Articles tagged with Genetic Algorithms
Researchers develop a two-step encryption technique that improves complexity and energy efficiency, and has been proven safe for various attacks. The new algorithm simulates genetic coding processes to generate a complex key.
Researchers at Vanderbilt University Medical Center developed a predictive model to identify patients who may benefit from genetic testing based on routine information in electronic health records. The model accurately classified over 87% of cases and 96% of controls, and identified potential patients with rare undiagnosed diseases.
Researchers at Lehigh University and Iowa State University have developed a hybrid Cuckoo Search algorithm that accelerates computational modeling of complex alloys by reducing search time up to 13,000-fold. This breakthrough enables the creation of physically realizable systems that can be directly compared against experimental samples.
A new study recommends strategies for generating polygenic risk scores that inspire confidence among consumers, reducing variability and improving the accuracy of these scores. By running algorithms multiple times and paying attention to variable elements, companies can improve the quality of scores delivered to customers.
A new prediction algorithm called DORGE has identified novel tumor suppressor genes and oncogenes by integrating comprehensive genetic and epigenetic data. The study found that the algorithm successfully predicted both known and novel cancer driver genes, including dual-functional genes involved in protein-protein interaction networks.
Researchers at UVA have developed an algorithm to classify genomic data using quantum computers, potentially revolutionizing the field of genetic research. The new technology could analyze vast amounts of genetic data exponentially faster than conventional computers.
The study focuses on flipons, DNA sequences that act as on-off switches to change genetic information. Flipons enable the compilation of multiple messages from a single genomic sequence, generating more diversity than mutation or DNA rearrangements.
Researchers have created an algorithm that can visualize the evolution of genetic mutations, revealing patterns in how proteins change over time. The tool, called minimum epistasis interpolation, helps scientists understand how specific mutations combine to affect protein function.
Researchers from Drexel University have developed a method to analyze RNA codes that can reveal how groups of microbes operate. The new approach, called 'themetagenomics,' identifies recurring patterns indicating co-occurring groups of microbes, allowing for better understanding of their functions and potential health implications.
Researchers developed a deep learning model that extracts patterns from gene locations and functions to identify disease associations. The KAUST model achieves better accuracy than state-of-the-art methods by combining multiple datasets and incorporating graph convolutional networks.
Researchers created an AI algorithm, VarCoPP, to identify genetic combinations causing rare diseases. The algorithm provides confidence intervals for predicting pathogenicity, aiding doctors in diagnoses.
Researchers describe an effective, unbiased method for choosing the best algorithm for prioritizing GWAS results, called Benchmarker. Combining multiple strategies often gives the best results and certain algorithms perform best when looking for genes for specific traits.
A new algorithm can detect the molecular signature of a cancer-driving defect, identifying patients who could benefit from treatment with PARP inhibitors. The algorithm has the potential to expand greatly the pool of patients eligible for this therapy.
A new algorithm developed by Stanford researchers can accurately identify individuals at risk of familial hypercholesterolemia (FH), a cholesterol-raising genetic disease that increases the risk of early and fatal heart problems. The algorithm, trained on data from over 200 FH patients, correctly flagged 88% of cases in testing runs.
Gammaherpesvirus 1 (FcaGHV1) has been identified as a major contributor to feline cancer and chronic kidney disease, while morbillivirus has linked to tubulointerstitial nephritis. The viruses have no known zoonotic risk, but further research is needed.
Researchers from Siberian Federal University create a self-configuring evolutionary genetic algorithm to automate X-ray diffraction quantitative phase analysis for accurate electrolyte composition analysis. The method improves the efficiency of aluminium production by reducing human involvement and errors.
Researchers at UCSB have identified a nuanced way to influence surface hydrophobicity, which affects how water interacts with solid surfaces. By varying the patterning of hydrophobic groups, they found that it can produce large effects on water dynamics, making it useful for designing more efficient membranes.
A new genetic screen developed at Stanford University School of Medicine can predict a person's future risk of osteoporosis and bone fracture. The screen identified 899 regions in the human genome associated with low bone-mineral density, with those deemed high-risk being nearly 17 times more likely to develop osteoporosis.
A genetic algorithm developed by Spanish researchers accurately predicted the vertical growth of Tokyo's Minato Ward, predicting the number and locations of future skyscrapers. The study suggests that evolutionary computation can identify hidden patterns in urban systems and predict possible city evolution scenarios.
Researchers at Lobachevsky University and University of Calabria propose new visual techniques to compare nature-inspired and deterministic global optimization algorithms. Their experiments show that both classes of methods are competitive, with performance depending on computational budget.
A five-year study found that genetic testing can help tailor the warfarin dosage to individual patients undergoing joint replacement surgery, resulting in significant reductions in adverse side effects. The study showed a 75% decrease in major bleeding and a 15% decrease in blood clots.
A study of Peruvian populations reveals genetic adaptations to high altitude living and chronic mountain sickness. The researchers identified 11 regions containing 38 genes that were under positive selection, suggesting a molecular adaptation mechanism that regulates gene expression in response to environmental challenges.
A team of scientists compared 20 subgene resolution algorithms to analyze cancer genome data, reproducing known cancer genes and discovering new drivers. This study informs potential users about algorithm assumptions and results, shedding light on the complexities of cancer.
The study suggests that animal phyla emerged rapidly around 700 million years ago, prior to the global Ice Age. The earliest known animal fossils exhibit complex morphologies, indicating animals originated before the Cambrian Period. New molecular-clock analysis produces a new estimate for the ages of the oldest animal groups.
Researchers have developed a new tool called ModelFinder to study the evolution of proteins. The program allows for more accurate scientific estimates of evolutionary processes, which will help unravel the mysteries behind life's diversity on Earth.
Researchers developed a genetic algorithm to quantify conclusions about the rejection of classical notions of causality. The algorithm mapped out many dimensions of the departure from classical that quantum correlations exhibit.
Researchers found that non-cooperative norms initially dominate, followed by a norm allowing complex judgment of good and bad behavior. Cooperation then spreads as multiple cooperative norms coexist.
Researchers at UC Riverside developed an innovative energy management system for plug-in hybrids, cutting fuel consumption by over 30% through real-time data analysis. The system combines connected vehicle technology and evolutionary algorithms to optimize power split between engine and battery, achieving greater efficiency.
Researchers at Columbia University developed a new machine-learning algorithm called TeraStructure to analyze massive genetic data sets. The algorithm can estimate population structure more accurately and twice as fast as current state-of-the-art algorithms, making it potentially useful for identifying disease-causing genetic mutations.
A team of Lehigh engineers has created a novel method called AMIGO that considers multiple elements and is the first to factor in so many aspects. The algorithm demonstrates its effectiveness on transportation network recovery in an imagined post-earthquake San Diego, finding near-optimal solutions in a small number of trials.
The algorithm combines all published algae studies prior to February 2015 to rank genetic types' thermotolerance, helping identify colonies at risk from climate change. By pinpointing thermotolerant algae, conservationists can focus their efforts on preserving coral reefs.
A computer model mimics bird's syrinx to generate authentic sounding birdsong, utilizing Twitter updates for a unique crowd-generated audio-artwork. The system can manipulate acoustic space in real-time, offering an acoustically original experience.
A team led by Dmitry Korkin will analyze vast genomic data to find common genetic elements, known as LIMEs, that have remained unchanged over millions of years. These sequences are likely to perform vital functions in cells and provide insight into the roles they play.
Researchers developed an algorithm dubbed ENVE to detect genetic aberrations in tumors using deep sequencing data. The algorithm resolves most noise issues and reveals areas where genetic changes have gone awry, potentially leading to more effective treatments for multiple types of cancer.
New research reveals that minor changes in environment can trigger dramatic shifts in evolutionary strategy as a generalist species disperses into a heterogeneous landscape. This can lead to a positive feedback loop where reduced dispersal triggers further specialization.
Scientists used theoretical calculations to gain insights into the structure of β-NiOOH, a highly active compound in a nickel oxide catalyst. The research found that the material's mosaic texture and c-axis periodicity are stable tunnel structures that relieve stress between layers.
The Hotnet2 algorithm identified 16 subnetworks of genes mutated with surprising frequency in cancer samples. The research provides new leads for somatic mutations driving cancer and may help prioritize experiments towards specific genes and pathways.
Computer analysis of genetic data and supercomputing capabilities have enabled researchers to recreate the bird tree of life, revealing a burst of evolutionary growth after 15 million years. The study also uncovered new findings on birdsongs, feathers, biodiversity, and bird evolution.
A new algorithm has identified KLHL9 as the driving force behind aggressive glioblastoma, a type of brain cancer. Reintroducing KLHL9 protein suppressed tumors in mice transplants, offering hope for a possible treatment strategy. The approach also revealed new genes involved in breast cancer and Alzheimer's disease.
Researchers are studying nature's secrets to develop efficient light-based technologies. From seashells to spider wings, scientists are discovering inspiration in the natural world to create innovative solutions for energy, healthcare, and communications.
A study of chimpanzee aggression over five decades found that killings are not a result of human activities, but rather an adaptive tactic to eliminate rivals. The research provides strong support for the view that killing is an evolved behavior, increasing access to resources and improving survival rates.
A team of engineers developed an optimization algorithm inspired by salmon migration patterns, successfully solving 25 benchmarking problems and finding optimal solutions for laser setup. The Great Salmon Run (TGSR) algorithm outperformed conventional genetic algorithms in most cases, converging faster to optimal solutions.
Researchers develop a crowdsourcing game to tackle the phase problem, achieving successful results in low-resolution phasing puzzles. The approach leverages human pattern recognition capabilities to guide the search process.
Physicists at the University of Rochester have created a silicon nanocavity that allows light to be trapped for nanoseconds. The innovative design approach mimics evolutionary biology and achieves a 10-fold improvement on previous performances.
A team of computer theorists and evolutionary biologists identified an algorithm to describe the strategy used by genes during sexual recombination, addressing a long-standing paradox in evolution. The multiplicative weight update algorithm helps explain how natural selection and diversity interact.
23andMe's new HaploScore algorithm improves IBD detection accuracy, enabling more accurate genetic relationships and ancestry reports. The open-source algorithm can be applied to existing IBD segments, differentiating between true and false positives.
Researchers at Penn State have developed a new metamaterial with high absorption over broad bandwidth, providing better protection against electromagnetic radiation. The material is designed using genetic algorithms and can be easily manufactured due to its simple layer structure.
A new software system, XMLMATE, uses genetic algorithms to detect software errors, covering almost twice as many fatal errors as similar test methods. The system can be applied to various programming languages and applications, including computer networks, websites, and operating systems.
BYU engineer Dah-Jye Lee has created a genetic algorithm that accurately identifies objects in images or video sequences without human intervention. The algorithm learns from a set of images and sets its own parameters, achieving 100% accurate recognition on four image datasets and distinguishing between fish species with 99.4% accuracy.
Researchers have identified a genetic variation that can help estimate the correct dose of warfarin for African-Americans. This discovery may improve the predictability of warfarin dosing by 21 percent, reducing hospitalizations for adverse drug reactions.
Researchers developed powerful data-sifting algorithms to assemble the most complete genetic profile of acute myeloid leukemia, an aggressive form of blood cancer. The work aims to lead to new AML treatments based on the genetics of each patient's disease.
Researchers found chimpanzees inspect trees with similar fruit characteristics, relying on botanical knowledge to predict fruit availability. This discovery provides insights into the evolution of categorization abilities and abstract thinking in humans.
The evolutionary block cipher outperforms known cryptosystems in resisting both linear and differential attacks by introducing evolving encryption algorithms. This design feature enhances the system's security and efficiency, making it a promising solution for modern cryptography.
Researchers can now search for models that best explain complex data using an evolutionary computation approach, allowing for flexibility and insights. This method enables the simultaneous evaluation of many candidate solutions, speeding up the work and providing additional insights.
Case Western Reserve University researchers developed a new computer modeling method that accurately predicts how peripheral nerve axons respond to electrical stimuli, slashing the process from weeks to just seconds.
A new computer program called EFIT-V is being used by European police departments to help witnesses remember and recognize suspects. The software generates multiple faces that evolve based on the witness' memories, increasing identifications of suspects compared to traditional methods.
A new algorithm developed by Carnegie Mellon University has revealed how genetic networks in fruit flies evolve during their life cycle. The Tesla algorithm uses machine learning techniques to analyze the rewiring of these networks over time, providing insights into how they respond to stimuli and become dysfunctional.
Researchers developed a design for reliability (DFR) approach to minimize warranty costs by proactively identifying potential hardware and software failures. This approach can be implemented in the early development phase, reducing costly repairs and improving product quality.
Scientists developed a gene-based strategy for warfarin dosing using data from over 5,000 patients worldwide. The algorithm predicts ideal doses with high accuracy, reducing the need for trial-and-error approaches.
Researchers develop algorithm to analyze financial data and predict company failure, outperforming single-approach methods. The hybrid approach uses genetic-based weighting to identify fittest variables, allowing for accurate predictions of financial future.