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PubMed2004

Blind source separation and the analysis of microarray data.

We develop an approach for the exploratory analysis of gene expression data, based upon blind source separation techniques. This approach exploits higher-order statistics to identify a linear model for (logarithms of) expression profiles, described as linear combinations of "independent sources." As a result, it yields "elementary expression patterns" (the "sources"), which may be interpreted as potential regulation pathways. Further analysis of the so-obtained sources show that they are generally characterized by a small number of specific coexpressed or antiexpressed genes. In addition, the projections of the expression profiles onto the estimated sources often provides significant clustering of conditions. The algorithm relies on a large number of runs of "independent component analysis" with random initializations, followed by a search of "consensus sources." It then provides estimates for independent sources, together with an assessment of their robustness. The results obtained on two datasets (namely, breast cancer data and Bacillus subtilis sulfur metabolism data) show that some of the obtained gene families correspond to well known families of coregulated genes, which validates the proposed approach.

Bacillus subtilis↗

PubMed2003

Selection of oligonucleotide probes for protein coding sequences.

MOTIVATION: Large arrays of oligonucleotide probes have become popular tools for analyzing RNA expression. However to date most oligo collections contain poorly validated sequences or are biased toward untranslated regions (UTRs). Here we present a strategy for picking oligos for microarrays that focus on a design universe consisting exclusively of protein coding regions. We describe the constraints in oligo design that are imposed by this strategy, as well as a software tool that allows the strategy to be applied broadly. RESULT: In this work we sequentially apply a variety of simple filters to candidate sequences for oligo probes. The primary filter is a rejection of probes that contain contiguous identity with any other sequence in the sample universe that exceeds a pre-established threshold length. We find that rejection of oligos that contain 15 bases of perfect match with other sequences in the design universe is a feasible strategy for oligo selection for probe arrays designed to interrogate mammalian RNA populations. Filters to remove sequences with low complexity and predicted poor probe accessibility narrow the candidate probe space only slightly. Rejection based on global sequence alignment is performed as a secondary, rather than primary, test, leading to an algorithm that is computationally efficient. Splice isoforms pose unique challenges and we find that isoform prevalence will for the most part have to be determined by analysis of the patterns of hybridization of partially redundant oligonucleotides. AVAILABILITY: The oligo design program OligoPicker and its source code are freely available at our website.

Algorithms↗

PubMed2003

Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions.

MOTIVATION: Two practical realities constrain the analysis of microarray data, mass spectra from proteomics, and biomedical infrared or magnetic resonance spectra. One is the 'curse of dimensionality': the number of features characterizing these data is in the thousands or tens of thousands. The other is the 'curse of dataset sparsity': the number of samples is limited. The consequences of these two curses are far-reaching when such data are used to classify the presence or absence of disease. RESULTS: Using very simple classifiers, we show for several publicly available microarray and proteomics datasets how these curses influence classification outcomes. In particular, even if the sample per feature ratio is increased to the recommended 5-10 by feature extraction/reduction methods, dataset sparsity can render any classification result statistically suspect. In addition, several 'optimal' feature sets are typically identifiable for sparse datasets, all producing perfect classification results, both for the training and independent validation sets. This non-uniqueness leads to interpretational difficulties and casts doubt on the biological relevance of any of these 'optimal' feature sets. We suggest an approach to assess the relative quality of apparently equally good classifiers.

Algorithms↗

PubMed2003

Electrophoretic data classification for phylogenetics and biostatistics.

SUMMARY: This paper presents ClassMaker, a macro of MS Excel able to classify continuous data of molecular weight data as binary (1/0) values. The output is represented by a binary matrix, which can be introduced in every software application for phylogenetics or multivariate statistics. This application is designed in order to be a link between image analysis programs and statistical or phylogenetic applications, in order to produce a complete series of free programs able to carry out the complete analysis from the gel to the dendrogram. AVAILABILITY: ClassMaker is freely available from http://www.agr.unipg.it/cardinali/index.html, where a list of the URLs from which programs of image analysis, statistics and phylogenetics can be freely downloaded.

Algorithms↗

PubMed2004

Mining gene expression data based on template theory.

MOTIVATION: It is understood that clustering genes are useful for exploring scientific knowledge from DNA microarray gene expression data. The explored knowledge can be finally used for annotating biological function for novel genes. Representing the explored knowledge in an efficient manner is then closely related to the classification accuracy. However, this issue has not yet been paid the attention it deserves. RESULT: A novel method based on template theory in cognitive psychology and pattern recognition is developed in this study for representing knowledge extracted from cluster analysis effectively. The basic principle is to represent knowledge according to the relationship between genes and a found cluster structure. Based on this novel knowledge representation method, a pattern recognition algorithm (the decision tree algorithm C4.5) is then used to construct a classifier for annotating biological functions of novel genes. The experiments on five published datasets show that this method has improved the classification performance compared with the conventional method. The statistical tests indicate that this improvement is significant. AVAILABILITY: The software package can be obtained upon request from the author.

Algorithms↗

PubMed2001

Thymidylate synthase as a molecular target for drug discovery using the National Cancer Institute's Anticancer Drug Screen.

Thymidylate synthase (TS) is a critical cellular target for cancer chemotherapeutics, particularly the fluoropyrimidine and antifolate classes of antineoplastic agents. One of the primary mechanisms of clinical insensitivity to these agents is through the overexpression of the target enzyme, TS. Thus, there is a need for the development of agents which selectively target TS-overexpressing malignant cells. To this end, we conducted a search for agents which potentially selectively target TS-overexpressing cells using two separate algorithms for identifying such compounds in the NCI Drug Repository by comparing cytotoxicity profiles of 30000 compounds with the TS expression levels measured by Western blot analysis in 53 cell lines. Using the traditional COMPARE analysis we were unable to identify compounds which maintain a selective ability to kill high TS-expressing cells in a subsequent four cell line validation assay. A new algorithm, termed COMPARE Effect Clusters analysis, enabled the identification of a particular drug cluster which contained compounds that maintained a selective ability to kill TS-overexpressing cell lines in the validation assay. While the identified compounds were selectively cytotoxic to TS-overexpressing cells, we found that they were not specifically targeting TS as a mechanism of action. Apparently, the overexpression of TS was providing a marker for sensitivity. This identified class of compounds which appears to be selectively cytotoxic against cells which overexpress TS may be useful for the development of therapeutics for those whose cancers overexpress TS de novo.

Antineoplastic Agents↗

PubMed2006

Systematic identification of functional orthologs based on protein network comparison.

Annotating protein function across species is an important task that is often complicated by the presence of large paralogous gene families. Here, we report a novel strategy for identifying functionally related proteins that supplements sequence-based comparisons with information on conserved protein-protein interactions. First, the protein interaction networks of two species are aligned by assigning proteins to sequence homology clusters using the Inparanoid algorithm. Next, probabilistic inference is performed on the aligned networks to identify pairs of proteins, one from each species, that are likely to retain the same function based on conservation of their interacting partners. Applying this method to Drosophila melanogaster and Saccharomyces cerevisiae, we analyze 121 cases for which functional orthology assignment is ambiguous when sequence similarity is used alone. In 61 of these cases, the network supports a different protein pair than that favored by sequence comparisons. These results suggest that network analysis can be used to provide a key source of information for refining sequence-based homology searches.

Animals↗

PubMed2006

Detecting degree symmetries in networks.

The surrounding of a vertex in a network can be more or less symmetric. We derive measures of a specific kind of symmetry of a vertex which we call degree symmetry--the property that many paths going out from a vertex have overlapping degree sequences. These measures are evaluated on artificial and real networks. Specifically we consider vertices in the human metabolic network. We also measure the average degree-symmetry coefficient for different classes of real-world network. We find that most studied examples are weakly positively degree symmetric. The exceptions are an airport network (having a negative degree-symmetry coefficient) and one-mode projections of social affiliation networks that are rather strongly degree symmetric.

Algorithms↗

PubMed2004

Classification of anatomical structures in MR brain images using fuzzy parameters.

We present an algorithm that automatically segments and classifies the brain structures in a set of magnetic resonance (MR) brain images using expert information contained in a small subset of the image set. The algorithm is intended to do the segmentation and classification tasks mimicking the way a human expert would reason. The algorithm uses a knowledge base taken from a small subset of semiautomatically classified images that is combined with a set of fuzzy indexes that capture the experience and expectation a human expert uses during recognition tasks. The fuzzy indexes are tissue specific and spatial specific, in order to consider the biological variations in the tissues and the acquisition inhomogeneities through the image set. The brain structures are segmented and classified one at a time. For each brain structure the algorithm needs one semiautomatically classified image and makes one pass through the image set. The algorithm uses low-level image processing techniques on a pixel basis for the segmentations, then validates or corrects the segmentations, and makes the final classification decision using higher level criteria measured by the set of fuzzy indexes. We use single-echo MR images because of their high volumetric resolution; but even though we are working with only one image per brain slice, we have multiple sources of information on each pixel: absolute and relative positions in the image, gray level value, statistics of the pixel and its three-dimensional neighborhood and relation to its counterpart pixels in adjacent images. We have validated our algorithm for ease of use and precision both with clinical experts and with measurable error indexes over a Brainweb simulated MR set.

Algorithms↗

PubMed2004

Constructing diffeomorphic representations for the groupwise analysis of nonrigid registrations of medical images.

Groupwise nonrigid registrations of medical images define dense correspondences across a set of images, defined by a continuous deformation field that relates each target image in the group to some reference image. These registrations can be automatic, or based on the interpolation of a set of user-defined landmarks, but in both cases, quantifying the normal and abnormal structural variation across the group of imaged structures implies analysis of the set of deformation fields. We contend that the choice of representation of the deformation fields is an integral part of this analysis. This paper presents methods for constructing a general class of multi-dimensional diffeomorphic representations of deformations. We demonstrate, for the particular case of the polyharmonic clamped-plate splines, that these representations are suitable for the description of deformations of medical images in both two and three dimensions, using a set of two-dimensional annotated MRI brain slices and a set of three-dimensional segmented hippocampi with optimized correspondences. The class of diffeomorphic representations also defines a non-Euclidean metric on the space of patterns, and, for the case of compactly supported deformations, on the corresponding diffeomorphism group. In an experimental study, we show that this non-Euclidean metric is superior to the usual ad hoc Euclidean metrics in that it enables more accurate classification of legal and illegal variations.

Algorithms↗

PubMed2004

A method for modeling noise in medical images.

We have developed a method to study the statistical properties of the noise found in various medical images. The method is specifically designed for types of noise with uncorrelated fluctuations. Such signal fluctuations generally originate in the physical processes of imaging rather than in the tissue textures. Various types of noise (e.g., photon, electronics, and quantization) often contribute to degrade medical images; the overall noise is generally assumed to be additive with a zero-mean, constant-variance Gaussian distribution. However, statistical analysis suggests that the noise variance could be better modeled by a nonlinear function of the image intensity depending on external parameters related to the image acquisition protocol. We present a method to extract the relationship between an image intensity and the noise variance and to evaluate the corresponding parameters. The method was applied successfully to magnetic resonance images with different acquisition sequences and to several types of X-ray images.

Algorithms↗

PubMed2004

Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels.

Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.

Aged↗

PubMed2005

Prospective motion correction of X-ray images for coronary interventions.

A method for prospective motion correction of X-ray imaging of the heart is presented. A 3D + t coronary model is reconstructed from a biplane coronary angiogram obtained during free breathing. The deformation field is parameterized by cardiac and respiratory phase, which enables the estimation of the state of the arteries at any phase of the cardiac-respiratory cycle. The motion of the three-dimensional (3-D) coronary model is projected onto the image planes and used to compute a dewarping function for motion correcting the images. The use of a 3-D coronary model facilitates motion correction of images acquired with the X-ray system at arbitrary orientations. The performance of the algorithm was measured by tracking the motion of selected left coronary landmarks using a template matching cross-correlation. In three patients, we motion corrected the same images used to construct their 3D + t coronary model. In this best case scenario, the algorithm reduced the motion of the landmarks by 84%-85%, from mean RMS displacements of 12.8-14.6 pixels to 2.1-2.2 pixels. Prospective motion correction was tested in five patients by building the coronary model from one dataset, and correcting a second dataset. The patient's cardiac and respiratory phase are monitored and used to calculate the appropriate correction parameters. The results showed a 48%-63% reduction in the motion of the landmarks, from a mean RMS displacement of 11.5-13.6 pixels to 4.4-7.1 pixels.

Algorithms↗

PubMed2005

Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft kappa-NN ensemble.

Most classical template-based frontal face recognition techniques assume that multiple images per person are available for training, while in many real-world applications only one training image per person is available and the test images may be partially occluded or may vary in expressions. This paper addresses those problems by extending a previous local probabilistic approach presented by Martinez, using the self-organizing map (SOM) instead of a mixture of Gaussians to learn the subspace that represented each individual. Based on the localization of the training images, two strategies of learning the SOM topological space are proposed, namely to train a single SOM map for all the samples and to train a separate SOM map for each class, respectively. A soft kappa nearest neighbor (soft kappa-NN) ensemble method, which can effectively exploit the outputs of the SOM topological space, is also proposed to identify the unlabeled subjects. Experiments show that the proposed method exhibits high robust performance against the partial occlusions and variant expressions.

Algorithms↗

PubMed2002

Gene expression during the life cycle of Drosophila melanogaster.

Molecular genetic studies of Drosophila melanogaster have led to profound advances in understanding the regulation of development. Here we report gene expression patterns for nearly one-third of all Drosophila genes during a complete time course of development. Mutations that eliminate eye or germline tissue were used to further analyze tissue-specific gene expression programs. These studies define major characteristics of the transcriptional programs that underlie the life cycle, compare development in males and females, and show that large-scale gene expression data collected from whole animals can be used to identify genes expressed in particular tissues and organs or genes involved in specific biological and biochemical processes.

Algorithms↗

PubMed2004

Predicting spatial data with RBF networks.

Spatial prediction needs to account for spatial information, which makes conventional radial basis function (RBF) networks inappropriate, for they assume independent and identical distribution. In this paper, we fuse spatial information at different layers of RBF. Experiments show fusion at hidden layer gives the best result and suggest that the optimal value is around one for the coefficient, which is used in the linear combination at the output layer.

Algorithms↗

PubMed2004

Fair attribution of functional contribution in artificial and biological networks.

This letter presents the multi-perturbation Shapley value analysis (MSA), an axiomatic, scalable, and rigorous method for deducing causal function localization from multiple perturbations data. The MSA, based on fundamental concepts from game theory, accurately quantifies the contributions of network elements and their interactions, overcoming several shortcomings of previous function localization approaches. Its successful operation is demonstrated in both the analysis of a neurophysiological model and of reversible deactivation data. The MSA has a wide range of potential applications, including the analysis of reversible deactivation experiments, neuronal laser ablations, and transcranial magnetic stimulation "virtual lesions," as well as in providing insight into the inner workings of computational models of neurophysiological systems.

Algorithms↗

PubMed2005

Robust detection of periodic time series measured from biological systems.

BACKGROUND: Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications. Furthermore, the observed time series can exhibit other non-idealities, such as outliers, short length and distortion from the original wave form. Hence, the computational methods should preferably be robust against such anomalies in the data. RESULTS: We propose a general-purpose robust testing procedure for finding periodic sequences in multiple time series data. The proposed method is based on a robust spectral estimator which is incorporated into the hypothesis testing framework using a so-called g-statistic together with correction for multiple testing. This results in a robust testing procedure which is insensitive to heavy contamination of outliers, missing-values, short time series, nonlinear distortions, and is completely insensitive to any monotone nonlinear distortions. The performance of the methods is evaluated by performing extensive simulations. In addition, we compare the proposed method with another recent statistical signal detection estimator that uses Fisher's test, based on the Gaussian noise assumption. The results demonstrate that the proposed robust method provides remarkably better robustness properties. Moreover, the performance of the proposed method is preferable also in the standard Gaussian case. We validate the performance of the proposed method on real data on which the method performs very favorably. CONCLUSION: As the time series measured from biological systems are usually short and prone to contain different kinds of non-idealities, we are very optimistic about the multitude of possible applications for our proposed robust statistical periodicity detection method.

Algorithms↗