complex interactions between genetic susceptibility, immune regulation, and tissue-specific inflammatory processes, yet most risk variants identified by genome-wide association studies occur in non-coding regions with p...
We introduce a generative Bayesian switching dynamical model for action recognition in 3D skeletal data. Our model encodes highly correlated skeletal data into a few sets of low-dimensional switching temporal processes and from there decodes to the m...
Serum free light chains (sFLC) reflect immunoglobulin production and renal clearance, making abnormal results biologically heterogeneous in lymphoma. We reviewed clinical, translational, and analytical evidence using a documented PubMed search. Three...
Background: Lipid peroxidation is a primary driver of biological membrane damage and mediates the relationship between environmental exposure and adverse health outcomes. Malondialdehyde (MDA) is a widely recognized biomarker for quantifying oxidativ...
Genome mining has become an increasingly powerful, scalable, and economically accessible tool for the study of natural product biosynthesis and drug discovery. However, there remain important biological and practical problems that can complicate or o...
Somatic mutation patterns observed in cancer genomes are widely used to generate hypotheses about functional relationships among cancer genes and signaling pathways. However, mutation co-occurrence and mutual exclusivity are assessed at multiple leve...
There are still grey areas in the understanding of the myoarchitecture of the ventricular mass. This is despite the progress of investigation methods since the beginning of the 21st century (diffusion tensor magnetic resonance imaging, microcomputed...
Label-free imaging is becoming ever more important, especially in modern molecular biophysics. This method allows observation of biological structures and dynamics without the alteration caused by dyes or genetic labels. Coherent Anti-Stokes Raman Sc...
Lactate electrochemical biosensors were fabricated using Pediococcus sp lactate oxidase (E.C. 1.1.3.2), an external polyurethane membrane laminate diffusion barrier and an internal ionomeric polymer barrier (sulphonated polyether ether sulphone polye...
Targeted metagenomics is the solution of choice to reveal differential microbial profiles (defined by richness, diversity and composition) as part of case-control studies. It is well documented that each data processing step may have the potential to...
24 September 2026
Background: Accurate mapping of microarray probes to gene loci is essential for a reliable interpretation of gene expression data, yet available probe annotations are often incomplete or outdated. In this study, we aimed to develop and apply an exper...
Gastric Cancer (GC) is characterized by remarkable biological heterogeneity, reflected by the continuous expansion of biomarkers with diagnostic, prognostic, predictive, and therapeutic relevance. However, most reviews organize these biomarkers accor...
Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted...
Reduced nicotinamide adenine dinucleotide phosphate (NADPH) sustains reductive biosynthesis, antioxidant defense, oxidase activity, and redox signaling; nevertheless, studies framed as “NADPH imaging” often interrogate biochemically non-e...
Results obtained with mass spectrometry-based omics depend on every stage of the work, from sample collection to data processing. Quality guidance exists for individual omics fields and for individual stages, but it rarely states when a dataset is re...
Raman spectroscopy has become an important tool for biomedical analysis due to its ability to provide label-free, non-destructive molecular fingerprints of biological samples. However, existing deep learning approaches for classifying biological Rama...
The expanding scope and sensitivity of genomic testing are reshaping hereditary cancer assessment, shifting attention from the detection of genomic variants alone to the more complex task of determining their significance, biological origin, and rele...
The assessment of broiler welfare in intensive production systems is based on a variety of behavioral, health, physiological, and production indicators. Despite the growing amount of available data, its interpretation remains a significant challenge,...
Exercise training is frequently examined in relation to cognitive and brain health in aging populations. Yet many exercise–cognition studies remain difficult to interpret mechanistically because cognitive outcomes are often assessed without suf...
It is now well-established that microRNA dysregulation is a hallmark of human diseases, and that aberrant expression of miRNA is not randomly associated with human pathologies but plays a causal role in the pathological process. Investigations of the...
Background: The high costs, long development timelines, and low clinical success rates in oncology highlight an urgent need for reliable computational strategies for drug repositioning. Current machine learning approaches often integrate heterogeneou...
This paper presents a biologically inspired model, the Stereoscopic Direction Detection Mechanism (SDDM), designed to detect motion direction in three-dimensional space. The model addresses two key challenges: the lack of biological interpretability...
The Systemic–Evolutionary Theory of Cancer (SETOC) is a recently proposed theory based on two important concepts: (i) Evolution, understood as a process of cooperation and symbiosis (Margulian-like), and (ii) The system, in terms of the integra...
The behavior of a cell is strongly influenced by the physical properties and stimuli in its microenvironment. Furthermore, the activation and modulation of mechanotransduction pathways are involved in tissue development and homeostasis and even patho...
Network pharmacology examines drug action across interacting targets, pathways, and disease modules. Increasingly, however, the graph neural networks and knowledge-graph models used for this purpose are difficult to inspect. This critical narrative r...
Background/Objectives: Interpretation of high-dimensional molecular profiling data requires computational strategies that go beyond single-layer analyses to resolve coordinated biological programs. Although multi-omics integration has advanced latent...
Psychoactive substances, including illicit drugs, prescription medications, and novel psychoactive compounds, are frequently analyzed in biological and in non-biological samples. Interpreting results is paramount for ensuring proper medical treatment...
In bioinformatics, pathway analyses are used to interpret biological data by mapping measured molecules with known pathways to discover their functional processes and relationships. Pathway analysis has become an essential tool for interpreting large...
We present LazyNet, a compact one-step neural-ODE model for single-cell CRISPR activation/interference (A/I) that operates directly on two-snapshot (“pre → post”) measurements and yields parameters with clear mechanistic meaning. The...
Deep eutectic systems (DESs) have evolved from sustainable solvent alternatives to promising bioactive platforms with reported antimicrobial, anti-inflammatory, regenerative, cryoprotective, and cytoprotective properties. However, despite the growing...
Metabolomics generates a vast amount of data and heavily relies on data science for biological interpretation [...]
Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visuali...
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