3 ms·

I think there's been a lot of research/grants for working on population genetics; specifically, sequencing genomes and genetic expression profiles of patient po

I think there's been a lot of research/grants for working on population genetics; specifically, sequencing genomes and genetic expression profiles of patient pool of a particular disease (e.g., schizophrenia) and comparing it to the general population pool and clustering to find particular groups of genes that might be responsible for a particular disease. Likewise, there are state agencies and startup's alike working on pre-natal genetics screening that basically tries to identify from pre-natal samples particular markers (SNPs) known for Down Syndrome, Tay-Sachs etc. Not to mention people who submit their samples to find interesting markers from 23andme. I'd love to hear more from folks in the field what they think the next big application of computational biology, clinical genomics will be. Obviously, there has been a lot of mixed opinion regarding both on the consumer side (FDA banning 23andme from making broad statements of users having a higher-probability of cancer due to outdated and controversial data) and research side (has Human Genome Project since its completion in the early 2000's yielded a significant gene to target compound to drug on the market?) More practically, Illumina (https://www.google.com/finance?q=NASDAQ%3AILMN&ei=af5xVYHoB4eTsQf64IDoBg https://www.google.com/finance?q=NASDAQ%3AILMN&ei=af5xVYHoB4...) has more than 10x in the past 6 years, riding the wave of demand and growth of next-gen sequencing technology. If Cloud Computing is hailed as the sequencer's of the next phase in the biotech revolution, what stocks should I buy now to take advantage of it?

donovanr 11y ago
A lot of the low-hanging fruit, in terms of statistical significance for heritable disease, has already been picked, in the sense of a naive search for SNP X in gene Y that causes disease Z. The more people continue to look for single SNPs or even single gene causal factors, the more we learn that they are of limited use, due to the complexity and redundancy of biological interaction networks (genes/proteins/epigenetic modifications/environment/etc) One thing that people are definitely still interested in regarding single genes or SNPS is looking for biomarkers. For instance, it's pretty easy to look at RNA abundance with RNA-seq and make a model with good discriminatory power to predict who does or doesn't have (say) breast cancer. But Those tests can be expensive, and profiling the appropriate tissue can be difficult. On the other hand, if we could find SNPs that correlate well with the RNA expression model that predicts disease, then we could just do a cheaper test/faster/easier test for the SNPs. Even better if we can validate causation independently using the emerging, though incomplete corpus of biological pathways. Beyond that, some folks are excited about the field of functional genomics, which aims to correlate genome-level data to the structure and function of gene products (eg proteins) to get a more low-level, causal look at things. Biological network extraction is something I'm excited about. Here, you usually want to extract pairs of features, or better yet, higher order structures from data to learn about what factors are important in disease pathways. This puts you immediately in the regime of way more potential features than data points, even if you sequence everyone in the world. I think this consideration worth thinking about when people try to hype you that big data and machine learning will just "solve" biology or medicine. L1 regularization only gets you so far. But it gets you somewhere, and can be hugely useful in suggesting new experiments. I also think it's important to think about how it may soon be cheaper to store actual RNA/DNA and sequence it on demand, than it will be to store the data itself. DNA sequences would be a fine thing to sequence once and store, but for stuff, maybe not so much.
buckbova 11y ago
Illumina is building a cloud product of its own and we still need sequencers. So, Illumina still looks like a good investment.

donovanr 11y ago

A lot of the low-hanging fruit, in terms of statistical significance for heritable disease, has already been picked, in the sense of a naive search for SNP X in gene Y that causes disease Z. The more people continue to look for single SNPs or even single gene causal factors, the more we learn that they are of limited use, due to the complexity and redundancy of biological interaction networks (genes/proteins/epigenetic modifications/environment/etc) One thing that people are definitely still interested in regarding single genes or SNPS is looking for biomarkers. For instance, it's pretty easy to look at RNA abundance with RNA-seq and make a model with good discriminatory power to predict who does or doesn't have (say) breast cancer. But Those tests can be expensive, and profiling the appropriate tissue can be difficult. On the other hand, if we could find SNPs that correlate well with the RNA expression model that predicts disease, then we could just do a cheaper test/faster/easier test for the SNPs. Even better if we can validate causation independently using the emerging, though incomplete corpus of biological pathways. Beyond that, some folks are excited about the field of functional genomics, which aims to correlate genome-level data to the structure and function of gene products (eg proteins) to get a more low-level, causal look at things. Biological network extraction is something I'm excited about. Here, you usually want to extract pairs of features, or better yet, higher order structures from data to learn about what factors are important in disease pathways. This puts you immediately in the regime of way more potential features than data points, even if you sequence everyone in the world. I think this consideration worth thinking about when people try to hype you that big data and machine learning will just "solve" biology or medicine. L1 regularization only gets you so far. But it gets you somewhere, and can be hugely useful in suggesting new experiments. I also think it's important to think about how it may soon be cheaper to store actual RNA/DNA and sequence it on demand, than it will be to store the data itself. DNA sequences would be a fine thing to sequence once and store, but for stuff, maybe not so much.

buckbova 11y ago

Illumina is building a cloud product of its own and we still need sequencers. So, Illumina still looks like a good investment.