| > For now, however, in hard-core physical science at least, there is little evidence of any major BD-driven breakthrough > Yet, this is precisely what is advocated in the less theoretically grounded disciplines of biology and medicine, let alone social sciences and economics. The oft-repeated mantra of the life sciences, as the pursuit of ‘hypothesis driven research’, has been cast aside in favour of large data collection activities The thing is, I've just spent two years working for molecular neurobiologists in the field of Single Cell RNA Sequencing, and large data collection has definitely lead through tons of breakthroughs there. We can now classify cell types based on gene activation, on top of the previously existing morphology and location the cells are found. That can then be used to discover new subtypes, the origins of cells during embryonic development, and even predict which cells will evolve into others[0][1][2][3]. All of this requires vast amounts of data to ensure there is enough statistical power. In fact, the insistence on using unbiased samples before applying clustering algorithms is a big part of overcoming biases based on pre-existing expectations. (Also, may I request that you edit your comment and break up that block of text into sub-paragraphs, for the sake of readability?) [0] http://mousebrain.org/ [1] https://linnarssonlab.org/osmFISH/ [2] http://gioelelamanno.com/post/velocitynature/ [3] https://www.nature.com/articles/d41586-018-05882-8 |