Articles tagged with Bioinformatics
The new approach can make predictions even for new systems and unknown molecules, outperforming previous methods that required extensive training on the target system. The method can be used to identify unknown small molecules in natural product research, environmental analysis, food chemistry, and pharmaceutical research.
A Baylor College of Medicine-led team developed an AI-powered strategy to discover small molecules called 'molecular glues' to treat disease. They found a new class of molecular glues that can potentially neutralize harmful proteins linked to blood cancers and autoimmune diseases.
Researchers developed Metax, a computational tool that accurately identifies and estimates the abundance of microorganisms in complex samples. This improves taxonomic profiling, helping to uncover microbial patterns linked to health and disease.
Researchers found that rare C-to-G mutations occurred at an unexpectedly high frequency during the emergence of the SARS-CoV-2 delta variant, contributing to its increased transmissibility. The study analyzed publicly available genome sequences and found that the delta lineage contained two C-to-G transversions in its spike protein.
Researchers at the Stowers Institute used AlphaFold2 and evolutionary data to predict protein structures in aphids, which were previously inaccessible to AI. The study reveals a common architectural plan among 2,400 BICYCLE proteins, showcasing the evolution's role in helping AI predict protein structures.
A three-herb preparation restored immune function in rats exposed to simulated weightlessness and challenged with bacteria, suggesting a nutritional way to help crew health on long-duration missions. The study also highlights the potential for the formula to support individuals on the ground who experience similar immune declines.
Researchers developed an AI-based method to predict bitterness of peptides, enabling de novo design of bitter peptides for improved flavor control. The method used a combination of a protein language model and an artificial neural network to analyze structural data, resulting in the identification of new bitter-tasting peptides.
Researchers have discovered a new mechanism that helps cells protect genetic information during DNA replication, preventing errors and preserving genome integrity. This discovery could have implications for precision oncology and our understanding of the molecular machinery responsible for copying DNA.
The journal refines its evaluation criteria to emphasize translational impact, external validation, and actionable implementation in healthcare environments. Submissions will be evaluated based on real-world clinical applicability and systematic implementation of informatics solutions.
Researchers identify recurring states of tumor microenvironment that predict response to immunotherapy, finding that early treatment-induced changes can anticipate course of immune response
JMIR Publications and ZB MED extend their Flat-Fee Unlimited Open Access Publishing Agreement for two years, covering over 30 Gold Open Access journals with zero Article Processing Charges (APCs) for participating German research institutions. The new agreement provides predictable and sustainable funding for open access publishing.
Researchers have discovered a significant increase in blood viruses, including Anelloviridae, in gastric cancer patients, providing potential biomarkers for diagnosis. The study found that the virome is compartment-specific, with the gut virome showing increased diversity in cancer patients.
Crop-GPA 2.0 uses hierarchical genomic representations and cross-species pre-training to decode genotype-phenotype associations across crops. The framework outperformed existing methods across multiple prediction tasks and retained robust performance during cross-species transfer.
Researchers have developed the AlphaGenome Atlas, a comprehensive map of more than 9 billion possible single-letter DNA changes. The one-petabyte dataset provides artificial intelligence-generated predictions for the molecular effects of these changes, accelerating understanding of the human genome.
A team of researchers found that SARS-CoV-2's 3CLpro protease can cleave and disable influenza virus's NP and PA proteins, leading to rapid protein degradation and cytoplasmic mislocalization. This mechanism may have contributed to the decline of influenza cases during the COVID-19 pandemic.
Researchers investigated NDUFA11's role in Parkinson's disease, finding it maintains mitochondrial function and integrity. Impaired NDUFA11 function contributes to mitochondrial dysfunction, oxidative stress, and PD-related abnormalities.
Researchers developed a computational model to study aging in 40 types of human tissue, identifying three major aging patterns and underlying molecular changes. Tissues show bimodal structural aging, with accelerated aging from 35-40 and 55-60, coinciding with fertility decline and menopause.
Researchers developed a method to quantify individual cell damage using molecular markers, enabling detailed analysis of disease progression in tissue samples. This method can distinguish early disease mechanisms and differentiate between patient-specific and general disease progression.
Researchers found that pre-existing antibodies influence B-cell responses to influenza vaccination in distinct ways. Individuals with higher baseline antibody levels exhibited increased frequencies of specific B-cell receptor features, while those with lower levels showed greater mutation levels and broader neutralizing activity.
A new study identifies MSLN, TROP-2, and LIV-1 as potential therapeutic targets for cervical cancer, with distinct profiles across tumor subtypes. LIV-1 may represent a precision medicine target for a biologically defined subgroup of cervical cancer patients.
The University of Tennessee Herbert College of Agriculture has introduced a new Bachelor of Science in Bioinformatics program, combining biological sciences and data analytics. Graduates will enter high-demand careers with unique technical skills in precision agriculture and environmental sustainability.
Researchers from USC-led study found that different epigenetic clocks capture distinct aspects of cellular aging, while introducing new gene-expression based clocks with stronger predictive power. These tools can better predict age-related disease and mortality by examining DNA methylation patterns and gene expression.
The first complete marmoset genome is now available, providing a high-quality reference for studying complex diseases like Alzheimer's. The new genome reveals variation in genes linked to Alzheimer's disease, as well as immune system genes and previously un-catalogued ribosomal DNA genes.
The EU project B-Cubed developed automated data pipelines to standardize biodiversity data, creating 'species occurrence cubes' that support consistent indicator reporting. These data cubes enable the derivation of meaningful measures of biodiversity status and trends, allowing for timely responses to dynamic threats.
LorMe is an open-source R framework designed to provide a unified approach for standardized microbiome analysis. The platform enables seamless data exchange with widely used microbiome analysis packages, allowing diverse analytical approaches to work together while maintaining flexibility and transparency.
Technion researchers have identified a previously unknown mechanism that enables bacteria to rapidly develop antibiotic resistance. This non-canonical gene amplification process produces dozens of copies of resistance-conferring genes, allowing bacteria to adapt quickly to antibiotic treatment.
Researchers developed a new computational approach to identify genes that characterize different cellular states from mRNA-seq data, offering more accurate and interpretable analysis of complex biological data. The Cartesian Distance-Based Gene Expression (CDBGE) algorithm was evaluated using multiple publicly available datasets, demon...
BetaDescribe, an AI system, converts protein sequences into detailed textual descriptions of their functions and characteristics. The technology helps bridge the gap between characterized and existing proteins in nature, enabling researchers to rapidly generate evidence-based hypotheses regarding unknown proteins.
Jill Mesirov brings expertise in computational biology and genomics to Sanford Burnham Prebys, aiming to advance cancer research and treatment. She will mentor next-gen computational biologists and leverage AI tools to personalize therapy.
Researchers found that diapause eggs exhibit increased chromatin remodeling, reduced gene expression for neural development, and enhanced metabolism. This study sheds light on the developmental mechanisms underlying insect diapause, a survival strategy for cold temperatures.
A new AI model, SpliceSelectNet, accurately predicts RNA splicing by capturing long-range DNA signals. The model's hierarchical Transformer architecture preserves high computational efficiency while maintaining single-nucleotide resolution, enabling accurate analysis of genomic regions.
Prof. Orly Lewis is developing a flexible publishing platform for interactive knowledge environments, while Prof. Nir Friedman is creating an epigenomic liquid biopsy for early detection and monitoring of Metabolic Dysfunction-Associated Steatohepatitis.
Researchers found PLXNC1 expression is significantly upregulated in CMS4 tumors, serving as an independent risk factor for poor overall survival. Elevated PLXNC1 promotes mesenchymal phenotype, stromal infiltration, and immunosuppressive tumor microenvironment.
JMIR Publications and Jisc have expanded their Flat-Fee Unlimited Open Access Partnership, making APCs free for researchers affiliated with participating institutions. The agreement allows researchers to prioritize open access publishing without financial burdens.
JMIR Publications has extended its agreement with Eindhoven University of Technology for two years, providing researchers with unlimited APC-free publishing in all JMIR's Gold Open Access journals. The partnership eliminates individual Article Processing Charges (APCs) and ensures equity for TU/e researchers.
A study from Hiroshima University identifies enhancer sequences active during worker bee metamorphosis, revealing key genetic mechanisms regulating social caste development in honeybees. The research provides direct evidence of transcription factor binding sites and sheds light on the evolution of honeybee sociality.
PolyGenie facilitates the analysis, exploration, and reuse of genomic data by the research community. The platform demonstrates its capabilities using data from the GCAT cohort, revealing over 200,000 potential associations between genetic risk and health-related characteristics.
The study synthesizes recent advances in single-cell and spatial transcriptomics to identify tumor-enriched cell subsets closely related to prognosis and treatment response. The review introduces the
MDNA, an open-source software suite, enables accurate models of DNA structures and simulations. It facilitates visualization and analysis of DNA-protein interactions, improving understanding of DNA dynamics in complex biological systems.
Shandong University researchers have developed MuSE-Promoter, a deep learning framework that integrates multiple complementary ways of looking at DNA sequences. The method consistently outperforms state-of-the-art tools in challenging cross-cell-line transfer and promoter-enhancer discrimination tasks.
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 team of researchers has developed a deep learning-based method called DeepAFM to analyze noisy atomic force microscopy (AFM) images and infer protein states. The method produces accurate results, even with background noise and scanning distortions.
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.