Chapter
Reverting Cells to Combat Genetic Diseases
10:11 - 27:26 (17:15)
Scientists hope that by reverting the cells in which a genetic disease begins and is driven by genetics to a healthy state, it could also revert the global clinical phenotypes of a disease. This could potentially help combat the genetic burden of diseases in people with higher variations in their genome.
Clips
The primary focus of applying machine learning in biochemistry is to build predictive models.
10:11 - 14:04 (03:53)
Summary
The primary focus of applying machine learning in biochemistry is to build predictive models. The goal is to create data sets that enable machine learning to be applied productively to address fundamental problems in human health.
ChapterReverting Cells to Combat Genetic Diseases
Episode#93 – Daphne Koller: Biomedicine and Machine Learning
PodcastLex Fridman Podcast
The speaker discusses the evolution of data sets and drug discoveries for autoimmune diseases over the past 12 years, noting that there are now multiple drugs available to help those with autoimmune diseases.
14:04 - 16:13 (02:09)
Summary
The speaker discusses the evolution of data sets and drug discoveries for autoimmune diseases over the past 12 years, noting that there are now multiple drugs available to help those with autoimmune diseases.
ChapterReverting Cells to Combat Genetic Diseases
Episode#93 – Daphne Koller: Biomedicine and Machine Learning
PodcastLex Fridman Podcast
The disease in a dish model involves understanding how potentially sick cells differ from healthy cells and exploring interventions that may revert the unhealthy looking cell to a healthy cell.
16:13 - 20:56 (04:42)
Summary
The disease in a dish model involves understanding how potentially sick cells differ from healthy cells and exploring interventions that may revert the unhealthy looking cell to a healthy cell. This model holds potential promise for diseases caused by human genetics, where typical animal models are ineffective.
ChapterReverting Cells to Combat Genetic Diseases
Episode#93 – Daphne Koller: Biomedicine and Machine Learning
PodcastLex Fridman Podcast
Collagenic risk scores assess an individual person's genome and their risk for certain diseases, based on the number of variations that are causative or protective of those diseases.
20:56 - 27:26 (06:30)
Summary
Collagenic risk scores assess an individual person's genome and their risk for certain diseases, based on the number of variations that are causative or protective of those diseases. The total number of stem cells, including induced pluripotent stem cells, is estimated to be between 5 and 10,000, and there are differences in how well these stem cells differentiate between individuals.