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Pulmonary tuberculosis produces a broad spectrum of radiographic abnormalities. During the primary phase of the disease these include pulmonary consolidation (50%), which often involves the middle or lower lobes or the anterior segment of an upper lobe; cavitation (29%) or pneumatocele formation (12%); segmental or lobar atelectasis (18%); pleural effusion(More)
Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Recent machine learning work in this domain has focused on developing new input space representations for protein sequences, that is, string kernels, some of which give state-of-the-art performance for the binary prediction task of(More)
UNLABELLED We present a large-scale implementation of the Rankprop protein homology ranking algorithm in the form of an openly accessible web server. We use the NRDB40 PSI-BLAST all-versus-all protein similarity network of 1.1 million proteins to construct the graph for the Rankprop algorithm, whereas previously, results were only reported for a database of(More)
In a retrospective evaluation of chest roentgenograms and medical records of 40 patients with non-M tuberculosis (atypical) mycobacterial pulmonary disease, 34 had M avium-intracellulare, five had M kansasii, and one had M fortuitum. The roentgenologic spectrum of disease closely resembled that of M tuberculosis. One third of the patients had predisposing(More)
BACKGROUND Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art(More)
BACKGROUND Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently(More)
Virtually every molecular biologist has searched a protein or DNA sequence database to find sequences that are evolutionarily related to a given query. Pairwise sequence comparison methods--i.e., measures of similarity between query and target sequences--provide the engine for sequence database search and have been the subject of 30 years of computational(More)