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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.

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.

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.

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.

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."

Well said. In terms of my mention of "old formal methods", these are being used today as academics tend to be risk averse related to employing more experiemental methods due to reputational and funding risks.

Who developed those methods in the first place? If it was academics, why did they not have the same "reputational and funding risks" in the late 1990s, which is when you said "the bioinformatics revolution started"? What's changed in the last 20 years?

You compare academics in general to a small number of non-academic people and organizations. The latter are not representative of non-academics, because I know people in industry who use the same methods as those in academics. Is Patcher, the author of this piece and an academic, one of the people who uses "old formal methods" due to "reputational and funding risks"?

I'll add that I mostly work in cheminformatics. There is relatively little academic research in this field. Most of it is driven by industry, and more specifically drug discovery. A lot of people in industry also use "old formal methods".

Finally, here's a quote from someone who "served on the advisory council of [a longevity] organization, along with the chairmen of a number of major US corporations":

> Why has the problem of aging been such an intractable one? Up until [recently], the prevalent view of scientists had been that the task of controlling aging was fundamentally impossible. But today, such a consensus no longer exists. Many researchers now believe that their predecessors failed, not because their goals were misguided, but because the tools and the level of sophistication they could bring to the task were inadequate. Moreover, it is argued that progress has been hampered because funding has been scarce, and researchers concerned with aging have been too few and far between. ...

> Growing public and private support for aging research reflects the scientific community’s own increasing commitment. Today, aging research occupies unprecedented numbers of highly talented individuals, not only specialists in gerontology, but researchers from other disciplines as well. These include biochemistry, endocrinology, immunology, neurobiology, genetics, and cell biology, to name only a few.

Before following the link, when do you think that essay was written? http://www.garfield.library.upenn.edu/essays/v6p107y1983.pdf .

[PDF]Origin & History of Bioinformatics - arXiv arxiv.org/pdf/0911.4230 by SM Thampi - ‎2009

http://arxiv.org/ftp/arxiv/papers/0911/0911.4230.pdf

"The gathering, archival, dissemination, modeling, and analysis of biological data falls within a relatively young field of scientific inquiry, currently known as ‘bioinformatics’, ‘Bioinformatics was spurred by wide accessibility of computers with increased compute power and by the advent of genomics. Genomics made it possible to acquire nucleic acid sequence and structural information from a wide range of genomes at an unprecedented pace and made this information accessible to further analysis and experimentation. For example, sequences were matched to those coding for globular proteins of known structure (defined by crystallography) and were used in high-throughput combinatorial approaches (such as DNA microarrays) to study patterns of gene expression. Inferences from equences and biochemical data were used to construct metabolic networks. These activities have generated terabytes of data that are now being analyzed with computer, statistical, and machine learning techniques. The sheer number of sequences and information derived from these endeavors has given the false impression that imagination and hypothesis do not play a role in acquisition of iological knowledge. However, bioinformatics becomes only a science when fueled by hypothesis-driven research and within the context of the complex and everchanging living world. "

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.

Why do you think it has to be web-based?

- 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/

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).

It was a comparison. Once MooTools was the library to use, now its dead.