Articles tagged with Bioinformatics
A new report by Frontier Economics reveals that EMBL-EBI's open data resources support growing numbers of scientists and innovators worldwide, driving £11.8 billion in annual productivity gains. The report highlights the critical role of these resources in enabling breakthroughs across science, medicine, and biotechnology.
A new study by Kyota Yasuda found a strong correlation between RBP diversity and neuronal count in six model organisms, suggesting that post-transcriptional regulation is a key factor in nervous system complexity. RBP diversity increased from 397 families in nematode worms to 469 in humans, correlating with enhanced neural complexity.
Researchers developed genESOM, a generative AI that can expand dataset volume and simulate larger animal numbers while maintaining reliability. This allows for 30-50% reduction in animal experiments without compromising results.
The GlycoHBF dataset maps protein and glycosylation landscapes across 15 human body fluids, providing a crucial reference framework for research. The study establishes the baseline molecular profiles of healthy and non-malignant body fluids, enabling differentiation between normal physiological variation and pathological changes.
A new mathematical model called LFSPRO was developed to predict the risk of Li-Fraumeni Syndrome. The model provides a more quantitative risk estimate for individuals who would benefit from testing but do not meet established criteria.
A massive study of ancient DNA from nearly 16,000 people across over 10,000 years in West Eurasia reveals that natural selection has shaped modern human genomes more than previously thought. Many gene variants linked to health and complex traits have been selected since farming began.
PhytoCell, an ensemble learning framework, analyzes plant single-cell RNA sequencing data to identify marker genes and assign cell types accurately. The framework achieves precise annotation of cell subpopulations and effectively removes redundant noise.
Researchers at Hiroshima University have developed a new approach to predicting harmful algal blooms by coupling three models and accounting for plankton species interactions. This improved forecasting can help prevent economic losses and protect fish stocks in countries like Chile, which has been hit hard by these blooms.
Researchers propose tailored approaches using CAR platforms, incorporating effector cells like macrophages and Tregs to modulate key processes in neurodegeneration. High-precision immunomodulation is essential for overcoming the complex nature of these diseases.
HSE researchers train a neural network, GSMFormer-PPI, to predict protein–protein interactions by integrating three types of data: sequence, structure, and surface properties. The model achieves 95.7% accuracy, outperforming popular graph-based models.
A team from Emory University developed a simple method to test the accuracy of protein language models, which are used to analyze complex biological data. By comparing how these models 'embed' natural proteins versus synthetic ones, researchers can estimate their reliability and improve their performance.
Researchers developed an interpretable machine learning algorithm, scOMM, to classify cell types consistently across different single-cell methods. The integration strategies and scOMM establish a robust approach for cell atlas generation in complex tissues, leading to the discovery of previously undetected rare cell types.
Researchers at Hiroshima University developed a new tool to quickly and accurately map fungal gene functions, even for species with no reference genomes. The tool successfully annotated over 96% of protein-coding transcripts, providing high-resolution functional detection in diverse fungal lifestyles.
A newly developed AI tool called EvORanker analyzes genetic patterns across over 1,000 species to identify the cause of rare diseases. In clinical testing, it successfully identified the disease-causing gene in nearly 70% of cases, offering new hope for treatment and closure.
Researchers developed an AI tool called PathogenFinder2 that can detect harmful bacteria before they infect humans. The tool uses protein language models and has been shown to significantly improve the detection of bacterial threats.
A single-celled predator, Rapaza viridis, retains chloroplasts from prey algae and imports host-made proteins into them, revealing deeper levels of host–organelle integration. This process may have played a role in the emergence of plant cells.
A computational method called scSurv links individual cells to patient outcomes using bulk RNA sequencing data, identifying cell populations associated with survival across several cancers. The model estimates the contributions of over 10,000 individual cells to disease risk and prognosis, providing a foundation for precision medicine.
Researchers develop MS-based glycoRNA analytical pipeline for precise structural characterization. Glycan abundance patterns reveal distinctive physiological and pathological states, serving as potential biomarkers.
A new tool, metapipeline-DNA, automates and standardizes genome sequencing analysis, reducing the complexity of large and complicated data. The open-access resource, developed by Sanford Burnham Prebys and the University of California Los Angeles, aims to improve collaboration and reproducibility across research labs.
A study published in American Journal of Physiology-Endocrinology and Metabolism found that low testosterone and high fructose intake synergistically contribute to liver damage in mice. The researchers discovered that changes in gut microbiota led to increased levels of pyruvate, which promotes fat accumulation in the liver.
Researchers developed a new computational approach to predict chemotherapy response in triple-negative breast cancer, outperforming current methods. The TmS biomarker accurately sorts patients into those with favorable or poor prognosis, highlighting its potential as an effective starting point for patient stratification.
A University of Houston professor has found that tree-like thin films release heat at least three times better than traditional methods, enabling more efficient cooling in AI data centers. The discovery demonstrates the power of physics-aware AI design for validating high-impact cooling solutions.
Pentachlorophenol exposure triggers inflammatory responses and oxidative stress, leading to liver damage and compromised immune function. The study identified the Toll-like receptor signaling pathway as a key mechanism of PCP-induced immunotoxicity.
A recent systematic review has compiled and catalogued publicly available omics data on pituitary tumours, highlighting the need for standardisation and clinical annotation. The resulting catalogue facilitates the reuse of data for future research projects and precision medicine initiatives.
A study published in Science of The Total Environment found that e-cigarette exposure alters gut microbiota composition and affects neurobehavior in zebrafish. The researchers observed disruptions in the gut microbiome, with reduced microbial network stability and altered community composition, suggesting potential health risks.
The Mental Health Goals programme aims to transform mental health research infrastructure with a £50 million government-funded initiative. King's College London, Cardiff University, and University of Oxford will co-lead two workstreams to develop the world's largest dataset for depression and improve links between research and industry.
A new study using AI-powered BioBERT model accurately identifies fungal lifestyles, switching between helpful partner for plants to aggressive decomposers. The tool has nearly 90% accuracy and can scan thousands of papers in minutes, flagging species that may switch roles.
FAIRification is made possible through a step-by-step approach using the DZD basic data set as an example. The study provides concrete recommendations for the scientific community, facilitating faster comparisons, new findings, and efficient use of existing resources.
Researchers at the University of Missouri are developing AI models to accurately detect melanoma by analyzing images of skin abnormalities. The technology can help dermatologists identify cases that may require closer attention, leading to earlier treatment and improved health outcomes.
Researchers at John Innes Centre and Earlham Institute developed a powerful single-cell visualisation technique to understand wheat spike development. The study reveals distinct expression patterns across spikes, shedding light on why basal spikelets fail to achieve full size.
Researchers found identical bacterial strains in the mouth and gut of patients with advanced chronic liver disease, suggesting oral bacteria colonize the gut. These bacteria can damage the intestinal barrier, compromising gut health.
Dr. Jeremy Horowitz has been selected for The Oceanography Society Early Career Award for his contributions to advancing black coral taxonomy, including new species and families. His work combines classical morphological taxonomy with phylogenomics and bioinformatics to describe new taxa and reconstruct evolutionary histories.
Researchers have decoded the logic of microRNA strand selection using AI, revealing a conserved and programmable mechanism governing gene regulation. The study found that this decision follows conserved rules rather than chance, with mammalian microRNAs showing a strong bias towards a single dominant strand.
A machine-learning-based algorithm developed by Tokyo Metropolitan University researchers can accurately count sister chromatid exchanges (SCEs) in chromosomes, giving a more objective measurement. The accuracy rate is 84%, which could help diagnose disorders like Bloom syndrome with greater consistency.
The study created a critical framework for understanding the architecture of the genome and its association with gene function in cells. The 4DN Consortium integrated data from over a dozen techniques to compile an extensive catalogue of looping interactions between genes and regulatory elements.
Researchers found that male fish exposed to vitamin C and potassium perchlorate showed improved fertility and less damage to their testes compared to those exposed only to the chemical. The study suggests a potential safeguard for individuals regularly exposed to these chemicals, including military personnel.
A new pipeline, NLRSeek, identifies previously overlooked NLR genes in sequenced genomes, revealing functional genes and driving evolutionary expansion in non-model species. This tool enhances disease resistance breeding by providing a more complete set of NLRs.
A team of HSE researchers has created a comprehensive map of quadruplexes, unstable DNA structures involved in gene regulation. The study reveals that quadruplexes function in pairs, regulating tissue-specific genes in healthy tissues and cell growth and division in cancerous tissues.
Researchers developed RNACOREX, a new open-source software tool that identifies gene regulation networks in cancer. The tool analyzes thousands of molecules simultaneously to detect key interactions, providing an interpretable molecular map that improves understanding of tumors.
Researchers analyzed DNA captured on air filters since the 1960s to track changes in ecosystem biodiversity. The study found a clear decline in biodiversity from the 1970s to the early 2000s, linked to human activities such as forest management.
Researchers have launched a new multimodal medical dataset, Observer, capturing anonymized, real-time interactions between patients and clinicians. The dataset links video, audio, transcripts, and electronic health records to study subtleties like body language and environmental factors affecting care.
Researchers have identified 32 causal genes that increase the likelihood of developing long COVID, including 13 new genes not previously associated with the disease. The study's findings could lead to more precise diagnoses and treatment options for the condition, which affects an estimated 400 million people worldwide.
A study analyzed large-scale human ChIP-seq data to identify unmeasured transcription factor-tissue/cell type pairs, revealing significant gaps in current knowledge. These findings indicate that essential regulatory mechanisms may have been overlooked, emphasizing the need for strategic prioritization of measurement targets.
A multidisciplinary team developed a computational pipeline called iSPy to quantify ploidy across tissues from microscopy images. This allows scientists to visualize and study polyploid cells directly in intact, living tissue, revealing spatial patterns of DNA content.
Machine learning (ML) streamlines nanomedicine development by predicting nanoparticle parameters and optimizing drug release profiles. ML models can personalize therapeutic regimens for individual patients, addressing longstanding challenges in cancer therapy.
A recent study found that potassium sorbate, a widely used food preservative, can induce acute kidney injury by disrupting cell-to-cell communication. The research identified amyloid precursor protein as a key regulator of this process.
An international team led by Lehigh University researcher Hannah Dailey is building predictive models to understand and eventually prevent bone healing complications. The team aims to incorporate biological differences into the model, using a library of imaging data from Switzerland's AO Research Institute Davos.
A new computational tool called DeepTarget predicts direct and indirect targets of cancer drugs, revealing that small molecules can have different targets and effects depending on the disease and cell type. The study demonstrates the tool's superior performance in real-world scenarios, highlighting its potential to accelerate drug deve...
Researchers identified a genetic marker, PAV284, associated with increased salt tolerance in sorghum seedlings. The SbTEF1 gene plays a role in regulating root growth under saline conditions.
Researchers developed a 'humanized' model of aortic valve calcification, enabling testing of potential treatments. The breakthrough could lead to new therapies to stop or reverse calcium buildup, improving outcomes for people with heart disease.
A new study published in iMetaMed uses single-cell Raman spectroscopy to track biochemical changes in mouse oocytes as they age, revealing a decline in metabolic maturity. The research suggests that mitochondrial dysfunction plays a key role in the age-related decline of egg cell quality.
Malignant mesothelioma of the tunica vaginalis testis has a global incidence of 0.54-0.95 per 10 million person-years, with asbestos exposure being a major risk factor. The expert consensus aims to shift clinical practice from empirical treatment to evidence-based, personalized care for patients with this rare disease.
The Gabriella Miller Kids First Data Resource Center has released its 37th study on extracranial germ cell tumors, a rare group of childhood cancers. The dataset comprises information from 393 children and young adults, including inherited genetic data and tumor-specific changes.
A new resource identifies genetic variants associated with elevated 'bad' cholesterol, a major contributor to heart disease. Clinicians can now predict patient risk for heart attacks and strokes, allowing for prevention and early treatment.
SeqHub enables rapid interpretation of proteins and genomes using AI-driven function prediction, context, and structure. The platform aims to accelerate discovery in biology by making sequences more searchable, interpretable, and engineerable.
Researchers at the University of Missouri are exploring the use of extracellular vesicles to target lung cancer. By manipulating these tiny messenger particles, scientists can deliver specific instructions to kill cancer cells while sparing healthy ones.
MARTi enables rapid taxonomic classification and abundance analysis of microorganisms in various settings, including agriculture, environmental monitoring, and clinical environments. The tool provides immediate analysis results, allowing for quick identification and targeted treatments of pathogen infections.
Researchers introduce theoretical foundations of HMMs, including evaluation, decoding, and learning algorithms, for applications in bioinformatics such as transmembrane protein prediction and gene finding. HMMs are expected to remain central to advancing genome annotation and functional genomics.
Researchers at HUN-REN Szegedi Biológiai Kutatóközpont have developed an AI-powered platform for automated 3D cell culture analysis, enabling high-precision screening of cellular models. The technology removes the limitation of throughput in personalized medicine, allowing for fast and accurate analysis of clinical samples.