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\nThe elusiveness of the binary<\/strong><\/p>\n
\nNormally when I write fictional characters, I don’t see the similarities with my own personality until I step back. It was only the other day, when I was struggling over annotating one of my validation screens, that I saw what I shared with my heroine.<\/p>\n
\nIt should be easy, right? An image of genetically altered cells either depicts something that looks like wild type, or something that differs from it: in other words, a phenotype. When you look at cells, it should be possible to say yes, this is a hit<\/em>, or no, it is not<\/em>. But somehow, the mind slides away from the inevitability of the task. I can’t help but worry that any ‘no’ might somehow be eliminating an important clue forever. The first time I went through this smaller subset of fly genes, I found myself ranking things in a painfully analogue fashion \u2013 a system of stars from none to four, supplemented with lots of prose marginalia. And even that wasn’t enough: in that seemingly infinite space between no stars and one, I couldn’t help, in a few cases, to write ‘maybe’. Occasionally further undermined by a question mark.<\/p>\n \nThis time through, on the repeat, I was determined to make a clean decision: score each phenotype, compare with the previous run-through’s star ranking, and then decide once and for all: an overall yes or no<\/em> for each gene.<\/p>\n \nWell, needless to say I failed miserably. I’ve managed to be ruthless enough to pare it down to only two stars and no maybes, but I couldn’t quite commit to that binary decision. It’s times like these when I start to see the appeal of automated image analysis. In this procedure, all the parameters become numbers, and you introduce a cut-off, under which the answer is no<\/em>. Even the pain of choosing the threshold is taken from you \u2013 it’s all down to statistics, which never equivocate or worry about the consequences of false negatives.<\/p>\n \nBut it’s proving difficult to find any image analysis partners willing to take on my data long-term. I’m already on my third collaborator, but only the other day she broke it off as well \u2013 over the phone. It was almost exactly like getting dumped:<\/p>\n \n“It’s not your dataset \u2013 it’s me,” she said. “I’m going in new directions.”<\/p>\n \nIt’s not the size of my dataset that’s scaring them off: it’s the complexity of the textures and structures we want to score. We’ve actually bought a MATLAB license and are thinking about going it alone \u2013 but it’s a scary new direction for this squishy biologist.<\/p>\n","protected":false},"excerpt":{"rendered":" The heroine of my third novel has a problem with filing: she is incapable of committing to a concrete decision. Instead of compartmentalizing her papers into simple, broad categories, she has arranged her reprint collection into a drawer of hundreds … Continue reading