Genetic Disease


Google Seeks People With Down Syndrome To Help Train AIs To Understand Human Speech

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The last half decade has ushered in the era of humans interacting with technology through speech, with Amazon's Alexa, Apple's Siri, and Google's AI rapidly becoming ubiquitous elements of the human experience. But, while the migration from typing to voice has brought great convenience for some folks (and improved safety, in the case of people utilizing technology while driving), it has not delivered on its potential for the people who might otherwise stand to benefit the most from it: those of us with disabilities. For people with Down Syndrome, for example, voice-based control of technology offers the promise of increased independence – and even of some new, potentially life-saving products. Yet, for this particular group of people, today's voice-recognizing AIs pose serious problems, as a result of a combination of 3 factors: To address this issue, and as a step forward towards ensuring that people with health conditions that cause AIs to be unable to understand them are able to utilize modern technology, Google is partnering with the Canadian Down Syndrome Society; via an effort called Project Understood, Google hopes to obtain recordings of people with Down Syndrome reading simple phrases, and to use those recordings to help train its AI to understand the speech patterns common to those with Down Syndrome. This effort is an extension of Google's own Project Euphonia, which seeks to improve computers' abilities to understand diverse speech patterns including impaired speech, and, which, earlier this year, began an effort to train AIs to recognize communication from people with the neuro-degenerative condition ALS, commonly known as Lou Gehrig's Disease.


Opportunities for Artificial Intelligence in Advancing Precision Medicine

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In the past decade, advances in genetic disease and precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment [1]. The enormous divergence of signaling and transcriptional networks mediating the cross talk between healthy, diseased, stromal, and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. Unexpectedly, the conclusion of the human genome did not translate into a burst of new drugs. The pharmaceutical industry rather announced a declining output in terms of the number of new drugs approved despite increasing commercial efforts of drug research and development [2, 3]. In contrast, machine learning (ML) as well as network and systems biology are innovating with impactful discoveries and are now starting to be seamlessly integrated into the biomedical discovery pipeline [4].


Exploring the Characterization and Classification of EEG Signals for a Computer-Aided Epilepsy Diagnosis System

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Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM).


Digital Health Trial Uses AI For Better Epilepsy Treatment Decisions

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Imagine having to choose from over 14,000 different treatment scenarios to decide which drugs might be best for a child or a loved one affected by epilepsy. This is what faces many families according to the experts at Stanford and doc.ai who have announced a new type of clinical trial using artificial intelligence (AI). The project's goal is to help make the process more scientific using population data and less prone to lengthy individual trial-and-error. Researchers are analyzing medications, side effects, genomic information, environmental exposures, activity and even physical traits. This type of work produces vast amounts of information and requires so much processing power that it can only be performed by the latest AI systems.


Artificial Intelligence has potential to transform gene therapy

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The research, Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design, published in the journal Science, was conducted by Dyno Therapeutics, a biotechnology company pioneering use of Artificial Intelligence in gene therapy. AAV capsids are presently the most commonly used vector for gene therapy because of their established ability to deliver genetic material to patient organs with a proven safety profile. However, there are only a few naturally occurring AAV capsids, and they are deficient in essential properties for optimal gene therapy, such as targeted delivery, evasion of the immune system, higher levels of viral production, and greater transduction efficiency. Starting at Harvard in 2015, the authors set out to overcome the limitations of current capsids by developing new machine-guided technologies to rapidly and systematically engineer a suite of new, improved capsids for widespread therapeutic use. In the research the authors demonstrate the advance of their unique machine-guided approach to AAV engineering.


An artificial intelligence approach to create AAV capsids for gene therapies – Biopharmanalyses

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Sam Sinai, George Church, Eric Kelsic, and Pierce Ogden are holden small models of the AVVs capsid in their hands. Improved AAV vector capsid for gene therapy engineered with a new machine-guided approach shows, in red, improvements in efficiency of viral production based on the average effect of insertions at all possible amino acid positions, with white showing neutral and blue showing deleterious positions.


How AI Is Helping Diagnose Rare Genetic Diseases

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AI has the power to search through millions of genetic variants at high speed and identify likely ... [ ] causes of rare diseases, while also comparing what they find with the existing medical literature. This is greater than the population of the United States, yet the ominous figures don't end there. According to the Global Genes organization, eight out of ten rare diseases are caused by a faulty gene, yet it takes an average of 4.8 years to arrive at an accurate diagnosis. This is part of the reason why 30% of children with a rare disease won't live to see their fifth birthday. Neither is this situation helped by the fact that 95% of rare diseases lack an FDA-approved treatment.


How AI is Changing the Way We Treat Diseases and Disabilities

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The age of artificial intelligence is allowing us to rethink the way that we treat diseases and disabilities. The combination of AI and Big Data, in addition to helping with medical diagnosis, coupled with biological delivery systems, such as gene therapy delivery system can significantly alter the way we treat a host of diseases that are, according to modern science, incurable: cancer, autism, some mental illnesses, and rare genetic illnesses. Specifically, combining AI, big data, robotics, gene therapy, and medical research has unleashed a host of possibilities to cure these types of diseases. At the same time, the combined innovation efforts are helping people with disabilities live their lives better. Here's an overview of some of these advances as we move into the new year.


Research enables artificial intelligence approach to create AAV capsids for gene therapies

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Cambridge, MA, November 28, 2019 -- Dyno Therapeutics, a biotechnology company pioneering use of artificial intelligence in gene therapy, today announced a publication in the journal Science that demonstrates the power of a comprehensive machine-guided approach to engineer improved capsids for gene therapy delivery. The research was conducted by Dyno co-founders Eric D. Kelsic, Ph.D. and Sam Sinai, Ph.D., together with colleague Pierce Ogden, Ph.D., at Harvard's Wyss Institute for Biologically Inspired Engineering and the Harvard Medical School laboratory of George M. Church, Ph.D., a Dyno scientific co-founder. AAV capsids are presently the most commonly used vector for gene therapy because of their established ability to deliver genetic material to patient organs with a proven safety profile. However, there are only a few naturally occurring AAV capsids, and they are deficient in essential properties for optimal gene therapy, such as targeted delivery, evasion of the immune system, higher levels of viral production, and greater transduction efficiency. Starting at Harvard in 2015, the authors set out to overcome the limitations of current capsids by developing new machine-guided technologies to rapidly and systematically engineer a suite of new, improved capsids for widespread therapeutic use.


Research enables artificial intelligence approach to create AAV capsids for gene therapies

#artificialintelligence

Cambridge, MA, November 28, 2019 -- Dyno Therapeutics, a biotechnology company pioneering use of artificial intelligence in gene therapy, today announced a publication in the journal Science that demonstrates the power of a comprehensive machine-guided approach to engineer improved capsids for gene therapy delivery. The research was conducted by Dyno co-founders Eric D. Kelsic, Ph.D. and Sam Sinai, Ph.D., together with colleague Pierce Ogden, Ph.D., at Harvard's Wyss Institute for Biologically Inspired Engineering and the Harvard Medical School laboratory of George M. Church, Ph.D., a Dyno scientific co-founder. AAV capsids are presently the most commonly used vector for gene therapy because of their established ability to deliver genetic material to patient organs with a proven safety profile. However, there are only a few naturally occurring AAV capsids, and they are deficient in essential properties for optimal gene therapy, such as targeted delivery, evasion of the immune system, higher levels of viral production, and greater transduction efficiency. Starting at Harvard in 2015, the authors set out to overcome the limitations of current capsids by developing new machine-guided technologies to rapidly and systematically engineer a suite of new, improved capsids for widespread therapeutic use.