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And the most long standing myth, which first started after the Human Genome was sequenced by Francis Collins and Craig Ventor (now working on human longevity) o

And the most long standing myth, which first started after the Human Genome was sequenced by Francis Collins and Craig Ventor (now working on human longevity) over 14 years ago: Bioinformatics software will single-handedly be responsible for discovering billion dollar drug targets. This is in large part why most early bioinformatics companies failed - due to lack of deliver on this front along with jangled software approaches that were being moved into the commercial world from academia. The reality is that most bionformatics software relies on old formal methods and is not geared toward true highly innovative discovery and data interpretation. I do think however that we are entering a new age of bioinformatics and its associated data mining, interpreation, visualization and discovery tools which hopefully will push the bounderies of being less formal and more experimental. We need to make discoveries faster when it comes to Life Sciences. I think new approaches in bioinformatics/datamining/data science/visualization will have the greatest impact in the areas of extending human lifespan. This is what Craig Ventor, Google Calico Labs, SENS, GenoPharmix, Buck Institute are all working on now.

kodisha 11y ago
I also think it has to happen on the web (open or closed), instead of the desktop software. Also, d3 wont be able to pull it off. If we do a comparison, i think that d3 is MooTools, wee need to get to ES6/angular/react kind of libraries.
- collyw 11y ago
  What do you feel that ES6/Angular/React do that d3 won't? They are for building user interfaces. d3 is for charting. (Most bioinformaticians I know seem to use R).
  - kodisha 11y ago
    It was a comparison. Once MooTools was the library to use, now its dead.
- dalacv 11y ago
  Why do you think it has to be web-based?
  - kodisha 11y ago
    - collaboration (easier than desktop, but still possible there) - instant updates and critical bug fixes - reproducibility - visibility also, because of "Atwood's Law" [1] [1] http://blog.codinghorror.com/the-principle-of-least-power/ http://blog.codinghorror.com/the-principle-of-least-power/
dalke 11y ago
I think this myth started long before the human genome was sequenced. It is, after all, little different than the dreams for computer aided drug design in the 1980s and nanotech of the 1990s. Btw, it's Craig Venter. Also, can you think of a time in the last century where phrases like "we are entering a new age" and "We need to make discoveries faster", weren't generally believed? I can understand your excitement, but suggest that those phrases are mostly meaningless.
- meeper16 11y ago
  It's all about the data and we did not have it until the genome was sequenced. This is when the bioinformatics revolution started. Convert the term "entering a new age" to "entering a new phase". Done. Better to be optimistic when it comes to human health, science and lifespan as opposed to letting cynicism hold us back.
  - dalke 11y ago
    I recognize that people may consider comments like mine to be cynical. However, consider the phrases "jangled software approaches" and "old formal methods". Those appear to be used in a negative context. As I think people in the past are equally clever and insightful as people now, with the same sorts of optimism about "human health, science and lifespan", I regard those negative comments in a different sort of light. They have a certain cynicism about the past. Why weren't the people of the 1960s "geared toward true highly innovative discovery and data interpretation"? Was it simply the lack of data? But this was also the era which coined the term "information explosion", and developed new industries to handle it. If it is simply the lack of data, then surely in 50 years time people will say that Venter, Google Calico Labs, etc. themselves were hayseeds who used "jangled software approaches" that weren't "geared toward true highly innovative discovery and data interpretation", no? Your baseline requires knowing the human genome, but someone else's baseline might require the genome for 1 million people. If so, then my belief is that such descriptions carry little intrinsic meaning, and mostly mean "we know more now then they did then."
    - meeper16 11y ago
nmrm2 11y ago
This post comes across as 1) overly dismissive of what's been done so far, and 2) extraordinarily breathless about the capabilities of "data science", whatever the hell that is. You don't get your massive data sets without all the work done in the 90's. Also, the whole history of Science is a counter-factual to the idea that "all you need is data". More often than not, you get the data. And then you have the data. And then a long time later you finally get the (right) theory. And then a long time later you finally get the product. Unbridled optimism isn't a virtue. IMO, the sort of attitude you're expressing here (to be clear, not you, but the general attitude, which is very common) has a good shot at precipitating the next AI Winter. The promise of automating Scientific insight is a dangerous (and IMO stupid) promise to make.
- meeper16 11y ago
  I admire, praise and was part of the initial revolution in bioinformatics. It is very true that it's not all just about the data and algorithms. When coupled properly, data and algorithmic processes result in a platform that enables the genomic scientits and researhers to make that leap in discovery. Platforms that can help researchers and commercial organizations to form new hypothesis are key. Ideally, it would be nice to see more platforms with the ability to combine pieces of knowledge to hypothisize new potential discoveries validated by researchers.

kodisha 11y ago

I also think it has to happen on the web (open or closed), instead of the desktop software. Also, d3 wont be able to pull it off. If we do a comparison, i think that d3 is MooTools, wee need to get to ES6/angular/react kind of libraries.

collyw 11y ago
What do you feel that ES6/Angular/React do that d3 won't? They are for building user interfaces. d3 is for charting. (Most bioinformaticians I know seem to use R).
- kodisha 11y ago
  It was a comparison. Once MooTools was the library to use, now its dead.
dalacv 11y ago
Why do you think it has to be web-based?
- kodisha 11y ago
  - collaboration (easier than desktop, but still possible there) - instant updates and critical bug fixes - reproducibility - visibility also, because of "Atwood's Law" [1] [1] http://blog.codinghorror.com/the-principle-of-least-power/ http://blog.codinghorror.com/the-principle-of-least-power/

dalke 11y ago

I think this myth started long before the human genome was sequenced. It is, after all, little different than the dreams for computer aided drug design in the 1980s and nanotech of the 1990s. Btw, it's Craig Venter. Also, can you think of a time in the last century where phrases like "we are entering a new age" and "We need to make discoveries faster", weren't generally believed? I can understand your excitement, but suggest that those phrases are mostly meaningless.

meeper16 11y ago
It's all about the data and we did not have it until the genome was sequenced. This is when the bioinformatics revolution started. Convert the term "entering a new age" to "entering a new phase". Done. Better to be optimistic when it comes to human health, science and lifespan as opposed to letting cynicism hold us back.
- dalke 11y ago
  I recognize that people may consider comments like mine to be cynical. However, consider the phrases "jangled software approaches" and "old formal methods". Those appear to be used in a negative context. As I think people in the past are equally clever and insightful as people now, with the same sorts of optimism about "human health, science and lifespan", I regard those negative comments in a different sort of light. They have a certain cynicism about the past. Why weren't the people of the 1960s "geared toward true highly innovative discovery and data interpretation"? Was it simply the lack of data? But this was also the era which coined the term "information explosion", and developed new industries to handle it. If it is simply the lack of data, then surely in 50 years time people will say that Venter, Google Calico Labs, etc. themselves were hayseeds who used "jangled software approaches" that weren't "geared toward true highly innovative discovery and data interpretation", no? Your baseline requires knowing the human genome, but someone else's baseline might require the genome for 1 million people. If so, then my belief is that such descriptions carry little intrinsic meaning, and mostly mean "we know more now then they did then."
  - meeper16 11y ago

nmrm2 11y ago

This post comes across as 1) overly dismissive of what's been done so far, and 2) extraordinarily breathless about the capabilities of "data science", whatever the hell that is. You don't get your massive data sets without all the work done in the 90's. Also, the whole history of Science is a counter-factual to the idea that "all you need is data". More often than not, you get the data. And then you have the data. And then a long time later you finally get the (right) theory. And then a long time later you finally get the product. Unbridled optimism isn't a virtue. IMO, the sort of attitude you're expressing here (to be clear, not you, but the general attitude, which is very common) has a good shot at precipitating the next AI Winter. The promise of automating Scientific insight is a dangerous (and IMO stupid) promise to make.

meeper16 11y ago
I admire, praise and was part of the initial revolution in bioinformatics. It is very true that it's not all just about the data and algorithms. When coupled properly, data and algorithmic processes result in a platform that enables the genomic scientits and researhers to make that leap in discovery. Platforms that can help researchers and commercial organizations to form new hypothesis are key. Ideally, it would be nice to see more platforms with the ability to combine pieces of knowledge to hypothisize new potential discoveries validated by researchers.