AI News, Deep learning for genomics
Genome of ‘Lonesome George’ reveals a tortoise’s secrets to long life
Highly precise atomic clocks from three different continents have been recruited in the search for dark matter.
One theory proposes that this dark matter is actually an artefact of inconsistencies that formed in the fabric of space-time as the early Universe cooled.
That would momentarily disturb the inner workings of extremely precise atomic clocks, enabling scientists to detect the defect.
A topological defect would show up as a shift in the frequencies of the lasers and atoms that the clocks use to keep time, with a different shift for each clock.
Machine Learning in Genomics – Current Efforts and Future Applications
Genomics is a branch of molecular biology focused on studying all aspects of a genome, or the complete set of genes within a particular organism.
In this article we will explore: Before diving into present applications, we’ll begin with background facts and terminology about genomics and precision medicine, and a quick summary of the findings of our research on this topic: The ability to sequence DNA provides researchers with the ability to “read” the genetic blueprint that directs all the activities of a living organism.
With a market size projected to reach $87 billion by 2023, the field of Precision Medicine (also known as personalized medicine) is an approach to patient care that encompasses genetics, behaviors and environment with a goal of implementing a patient or population-specific treatment intervention;
even after a massive relative plunge in cost between 2007 and 2012: Current applications of machine learning in genomics appear to fall under the following two categories: Next, we’ll explore four major areas of current machine learning applications in genomics.
Current applications of machine learning in the field of genomics are impacting how genetic research is conducted, how clinicians provide patient care and making genomics more accessible to individuals interested in learning more about how their heredity may impact their health.
Next Generation Sequencing has emerged as a buzzword which encompasses modern DNA sequencing techniques, allowing researchers to sequence a whole human genome in one day as compared to the classic Sanger sequencing technology which required over a decade for completion when the human genome was first sequenced.
Specifically, algorithms are designed based on patterns identified in large genetic data sets which are then translated to computer models to help clients interpret how genetic variation affects crucial cellular processes.
Founded in 2012, the company has accrued $5.8 million in total equity funding from 7 investors which include a mix of accelerators, venture capital firms and biotech company and DNA sequencing veteran Illumina.
The company reports two key findings from a recent study: 1) an increased amount of training data improves the accuracy of an algorithm in its ability to predict CRISPR activity and 2) the accuracy of the model decreases when applied to a different species, such as humans vs.
The firm’s latest three AI company investments totaled roughly $133.35 million in Series A and B funding, perpetuating a trend of relatively high AI investment in the healthcare sector (compared to other industry verticals).
Despite concerns around regulation and the role of health professionals in helping individuals interpret their test results, direct-to-consumer genomics is a rapidly growing industry and leading companies such as 23andMe and Ancestry.com are becoming household names.
Unique factors used to develop each report include “genotype, sex, age, and self-identified primary ancestry.” These factors would be determined either from a customer’s genetic information or derived from a survey that would be administered prior to accessing the report.
With over 2 million customers to date, it will be interesting to see what economic impact the Genetic Weight report will have on user lifestyle habits, the weight loss industry in general and on the company’s business model going forward.
Future applications of machine learning in the field of genomics are diverse and may potentially contribute to the development of patient or population-specific pharmaceutical drugs, help farmers improve soil quality and crop yield, and contribute to the development of advanced genetic screening tools for newborns.
Results of the study showed that instances of false positives were reduced “from 21 to 2 for phenylketonuria (PKU), from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency.” The potential for genomics to help improve soil quality and crop yield is an emerging area of interest and promise within the sphere of agriculture.
Machine learning in genomics is currently impacting multiple touch points including how genetic research is conducted, how clinicians provide patient care and the accessibility of genomics to individuals interested in learning more about how their heredity may impact their health.
Efforts to implement AI to help accelerate the path from bench-to-bedside and make precision medicine more commonplace is smart business (readers will a deeper interest in this topic may want to explore our recent article on the applications of machine learning in medicine and pharma).
Artificial Intelligence Is the Next Big Player in Genomics
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.
Biospace formerly reported on KSQ Therapeutics’ “$80 million Series C financing that will be put towards the development of oncology drug candidates via CRISPRomics™, a drug discovery engine that creates insights specific to individual human genes on an industrial scale.” The rapid advancement of CRISPR capability does not end here, however.
With this insight, they can make decisions about care, what an organism might be susceptible to in the future, what mutations might cause different diseases and how to prepare for the future.” Multiple aspects of human life are determined by an individual's genetics, including predispositions to illnesses such as cystic fibrosis, Huntington's disease, sickle cell anemia, and others.
For example, the company Deep Genomics is analyzing genomic data using deep learning – the process by which a computer integrates a very large amount of data and then, based on its learning techniques drawn from analyzing other datasets, interprets that new information.
Another advantage AI systems could bring to clinical research is to process simultaneously huge amounts of genomic, physiological, health, environmental and lifestyle data, meaning these systems would read the content of our genomes, our smartphones and our medical records, and draw inferences [sic] about what they have learned.
- On 5. marts 2021
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