AI News, BOOK REVIEW: Machine Learning in Genomics artificial intelligence

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).

Machine Learning Algorithm for Genome-Derived Prediction of Tumor Type

In a study reported in JAMA Oncology, Penson et al developed a machine learning algorithmic classifier that may be successful in identifying tumor type and origin based on DNA-sequence data obtained at point of care.

Analysis of circulating tumor DNA resulted in correct prediction of tumor type in 12 (63.2%) of 19 patients with genitourinary cancers, 23 (85.2%) of 27 patients with metastatic breast cancer, and 10 (71.4%) of 14 with metastatic prostate cancer. Probable tissue of origin was predicted from targeted tumor sequencing in 95 (67.4%) of 141 patients with cancers of unknown primary site.

The investigators concluded, “These results suggest that the application of artificial intelligence to predict tissue of origin in oncologic practice can act as a useful complement to conventional histologic review to provide integrated pathologic diagnoses, often with important therapeutic implications.” Barry S.

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