Who cares about negative results? Most of the people think that researchers would not try to publish a paper that focused on what they did not find. However, that is not to say that negative results do not have scientific value—in fact, they can be quite useful. Fortunately now is easy to find a journal that agree to publish the negative results.
In the field of bioinformatics, for example, both a positive and a negative dataset are required for training machine learning algorithms, such as protein-protein interaction prediction tools. Dmitri Frishman, along with co–first authors Pawel Smialowski and Philipp Pagel and their colleagues at the Technical University of Munich and the German Research Center for Environmental Health, recently introduced a well-curated database of protein pairs that are unlikely to interact, aptly called the “Negatome.”
Protein interactions are responsible for carrying out almost all biological functions; the entire network of interactions is known as the interactome. We are still very far away from mapping the entire interactome of any cellular organism, so good prediction tools to generate hypotheses are needed. But although well-curated positive datasets of protein-protein interactions exist for several organisms, defining with certainty which proteins do not interact is actually extremely difficult. In addition to the slim literature evidence, “there is no technique that can conduct a large-scale measurement of non-interacting pairs,” explains Frishman.
The All Results Journals can help bioinformatics in a way that never have been seem before. Perhaps the larger scientific community will see the value of the Negatome and these new journals, and thus be encouraged to make negative results, in many different fields, more widely available.