Articles tagged with Bioinformatics
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
University of Missouri researchers are helping farmers prevent disease outbreaks by teaching biosecurity practices, such as hand sanitizing and wearing farm-dedicated shoes. They also provide guidance on safe composting methods to dispose of dead livestock, reducing the risk of disease spread.
AI accelerates pathogen evolution prediction and enables new target discovery, facilitating efficient compound design and optimization. This technology compresses the development cycle by focusing on key applications like phenotype-driven target identification and validation.
A new large language model, LassoESM, has been developed to predict lasso peptide properties, enabling the acceleration of rational design for biomedical applications. The model was trained on thousands of lasso peptide sequences and demonstrated accurate prediction of various properties.
A new project aims to enhance workforce readiness in molecular bioscience by creating open-access resources and modules tailored to student needs. The Molecular Data Education Hub will host instructional materials and case studies for instructors to implement into their courses.
Researchers from Ateneo de Manila University have identified key proteins produced by Helicobacter pylori that can trigger a strong immune response. By analyzing these proteins using immunoinformatics, the team has pinpointed potential vaccine targets to prevent stomach ulcers and cancer.
The bregr R package streamlines the process of analyzing large-scale biomedical data by automating model fitting, extracting tidy results, and providing publication-quality visualizations. This results in enhanced efficiency and scalability for researchers, while maintaining reproducibility and accuracy.
The E2E R package offers a comprehensive framework for ensemble learning, combining multiple base models to enhance prediction accuracy and robustness. It achieved excellent performance on imbalanced datasets, outperforming mature algorithms in Python.
The University of Texas at Arlington is hosting its fifth annual Texas Health Informatics Alliance Conference, which will delve into the ethical and effective applications of artificial intelligence in healthcare. Keynote speakers and panel discussions will explore topics such as AI security, cybersecurity, and legislative outcomes.
Researchers found that children with stable gut microbiomes tend to have better growth outcomes. The study created the first-ever pediatric undernutrition microbial genome catalog, which can predict and prevent malnutrition. This discovery opens the door to new diagnostics and therapeutics for addressing global child stunting issues.
The Gabriella Miller Kids First Pediatric Research Program has released its 36th study, introducing significant new data updates to two existing studies. These advances aim to uncover the genetic foundations of childhood cancers and congenital conditions. With over 110,000 data files available, researchers can explore publicly accessib...
Researchers found that combining arbuscular mycorrhizal fungi with biochar can reshape soil microbiomes, reduce cadmium uptake, and improve plant growth. The treatment resulted in up to 320% greater shoot biomass compared to untreated controls.
A big data study using multi-omics data from over 1,300 people with HIV has identified key molecular players causing non-AIDS-related comorbidities. The research reveals a range of previously hidden molecular patterns and players associated with various comorbidities.
Researchers used gene expression data to identify genes related to stress responses in wild varieties, which could be beneficial for developing hardier crops. Domesticated species showed genes related to hormone regulation and chemical detoxification.
Researchers developed a machine learning model that accounts for biological variability to identify optimal formulations for serum-free culture media. The model achieved approximately 1.6-fold higher cell density compared to commercially available products.
Researchers develop AI-based model for structural analysis of antibodies, offering precision in less time. The S2ALM model integrates sequence and structure, predicting missing information with high accuracy.
Researchers have published near-complete reference genomes of the central bearded dragon, a species where sex is influenced by both genetics and environmental factors. The assembled genomes revealed key genes, including Amh and Amhr2, that play a crucial role in male sexual differentiation.
Researchers at the University of Graz and the University of California, San Diego have developed a novel method to determine omega positions of lipids in complex biological samples. This breakthrough enables the study of biological mechanisms in unprecedented detail, particularly for inflammation-related diseases.
HTGAnalyzer is an automated tool simplifying complex transcriptomic workflows, enabling clinicians without bioinformatics expertise to perform essential analyses in precision medicine. The tool has been validated using multiple datasets and identified differentially expressed genes linked to cancer diagnosis, treatment, and prognosis.
The THER web tool analyzes hypoxia-associated transcriptomic data without programming skills, presenting results via volcano plots, heatmaps, and tables. Experimental validation confirms THER's reliability, revealing broad impacts of hypoxia on drug efficacy across tumor types.
The CPADS web tool integrates data from GEO, TCGA, and GDSC databases to analyze gene expression changes, correlations between genes or drugs, and pathway enrichment in cancer samples. L1CAM was identified as a potential drug resistance target in non-small cell lung cancer through CPADS analysis.