2083478
An integrative approach for causal gene identification and gene regulatory pathway inference
MOTIVATION
Gene expression variation can often be linked to certain chromosomal regions and are tightly associated with phenotypic variation such as disease conditions. Inferring the causal genes for the expression variation is of great importance but rather challenging as the linked region generally contains multiple genes. Even when a single candidate gene is proposed, the underlying biological mechanism by which the regulation is enforced remains unknown. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms.


RESULTS
We propose a new approach which aims at achieving the above objectives by integrating genotype information, gene expression, protein-protein interaction, protein phosphorylation, and transcription factor (TF)-DNA binding information. A network based stochastic algorithm is designed to infer the causal genes and identify the underlying regulatory pathways. We first quantitatively verified our method by a test using data generated by yeast knock-out experiments. Over 40% of inferred causal genes are correct, which is significantly better than 10% by random guess. We then applied our method to a recent genome-wide expression variation study in yeast. We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. New potential gene regulatory pathways are generated and presented as a global network.


AVAILABILITY
Source code is available upon request.
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.
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.
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.
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.
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.
4650146
A New Coarse-to-Fine Framework for 3D Brain MR Image Registration
Registration, that is, the alignment of multiple images, has been one of the most challenging problems in the field of computer vision. It also serves as an important role in biomedical image analysis and its applications. Although various methods have been proposed for solving different kinds of registration problems in computer vision, the results are still far from ideal when it comes to real world biomedical image applications. For instance, in order to register 3D brain MR images, current state of the art registration methods use a multi-resolution coarse-to-fine algorithm, which typically involves starting with low resolution images and working progressively through to higher resolutions, with the aim to avoid the local maximum "traps". However, these methods do not always successfully avoid the local maximum. Consequently, various rather sophisticated optimization methods are developed to attack this problem. In this paper, we propose a novel viewpoint on the coarse-to-fine registration, in which coarse and fine images are distinguished by different scales of the objects instead of different resolutions of the images. Based on this new perspective, we develop a new image registration framework by combining the multi-resolution method with novel multi-scale algorithm, which could achieve higher accuracy and robustness on 3D brain MR images. We believe this work has great contribution to biomedical image analysis and related applications.
15877902
Tree logic with recursion and model checking algorithm
Semi-structured data plays an increasingly important role in the exchange of information between globally distributed applications, which invokes renewed interests in typed programming languages that can manipulate tree-like data structures. Tree logic, inherited from ambient logic, is introduced as the formal foundation of related programming language and type systems. In this paper, we introduce recursion into such logic system, which can describe the tree data more clearly and concisely. By making a distinction between proposition and predicate, a concise semantics interpretation for our modal logic is given. We also develop a model checking algorithm for the logic without . operator. The correctness of the algorithm is shown. Such work can be seen as the basis of the semi-structured data processing language and more flexible type system.
15532502
Illumination normalization for face recognition and uneven background correction using total variation based image models
We present a new algorithm for illumination normalization and uneven background correction in images, utilizing the recently proposed TV+L/sup 1/ model: minimizing the total variation of the output cartoon while subject to an L/sup 1/-norm fidelity term. We give intuitive proofs of its main advantages, including the well-known edge preserving capability, minimal signal distortion, and scale-dependent but intensity-independent foreground extraction. We then propose a novel TV-based quotient image model (TVQI) for illumination normalization, an important preprocessing for face recognition under different lighting conditions. Using this model, we achieve 100% face recognition rate on Yale face database B if the reference images are under good lighting condition and 99.45% if not. These results, compared to the average 65% recognition rate of the quotient image model and the average 95% recognition rate of the more recent self quotient image model, show a clear improvement. In addition, this model requires no training data, no assumption on the light source, and no alignment between different images for illumination normalization. We also present the results of the related applications - uneven background correction for cDNA mic roar ray films and digital microscope images. We believe the proposed works can serve important roles in the related fields.
12141135
Construction of classification trees via the classification-hierarchy table
Abstract The classification-tree method developed by Grochtmann and coworkers is a black box testing technique to assist test engineers to construct test cases systematically from the functional specifications, via the construction of classification trees. This paper supplements their studies by proposing a methodology to construct classification trees systematically from given sets of classifications and their associated classes, via the notion of the classification-hierarchy table. The intuition of the classification-hierarchy table is to capture the hierarchical relationship for every pair of distinct classifications from which classification trees can be constructed.
31267474
Skewed Data Partition and Alignment Techniques for Compiling Programs on Distributed Memory Multicomputers
Minimizing data communication over processors is the key to compile programs for distributed memory multicomputers. In this paper, we propose new data partition and alignment techniques for partitioning and aligning data arrays with a program in a way of minimizing communication over processors. We use skewed alignment instead of the dimension-ordered alignment techniques to align data arrays. By developing the skewed scheme, we can solve more complex programs with minimized data communication than that of the dimension-ordered scheme. Finally, the experimental results show that our proposed scheme has more opportunities to align data arrays such that data communications over processors can be minimized.