10812503
Mapping gene ontology to proteins based on protein-protein interaction data
MOTIVATION
Gene Ontology (GO) consortium provides structural description of protein function that is used as a common language for gene annotation in many organisms. Large-scale techniques have generated many valuable protein-protein interaction datasets that are useful for the study of protein function. Combining both GO and protein-protein interaction data allows the prediction of function for unknown proteins.


RESULT
We apply a Markov random field method to the prediction of yeast protein function based on multiple protein-protein interaction datasets. We assign function to unknown proteins with a probability representing the confidence of this prediction. The functions are based on three general categories of cellular component, molecular function and biological process defined in GO. The yeast proteins are defined in the Saccharomyces Genome Database (SGD). The protein-protein interaction datasets are obtained from the Munich Information Center for Protein Sequences (MIPS), including physical interactions and genetic interactions. The efficiency of our prediction is measured by applying the leave-one-out validation procedure to a functional path matching scheme, which compares the prediction with the GO description of a protein's function from the abstract level to the detailed level along the GO structure. For biological process, the leave-one-out validation procedure shows 52% precision and recall of our method, much better than that of the simple guilty-by-association methods.
2057857
Gene-finding via tandem mass spectrometry
We propose a new gene-finding methodology that combines high performance liquid chromatograph (HPLC)-tandem mass spectrometry experiments with a fast computer algorithm to locate coding regions and introns. Proteins are first extracted from cells and digested by enzymes, and then the resulting peptides are separated and analyzed by HPLC-tandem mass spectrometry. We designed an algorithm to find DNA coding sequences, corresponding to open reading frames (ORF), in the genome such that their translated amino acid sequences are optimally correlated with these tandem mass spectra. In this algorithm, we also allow one gap, corresponding to an intron, between two DNA coding sequences, such that their concatenation becomes one coding sequence. Finally, the algorithm assembles these candidate coding sequences and introns into gene structures. Our algorithm was implemented to predict genes on 4 contigs with a total of 123 kbps using two sets of simulated digestion- HPLC-tandem mass spectrometry data of 2523 Caenorhabditis elegans Chromosome IV proteins, digested by trypsin and Asp-N respectively. Among 15 annotated genes in the forward strand, all 98 exons are hit by the predicted no-gap coding sequences, and 60 out of 83 introns are correctly predicted. We also tested gene structure prediction in a contig containing 3 genes. Combining splicing site predictions with predicted coding sequences and introns, we found all 3 gene structures.
8315237
Local Lazy Regression: Making Use of the Neighborhood to Improve QSAR Predictions
Traditional quantitative structure-activity relationship (QSAR) models aim to capture global structure-activity trends present in a data set. In many situations, there may be groups of molecules which exhibit a specific set of features which relate to their activity or inactivity. Such a group of features can be said to represent a local structure-activity relationship. Traditional QSAR models may not recognize such local relationships. In this work, we investigate the use of local lazy regression (LLR), which obtains a prediction for a query molecule using its local neighborhood, rather than considering the whole data set. This modeling approach is especially useful for very large data sets because no a priori model need be built. We applied the technique to three biological data sets. In the first case, the root-mean-square error (RMSE) for an external prediction set was 0.94 log units versus 0.92 log units for the global model. However, LLR was able to characterize a specific group of anomalous molecules with much better accuracy (0.64 log units versus 0.70 log units for the global model). For the second data set, the LLR technique resulted in a decrease in RMSE from 0.36 log units to 0.31 log units for the external prediction set. In the third case, we obtained an RMSE of 2.01 log units versus 2.16 log units for the global model. In all cases, LLR led to a few observations being poorly predicted compared to the global model. We present an analysis of why this was observed and possible improvements to the local regression approach.
31567164
Prediction of protein function using protein-protein interaction data
Assigning functions to novel proteins is one of the most important problems in the post-genomic era. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database, using the protein-protein interaction data from the Munich Information Center for Protein Sequences. We show that our approach outperforms other available methods for function prediction based on protein interaction data.
10712046
Algorithms for identifying protein cross-links via tandem mass spectrometry
Cross-linking technology combined with tandem mass spectrometry (MS-MS) is a powerful method that provides a rapid solution to the discovery of protein-protein interactions and protein structures. We studied the problem of detecting cross-linked peptides and cross-linked amino acids from tandem mass spectral data. Our method consists of two steps: the first step finds two protein subsequences whose mass sum equals a given mass measured from the mass spectrometry; and the second step finds the best cross-linked amino acids in these two peptide sequences that are optimally correlated to a given tandem mass spectrum. We designed fast and space-efficient algorithms for these two steps, and implemented and tested them on experimental data of cross-linked Hemoglobin proteins.
2822926
Sorting with Fixed-length Reversals
Abstract A popular puzzle TOP-SPIN consists of a permutation of 20 numbered disks on an oval track, with a turnstile capable of reversing a string of 4 consecutive disks. The goal is to sort the disks into identity permutation using reversals. We consider the more general case of sorting n element permutations with a turnstile of size k. The problem of computing the reversal distances is of considerable importance in reconstructing the evolutionary history of the genome. The minimum reversal sequence sorting the one genome to another corresponds to the most likely evolutionary path between them. We give a complete solution for all n and k, of the number of equivalence classes of n-permutations under k-reversal, for both permutations and circular permutations. We also prove the upper and lower bounds on the reversal distances between two permutations.
1080835
Region Based Hidden Markov Random Field Model for Brain MR Image Segmentation
In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms. Keywords—Finite Gaussian mixture model, Hidden Markov random field model, image segmentation, MRI.
1816267
From Region Encoding To Extended Dewey: On Efficient Processing of XML Twig Pattern Matching
Finding all the occurrences of a twig pattern in an XML database is a core operation for efficient evaluation of XML queries. A number of algorithms have been proposed to process a twig query based on region encoding labeling scheme. While region encoding supports efficient determination of structural relationship between two elements, we observe that the information within a single label is very limited. In this paper, we propose a new labeling scheme, called extended Dewey. This is a powerful labeling scheme, since from the label of an element alone, we can derive all the elements names along the path from the root to the element. Based on extended Dewey, we design a novel holistic twig join algorithm, called TJFast. Unlike all previous algorithms based on region encoding, to answer a twig query, TJFast only needs to access the labels of the leaf query nodes. Through this, not only do we reduce disk access, but we also support the efficient evaluation of queries with wildcards in branching nodes, which is very difficult to be answered by algorithms based on region encoding. Finally, we report our experimental results to show that our algorithms are superior to previous approaches in terms of the number of elements scanned, the size of intermediate results and query performance.
23513255
A public verifiable copy protection technique for still images
It is well known that a digital signature scheme and digital timestamp are two well-defined security methods for copyright protection. However, these techniques are not suitable for directly dealing with digital images. For this reason, digital watermarking has received considerable attention for copyright protection. Unfortunately, there are still some challenges in the proposed watermarking schemes. Here, we will introduce cryptographic tools into watermarking, in such a way that both the advantages of cryptographic tools and digital watermarking are available. The publicly verifiable property and tolerant distortion property are therefore possible. Based on this idea, a lossless and robust copyright-protection scheme, distinct from conventional watermarking techniques, is proposed. Our scheme is strong enough to resist common image processing, geometric distortions and some intentional attacks. Some of the attacks are still challenges for the proposed watermarking methods. A series of experiments are conducted to prove the robustness property. The StirMark and unZign attacks are adopted as benchmark verification.
1214419
On the Axiomatizability of Priority
This paper studies the equational theory of bisimulation equivalence over the process algebra BCCSP extended with the priority operator of Baeten, Bergstra and Klop. It is proven that, in the presence of an infinite set of actions, bisimulation equivalence has no finite, sound, ground-complete equational axiomatization over that language. This negative result applies even if the syntax is extended with an arbitrary collection of auxiliary operators, and motivates the study of axiomatizations using conditional equations. In the presence of an infinite set of actions, it is shown that, in general, bisimulation equivalence has no finite, sound, ground-complete axiomatization consisting of conditional equations over BCCSP. Sufficient conditions on the priority structure over actions are identified that lead to a finite, ground-complete axiomatization of bisimulation equivalence using conditional equations
17432425
Structure analysis for dynamic software architecture
The open and dynamic Internet environment greatly urges software entities that are distributed on different locations to coordinate with each other to accomplish a computing task. Software architecture is applied to abstract the software entities to be components and the coordination between them to be connectors and then a model is extracted as the architecture on which the design, analysis and verification are based. Currently, the notion of dynamic software architectures that can modify their architecture and enact modifications during the system execution has become one of the most active research areas. In this paper, we focus on the dynamic evolution of system structure other than coordination mechanisms (e.g. communication protocols). It is widely recognized that some restrictions should be imposed on the system evolution to ensure that the system structure may remain one style or transform within a scope. These conditions, to a large extent, make the system execute under control as expected.