Best hits of 11110110111: model-free selection and parameter-free sensitivity calculation of spaced seeds
نویسندگان
چکیده
منابع مشابه
All hits all the time: parameter-free calculation of spaced seed sensitivity
MOTIVATION Standard search techniques for DNA repeats start by identifying small matching words, or seeds, that may inhabit larger repeats. Recent innovations in seed structure include spaced seeds and indel seeds which are more sensitive than contiguous seeds. Evaluating seed sensitivity requires (i) specifying a homology model for alignments and (ii) assigning probabilities to those alignment...
متن کاملAll Hits All The Time: Parameter Free Calculation of Seed Sensitivity
Standard search techniques for DNA repeats start by identifying seeds, that is, small matching words, that may inhabit larger repeats. Recent innovations in seed structure have led to the development of spaced seeds [8] and indel seeds [9] which are more sensitive than contiguous seeds (also known as k-mers, k-tuples, l-words, etc.). Evaluating seed sensitivity requires 1) specifying a homology...
متن کاملParameter-free online learning via model selection
We introduce an efficient algorithmic framework for model selection in online learning, also known as parameter-free online learning. Departing from previous work, which has focused on highly structured function classes such as nested balls in Hilbert space, we propose a generic meta-algorithm framework that achieves online model selection oracle inequalities under minimal structural assumption...
متن کاملdetermination of maximal singularity free zones in the workspace of parallel manipulator
due to the limiting workspace of parallel manipulator and regarding to finding the trajectory planning of singularity free at workspace is difficult, so finding a best solution that can develop a technique to determine the singularity-free zones in the workspace of parallel manipulators is highly important. in this thesis a simple and new technique are presented to determine the maximal singula...
15 صفحه اولSimultaneous Model Selection and Optimization through Parameter-free Stochastic Learning
Stochastic gradient descent algorithms for training linear and kernel predictors are gaining more andmore importance, thanks to their scalability. While various methods have been proposed to speed up theirconvergence, the model selection phase is often ignored. In fact, in theoretical works most of the timeassumptions are made, for example, on the prior knowledge of the norm of ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Algorithms for Molecular Biology
سال: 2017
ISSN: 1748-7188
DOI: 10.1186/s13015-017-0092-1