IGLOSS: iterative gapless local similarity search

Gapped Local Similarity Search with Provable Guarantees

We present a program qhash, based on q-gram filtration and high-dimensional search, to find gapped local similarities between two sequences. Our approach differs from past q-gram-based approaches in two main aspects. Our filtration step uses algorithms for a sparse all-pairs problem, while past studies use suffix-tree-like structures and counters. Our program works in sequence-sequence mode, wh...

Robust Iterative Quantization for Efficient ℓp-norm Similarity Search

Iterative Quantization (ITQ) is one of the most successful hashing based nearest-neighbor search methods for large-scale information retrieval in the past a few years due to its simplicity and superior performance. However, the performance of this algorithm degrades significantly when dealing with noisy data. Additionally, it can barely facilitate a wide range of applications as the distortion ...

Gapless Local Alignment of Multiple Sequences

Martin C. Frith Joel H. Graber [email protected] [email protected] Zhiping Weng John L. Spouge [email protected] [email protected] 1 Bioinformatics Program, Boston University, Boston, MA 02215, U.S.A. 2 Biomedical Engineering Department, Boston University, Boston, MA 02215, U.S.A. 3 National Center for Biotechnology Information, National Library of Medicine, Building 38A, Bethesda, MD 20894, U.S.A.

Learning Decision Rules by Randomized Iterative Local Search

Learning easily understandable decision rules from examples is one of the classic problems in machine learning. Most learning systems for this problem employ some variation of a greedy separate-and-conquer algorithm, which makes the rules order-dependent, and hence difficult to understand. In this paper, we describe a system called LERILS that learns highly accurate and comprehensible rules fro...

Local Graph Patterns for Scientific Workflow Similarity Search