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At least 883 records · Page 49Linked to original sources
PubMed1988

Further properties of random walks on diagrams (graphs) with and without cycles.

Three problems are considered. The first is the relation between ensemble-averaged state probabilities in a random walk with absorption and time-averaged state probabilities in the corresponding closed diagram. The second problem is concerned with random walks on diagrams with cycles in which the cycle completion rates and probabilities may depend on the "remainder" after the previously completed cycle. The final topic is a study of cycle completions prior to absorption for diagrams that involve both cycles and absorption (e.g., a cycling enzyme that binds a dead-end inhibitor or poison in one of its states).

Biometry↗
PubMed2003

Statistics and graphs for heart rate variability: pNN50 or pNN20?

Two measures of heart rate variability, pNN50 and pNN20, are compared. A non-linear transformation of these measures is proposed, that is helpful in understanding their interrelationship. Provided a valid statistical test is employed, results from analysing pNN20 are likely to be equivalent to those from analysing pNN50. Arguments recently made by Mietus et al (2002 Heart 88 378-80) against the commonly used pNN50 are unconvincing.

Data Interpretation, Statistical↗
PubMed2006

Scaling graphs of heart rate time series in athletes demonstrating the VLF, LF and HF regions.

Scaling analysis of heart rate time series has emerged as a useful tool for the assessment of autonomic cardiac control. We investigate the heart rate time series of ten athletes (five males and five females), by applying detrended fluctuation analysis (DFA). High resolution ECGs are recorded under standardized resting conditions over 30 min and subsequently heart rate time series are extracted and artifacts filtered. We find three distinct regions of scale invariance, which correspond to the well-known VLF, LF and HF bands in the power spectra of heart rate variability. The scaling exponents alpha are alpha(HF): 1.15 [0.96-1.22], alpha(LF): 0.68 [0.57-0.84], alpha(VLF): 0.83[0.82-0.99], p < 10(-5)). In conclusion, DFA scaling exponents of heart rate time series should be fitted to the VLF, LF and HF ranges, respectively.

Adult↗
PubMed1997

Efficient discovery of conserved patterns using a pattern graph.

MOTIVATION: We have previously reported an algorithm for discovering patterns conserved in sets of related unaligned protein sequences. The algorithm was implemented in a program called Pratt. Pratt allows the user to define a class of patterns (e.g. the degree of ambiguity allowed and the length and number of gaps), and is then guaranteed to find the conserved patterns in this class scoring highest according to a defined fitness measure. In many cases, this version of Pratt was very efficient, but in other cases it was too time consuming to be applied. Hence, a more efficient algorithm was needed. RESULTS: In this paper, we describe a new and improved searching strategy that has two main advantages over the old strategy. First, it allows for easier integration with programs for multiple sequence alignment and data base search. Secondly, it makes it possible to use branch-and-bound search, and heuristics, to speed up the search. The new search strategy has been implemented in a new version of the Pratt program.

Algorithms↗
PubMed1998

SSMAL: similarity searching with alignment graphs.

MOTIVATION: We want to provide biologists with a fast and sensitive scanning tool for searching local alignments of a protein query sequence against databases of protein multiple alignments, such as ProDom. Conversely, we want to provide a tool for locally aligning a protein multiple alignment query against a protein database such as SWISSPROT. RESULTS: We developed the program SSMAL (Shuffling Similarities with Multiple Alignments) which utilizes features of the Blast (Altschul et al., J. Mol. Biol., 215, 403-410, 1990) algorithm and part of the Blast code. Our software allows both scanning of multiple alignments and searching with a multiple alignment. Deletions in the multiple alignment only are handled and a SSMAL search may miss some similarities found by a profile search. However, an SSMAL scan of a database such as ProDom would be 20-30 times faster that a profile scan. In the worst case, a SSMAL search is approximately 9 times faster than a profile search. AVAILABILITY: http://www.dkfz-heidelberg.de/tbi/ people/nicodeme and follow the hyperlink SSMAL. CONTACT: p.nicodeme@DKFZ-Heidelberg.de

Amino Acid Sequence↗
PubMed2005

Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps.

MOTIVATION: Determining protein function is one of the most important problems in the post-genomic era. For the typical proteome, there are no functional annotations for one-third or more of its proteins. Recent high-throughput experiments have determined proteome-scale protein physical interaction maps for several organisms. These physical interactions are complemented by an abundance of data about other types of functional relationships between proteins, including genetic interactions, knowledge about co-expression and shared evolutionary history. Taken together, these pairwise linkages can be used to build whole-proteome protein interaction maps. RESULTS: We develop a network-flow based algorithm, FunctionalFlow, that exploits the underlying structure of protein interaction maps in order to predict protein function. In cross-validation testing on the yeast proteome, we show that FunctionalFlow has improved performance over previous methods in predicting the function of proteins with few (or no) annotated protein neighbors. By comparing several methods that use protein interaction maps to predict protein function, we demonstrate that FunctionalFlow performs well because it takes advantage of both network topology and some measure of locality. Finally, we show that performance can be improved substantially as we consider multiple data sources and use them to create weighted interaction networks. AVAILABILITY: http://compbio.cs.princeton.edu/function

Algorithms↗