Muscle lineage enhancer logic is miswired in PAX3-FOXO1-driven rhabdomyosarcoma PAX7-FOXO1, PAX3-NCOA1, and PAX3-INO80D tumors show evidence of miswired enhancers FOXO1 super enhancer is myogenically activated and sustains PAX3-FOXO1 expression Oncogenic translocations select for miswired enhancers around disordered proteins

No abstract

Abstract-Computer search often uses an oracle to determine the value of a proposed problem solution. Information is extracted from the oracle using repeated queries. Crafting a search algorithm to most efficiently extract this information is the job of the programmer. In many instances this is done using the programmer's experience and knowledge of the problem being solved. For the Hamming oracle, we have the ability to assess the performance of various search algorithms using the currency of query count. Of the search procedures considered, blind search performs the worst. We show that evolutionary algorithms, although better than blind search, are a relatively inefficient method of information extraction. An algorithm methodically establishing and tracking the frequency of occurrence of alphabet characters performs even better. We also show that a search for the search for an optimal tree search, as suggested by our previous work, becomes computationally intensive.

Abstract-An event with low probability is unlikely to happen, but events with low probability happen all of the time. This is because many distinct low probability events can have a large combined probability. However, some low probability events can be seen to follow an independent pattern. Algorithmic specified complexity (ASC) measures the degree to which an event is improbable and follows a pattern. We show a bound on the probability of obtaining a particular value of algorithmic specified complexity. Consequently we can say that high ASC objects are improbable.

As engineers we would like to think that we produce something different from that of a chaotic system. The Eiffel tower is fundamentally different from the same components lying in a heap on the ground. Mt. Rushmore is fundamentally different from a random mountainside. But we lack a good method for quantifying this idea. This has led some to reject the idea that we can detect engineered or designed systems. Various methods have been proposed each of which has various faults. Some have trouble distinguishing noise from data, some are subjective, etc. We propose to use conditional Kolmogorov complexity to measure the degree of specification of an object. The Kolmogorov complexity of an object, is the length of the shortest computer program required to describe that object. Conditional Kolmogorov complexity is Kolmogorov complexity, with access to a context. The program can extract information from the context in a variety of ways allowing more compression. The more compressible an object is the more we may deem the object specified. Random noise is incompressible, and so compression indicates that the object is not simply random noise. We hope this model launches further dialog on use of conditional Kolmogorov complexity in the measurement of specified complexity.