Articles tagged with Bioinformatics
The Bioteque computational tool integrates vast amounts of biological data, providing insights into gene functions, protein interactions, and disease connections. By analyzing over 30 million functional interactions, the tool can predict drug sensitivity and accelerate biomedical research.
Researchers analyzed proteins from eight species and found that low-complexity regions (LCRs) share similar roles across species, helping proteins join larger-scale assemblies. They also discovered species-specific LCR sequences corresponding to unique functions, such as forming plant cell walls.
Researchers from Hiroshima University have identified two types of genes that are upregulated in both rice and thale cress when exposed to oxygen deprivation. The study suggests distinct molecular mechanisms under hypoxia in plants and animals, with potential applications for creating flood-tolerant crops.
A research team analyzed germ cells in chickens using RNA-sequencing to predict the mechanism that determines sex. The study reveals a mix of cell-autonomous and somatic-cell regulation, with sex determination occurring earlier in females than males.
A team from Ruhr-Universität Bochum developed a novel neural network that can classify tissue samples as containing tumors or not. The AI also generates an activation map showing where the tumor is detected, based on falsifiable hypotheses.
Researchers found 87 genes affected by deletions or short insertions in the woolly mammoth genome, potentially impacting its adaptability to cold environments. These changes may have contributed to unique traits such as fur growth and hair shape.
A new study at the University of Missouri found that prenatal opioid exposure can trigger long-term neurological or behavioral effects in children. The research suggests that opioids impact the developing fetus' gut microbiome, leading to changes in metabolic pathways and potentially affecting overall health and wellness.
Researchers have developed an algorithm to identify the protozoan Trypanosoma cruzi in photographs of blood samples taken with a mobile phone camera. The low-cost method can be reproduced and adapted for other purposes that depend on images, such as analyzing feces, skin, or colposcopies.
Researchers have developed Fanflow4Insects, a functional annotation workflow that annotates gene functions in insects. The team has annotated the Japanese stick insect and silkworm, providing valuable information for entomological research and genome editing.
Researchers developed a new machine learning method called MotifBoost that can predict infections based on limited T-cell receptor data. By focusing on short sequences of amino acids in the receptors, the approach achieved more accurate results with smaller datasets, shedding light on the human immune system's recognition of germs.
Researchers developed a mathematical model to predict the efficiency of nanoparticle delivery into cells, particularly in stem cells. They found that nanoparticles become trapped in bubble-like vesicles, preventing them from reaching their targets.
The Mount Sinai Hospital has been awarded $4.2 million over five years to establish a Proteogenomic Data Analysis Center, which will help identify potential biomarkers and drug targets for cancer and new insights into cancer biology.
A UC Riverside study reports that switching from combustible cigarettes to electronic cigarettes does not restore the nasal epithelium to that of a non-smoker. The researchers found alterations in gene expression profiles associated with cigarette smokers, indicating potential harm from prolonged EC use.
Rensselaer researchers will use a five-year grant to develop novel inhibitors of the SARS-CoV-2 virus's CLpro and PLpro proteases. The team aims to create an orally bioavailable drug that can be administered at home, with the potential for improved antiviral activity when combined with other drugs like remdesivir.
A team of scientists has developed a novel computational approach to analyze DNA sequences of thousands of bacteria, revealing previously unknown gene clusters responsible for producing metabolites of interest. The study highlights the potential of these bacterial compounds in treating colon cancer and improving treatment options.
A new machine learning algorithm called 'ikarus' has found a gene signature characteristic of tumors, distinguishing between healthy and tumor cells in various types of cancer. The algorithm was trained on single-cell sequencing data sets and demonstrated an extraordinarily high success rate in distinguishing between different cell types.
Researchers developed a machine learning model to predict NAFLD development based on gut microbiome data, showing 90% of subjects who developed the disease had subtle differences in their samples. The model combines easily measurable information from blood and microbiome data with high accuracy.
Researchers developed an algorithm to classify patients into disease subtypes based on molecular changes, enabling more targeted treatments. The tool has shown potential in a study on non-alcoholic fatty liver disease, identifying lipid biomarkers for disease progression.
A new computer-aided diagnostic tool, Deep-Lung Parenchyma-Enhancing (DLPE), uses artificial intelligence to reveal signs of pulmonary fibrosis in COVID long-haulers, helping to explain respiratory symptoms and improve disease management.
Researchers at Gwangju Institute of Science and Technology have developed a new bioinformatics pipeline, CRESSP, to investigate the mechanism underlying autoimmune diseases following SARS-CoV-2 infection. The tool identified potential epitopes responsible for COVID-related autoimmune diseases and predicted cross-reactive epitopes of di...
Researchers developed an algorithm to quickly identify two cancer drugs that work well together, reducing testing time from three years to eight weeks. The new method uses gene expression data to prioritize potential combinations and has been confirmed in lab tests.
A recent study found that boosting the pink pigeon's numbers is not sufficient to prevent its extinction in the future. The species has a high genetic load of bad mutations, which puts it at risk of collapse if conservation efforts do not continue.
Researchers have developed a refined tool to identify potentially druggable gene amplifications in tumors using epigenetic data. The new methodology improves the characterization of somatic copy number alterations up to 20% with high accuracy, reaching 100% specificity and 93.3% sensitivity.
A team of researchers used a CRISPR-based approach to study the evolution of lung cancer cells, tracking their history from the first activation of cancer-causing mutations. The study revealed significant diversity between subpopulations of cells within the same tumor, with certain groups becoming more fit and aggressive over time.
Researchers developed a new software tool, called 'meta-transcriptome detector,' that integrates genetic sequencing analysis of hosts and their microbiomes. The tool enables analysis of gene expression activity in both microbes and hosts simultaneously, allowing researchers to spot relationships between them.
Researchers have identified potential biomarkers in lupus nephritis through large-scale protein analysis, which may replace invasive tests. The study's findings could lead to more accurate diagnosis and treatment of the disease.
The study found that T-cell immunity was effective against Omicron variants in approximately 90% of vaccinated Europeans. The researchers identified a specific HLA-DRB1*03:01 variant that allowed the virus to evade immune recognition, particularly affecting individuals with this genetic variation.
A team of researchers developed a prognostic model using automated brain scans and machine learning to predict survival and recovery in severe TBI patients. The model accurately predicted risk of death and unfavorable outcomes at six months after the injury, potentially improving clinical decision-making and patient care.
A recent study by Japanese researchers found that Staphylococcus argenteus, a bacterial organism causing food poisoning, was present in 13.9% of tested chicken samples. The study also revealed cross-contamination among retail foods and slaughterhouses, suggesting a potential link between the two.
Researchers developed a bioinformatics platform called REFLECT to predict optimal combination therapies based on co-occurring tumor alterations. The tool selected combinations that resulted in improved patient outcomes across pre-clinical and clinical studies, leading to increased durable responses.
Researchers found that mutation frequencies in mitochondrial DNA of developing egg cells are lower and increase less with age compared to non-reproductive cells. This suggests a protective mechanism may keep reproductive cells relatively stable, potentially related to human propensity to reproduce at later ages.
A recent study identified two main subtypes of tuberculosis based on immune response to the infection. One subtype has a better prognosis and could improve treatment options, while another has a higher risk of treatment failure and death.
The SAVE program, a NIH initiative, provides real-time risk assessment of SARS-CoV-2 variants on immune protection. It uses a coordinated approach to identify and curate data about these variants, their impact on immunity, and their effects on vaccine protection.
Autophagy is induced when ER-bound ribosomes are stalled, rescuing them from cellular harm. The ERC grant aims to decipher the role of Autophagy in rescuing stalled ER-bound ribosomes.
The African BioGenome Project aims to sequence the genomes of Africa's endemic plants, animals, fungi, and protists. This will build resilience in breeding, sustainable food systems, and biodiversity conservation, aligning with the post-2020 Global Biodiversity Framework and the United Nations Sustainable Development Goals.
A team of researchers found that evolution has an overwhelming preference for simple algorithms, leading to the emergence of highly symmetric structures in biology. This is because simple recipes are more efficient and easier to follow than complex ones, resulting in a higher probability of producing symmetrical outcomes.
Researchers created a comprehensive genomic regulatory map of a 24-hour-old zebrafish embryo, identifying millions of regulatory segments that control gene transcription. The study used single-cell technologies and machine learning algorithms to analyze genome data from over 23,000 nuclei.
A recent study reveals that parthenogenesis, a type of asexual reproduction, leads to a loss of efficiency in selecting beneficial mutations, reduced genetic diversity, and decreased heterozygosity in stick insects. This has significant implications for species' ability to adapt to changing environments.
University of Illinois researchers found a key gene responsible for increasing soybean protein content by approximately 2%. The discovery could lead to significant increases in protein production, addressing global food security issues. However, the gene's function is unclear and may involve the plant's circadian machinery.
Researchers developed an AI system called GestaltMatcher that uses facial characteristics to detect rare diseases with high accuracy. The system considers similarities between patients and can even suggest diagnoses for previously unknown diseases.
A recent study by KU Leuven and The Francis Crick Institute analyzed 2,658 tumor samples and found that 21% exhibited double mutations in both maternal and paternal DNA copies. This discovery sheds new light on the origin of mutations and their role in cancer development.
Researchers have developed a machine learning framework called dynamo that can predict a cell's path over time, including its fate and genetic changes. The tool uses data from individual cells to create mathematical equations describing the cell's trajectory.
A study by Michigan Medicine researchers found that people with diabetes have more macrophages in their fat tissue, particularly the anti-inflammatory CD206+ subtype. This subtype is unique in terms of gene expression and may play a role in the development of diabetes.
Researchers have identified a potential novel mechanism of action against drug-resistant tuberculosis (TB) caused by mutations in the EmbR protein, which binds to transmembrane kinase PknH. This study proposes a new approach for computer-aided drug discovery and has important implications for treating other critical diseases.
Researchers discovered a small fraction of diatom cells participate in sexual reproduction, while others block growth and reduce nutrient absorption. This phenomenon occurs in nutrient-rich conditions, representing a paradox for microalgae that usually compete for resources.
Two independent teams of researchers have found the molecular mechanism of action of praziquantel, the only approved drug to treat schistosomiasis. The drug binds to a specific type of channel in the cell membrane, causing calcium ions influx and resulting in worm paralysis and death.
Researchers from Hiroshima University analyzed 839 collections of genetic material to identify common features of oxidative stress. They found two genes, CRIP1 and CRIP3, downregulated during oxidative stress, suggesting a relationship with zinc homeostasis. The study also identified shared biological processes between the two types of...
A research team developed an AI framework that analyzes protein interactions to predict effective and low-toxicity cancer drug combinations. The framework, GraphSynergy, outperforms conventional models in identifying synergistic combinations.
Researchers have developed a new AI technique, MuSIC, which combines microscopy, biochemistry techniques and artificial intelligence to reveal approximately 70 components contained within a human kidney cell line, half of which had never been seen before.
Scientists sequenced Plasmodium falciparum genomes to understand malaria parasite populations and track drug resistance biomarkers. The study identified population-specific SNPs that can be used to determine transmission routes and monitor drug resistance.
Researchers at Duke University used new tools to monitor neurons and analyze machine learning data to see how zebra finches practice their courtship calls. They found that a neurotransmitter called noradrenaline shuts down variability in the song, making it more precise when performed under pressure.
Researchers create maps of senescence in heart and lung cells, comparing different types of senescent cells across the lifespan. The goal is to understand how senescent cells contribute to age-related diseases and develop therapies called senolytics.
Researchers used AI to predict Michaelis constants from substrate and enzyme structures, achieving accuracy comparable to experimental values. This breakthrough enables computers to estimate enzyme activity in seconds without experimentation.
Researchers developed a new protein treatment that prevents glaucoma from forming in mice and reduces pressure in the eyes. The study provides new targets for therapies and aims to develop an injectable treatment for patients.
A new research center will use genomic data and socioeconomic factors to better predict health outcomes in individuals of diverse ancestry. The center aims to develop computational tools to combine large datasets and analyze them for consistent relationships among admixed populations.
Researchers found that wild and hatchery coho salmon demonstrate different genetic markers for negative assortment, a common finding in mating. They are now trying to emulate the natural mating of coho salmon in a hatchery environment using genetic profile information.
The UTHealth-CPRIT Innovation in Cancer Prevention Research Training Program aims to increase cancer prevention scientists from underrepresented minority groups. The program provides a systematic method to teach career skills, problem-solving skills, team science, and transdisciplinary communication.
Researchers at TU Wien propose a new method to interpret and mine fungal genomes to predict essential genes for the production of valuable substances. The FunOrder method identifies co-evolved genes that are functionally necessary, distinguishing them from gap genes.
The BiCIKL project links biodiversity data types and classes across 14 European institutions, creating a Biodiversity Knowledge Hub for seamless access. This initiative streamlines Open Science and FAIR practices, empowering researchers with efficient tools to explore and utilize biodiversity data.
A novel mortality risk prediction method helps tailor treatment decisions and transplant needs for patients based on individual symptoms. The new tool uses a random survival forest algorithm to predict individual mortality risk curves, calculate mortality at any given time, and provide a 95% confidence interval.