Over-optimism in bioinformatics research

The problem of ”false research findings” in medical research has focused much attention in the last few years (Ioannidis, 2005). One of the main problems, termed as ”fishing for significance” in the present letter, is that researchers often (consciously or subconsciously) report results that are in fact the product of an intensive optimization, i.e. of multiple comparisons. Such results are typically unlikely to be reproduced in an independent study and have a high probability to be false (Ioannidis, 2005). The ”fishing for significance” problem is enhanced by the so-called ”publication bias”: positive results have a much higher chance to get published than negative results, as already acknowledged fifty years ago (Sterling, 1959). In a word, many false positive results are produced through multiple comparisons, and false positives have higher chance to get published than true negatives. Moreover, the difficulty to publish negative results obviously encourages authors to find something positive in their study by performing numerous analyses until one of them yields positive results by chance, i.e. to fish for significance. Although this issue is by far less acknowledged and publicly admitted than in the medical context, the same types of problems occur in biostatistics and bioinformatics research.