A team from the University of Virginia School of Medicine is leveraging the power of quantum computing to gain better insight into genetic diseases with machine learning. Although quantum computers are still in their infancy, the researchers noted that when they do advance, they could offer computing power on a scale that's unimaginable on traditional computers. "We developed and implemented a genetic sample classification algorithm that is fundamental to the field of machine learning on a quantum computer in a very natural way using the inherent strengths of quantum computers," said Stefan Bekiranov, PhD. "This is certainly the first published quantum computer study funded by the National Institute of Mental Health and may be the first study using a so-called universal quantum computer funded by the National Institutes of Health." Quantum computers can consider significantly more possibilities than traditional computer programs.
Deep learning (DL) models are known for tackling the nonlinearities associated with data, which the traditional estimators such as logistic regression couldn't. However, there is still a cloud of doubt with regards to the increased use of computationally intensive DL for simple classification tasks. To find out if DL really outperforms shallow models significantly, the researchers from the University of Pennsylvania experiment with three ML pipelines that involve traditional methods, AutoML and DL in a paper titled, 'Is Deep Learning Necessary For Simple Classification Tasks.' The UPenn researchers stated that a support-vector machine (SVM) model might predict more accurately susceptibility to a certain complex genetic disease than a gradient boosting model trained on the same dataset. Moreover, choosing different hyperparameters within that SVM model can vary performances.
Small startups and big companies alike are recognizing that modern biotech R&D is as much a data ... [ ] problem as a science problem. Cloud technologies offer a way to bring together massive amounts of complex data to improve the way we feed, fuel, heal, and build our world with biology. These days, biotech R&D is as much a data problem as a science problem. Here's why: in the past decade, the exploding field of synthetic biology has done an incredible job solving the scientific challenges of making biology easier to engineer. I have written about how tools like gene editing, synthesis, sequencing, and automation are changing for the better the way we feed, fuel, heal, and build our world with biology.
The world of genomics has made abrupt strides in the past several years, with the first CRISPR-edited babies being born just a few weeks ago. Using advanced CRISPR technology, Scientist Jiankui He'announced that twin girls with an edited gene that reduces the risk of contracting HIV "came crying into this world as healthy as any other babies a few weeks ago."' The announcement was met with great backlash, sparking'outrage from many researchers and ethicists who say implanting edited embryos to create babies is premature and exposes the children to unnecessary health risks. Opponents also fear the creation of "designer babies," children edited to enhance their intelligence, athleticism or other traits.' CRISPR technology is used in editing human genomes.
A newly developed artificial intelligence (AI) system could help expedite the diagnosis of epileptic conditions such as Dravet syndrome. The AI system was described in a study, titled "A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome," in the journal Epilepsy & Behavior. Epilepsy is a broad disease category for many different conditions that involve seizures. Properly diagnosing epileptic conditions can be a challenge, especially given their different causes and symptoms. For example, mutations in the SCN1A gene are the most common cause of Dravet syndrome, but not all people with Dravet syndrome have such mutations, and SCN1A mutations can also be associated with other conditions, such as febrile seizures plus.
Some -omics tools can be more accurate, sensitive or efficient than others. Yet benchmarking is no tell-all. Inflammatory bowel disease (IBD) is a complex genetic disease that is instigated and amplified by the confluence of multiple genetic and environmental variables that perturb the immune–microbiome axis. Here the authors describe IBD as a model disease in the context of leveraging human genetics to dissect interactions in cellular and molecular pathways that regulate homeostasis of the mucosal immune system. Machine learning can tell different types of knot apart just by'looking' at them.
We know OpenTable as the restaurant reservation system, but OpenTable has revolutionized the entire restaurant industry. By compiling a comprehensive database of dates, names, places, check size and so on, OpenTable creates operational advantages for its food and beverage customers. It provides the infrastructure to manage those reservations, assign tables, recognize repeat diners and remember diner preferences. It also allows restaurants to better manage costs by staffing correctly and minimizing food waste.
The system can be deployed on a smartphone, achieves 91 percent top-10-accuracy in identifying over 215 different genetic syndromes, and has outperformed clinical experts in three separate experiments. The FDNA team's research paper, Identifying facial phenotypes of genetic disorders using deep learning, has been published in Nature Medicine. Using deep learning algorithms and brain-like neural networks, the Face3Gene app can predict congenital and neural developmental disorders in people through the detection of distinctive facial features in photos. Face2Gene builds on a technique the FDNA team introduced last January in the paper DeepGestalt -- Identifying Rare Genetic Syndromes Using Deep Learning. Researchers started by training an AI system to distinguish two conditions which cause distinct facial features -- Cornelia de Langs syndrome and Angelman syndrome -- from other, similar conditions.
To brave the unpredictability of epileptic seizures, three young tunisian entrepreneurs engineered a bracelet for users to monitor their own condition and most crucially, automatically contact their caregivers within seconds of a fit. Since their launch in 2017, Epilert continues to prevent epileptic fatalities and hopes to advance medical treatments by working closely with Tunisian doctors.
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