We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.

/pdf/protein-protein-interaction-detection-methods-and-analysis-1onwam4ehr.pdf

Protein-Protein Interaction Detection: Methods and Analysis

Motivation: Protein–protein interaction (PPI) networks are powerful models to represent the pairwise protein interactions of the organisms. Clustering PPI networks can be useful for isolating groups of interacting proteins that participate in the same biological processes or that perform together specific biological functions. Evolutionary orthologies can be inferred this way, as well as functions and properties of yet uncharacterized proteins. Results: We present an overview of the main state-of-the-art clustering methods that have been applied to PPI networks over the past decade. We distinguish five specific categories of approaches, describe and compare their main features and then focus on one of them, i.e. population-based stochastic search. We provide an experimental evaluation, based on some validation measures widely used in the literature, of techniques in this class, that are as yet less explored than the others. In particular, we study how the capability of Genetic Algorithms (GAs) to extract clusters in PPI networks varies when different topology-based fitness functions are used, and we compare GAs with the main techniques in the other categories. The experimental campaign shows that predictions returned by GAs are often more accurate than those produced by the contestant methods. Interesting issues still remain open about possible generalizations of GAs allowing for cluster overlapping. Availability and implementation: We point out which methods and tools described here are publicly available. Contact: simona.rombo@math.unipa.it Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/algorithms-and-tools-for-protein-protein-interaction-2qxec1ajri.pdf

Algorithms and tools for protein-protein interaction networks clustering, with a special focus on population-based stochastic methods.

The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM. STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL) algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches. STM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards.

/pdf/a-novel-functional-module-detection-algorithm-for-protein-1au3dcnk80.pdf

A novel functional module detection algorithm for protein-protein interaction networks

With completion of a draft sequence of the human genome, the field of gen etics stands on the threshold of significant theoretical and practical advances. Crucial to furthering these investigations is a comprehensive understanding of the expression, function, and regulation of the proteins encoded by an organism. It has been observed that proteins seldom act as single isolated species in the performance of their functions; rather, proteins involved in the sam e cellular processes often interact with each other. Therefore, the functions of uncharacterized proteins can be predicted through comparison with the interactions of similar known proteins. A detailed examination of the protein-protein interaction (PPI) network can thus yield significant new un derstanding of protein function. Clustering is the process of grouping data objects into sets (cluste rs) which demonstrate greater similarity among objects in the same cluster than in different clusters . Clustering in the PPI network context groups together proteins which share a larger number of interactions. The results of this process can illuminate the structure of the PPI network and suggest possible functions for members of the cluster which were previously uncharacterized. This chapter will begin with a brief introduction of the properties of protein-p rotein interaction networks, including a review of the data which has been generated by both experimental and computational approaches. A variety of methods which have been employed to cluster these networks will then be presented. These approaches are broadly characterized as either distance-based or

/pdf/clustering-methods-in-protein-protein-interaction-network-39g64brn31.pdf

Clustering methods in protein-protein interaction network

The recent high-throughput bio-techniques have provided us large-scale protein-protein interaction data through systematic identification of physical and genetic interactions among all proteins in an organism. Several previous studies have shown that using protein-protein interaction networks to predict protein function is a big step toward full understanding of the mechanisms of cells. However, the protein-protein interaction data derived from high-throughput experiments are typically very noisy, which presents great challenges to the existing methods. In this paper, we propose a novel common-neighbor-based model and a Bayesian framework to predict protein function on the basis of the small-world property of the protein-protein interaction network. We tested our approach on five data sets from various sources. The experimental results have shown that our approach has a better performance than several representative methods in terms of both precision and recall. In addition, our method is particularly effective to handle the high false-positive and false-negative rates in protein-protein interaction data

Prediction of Protein Function Using Common-Neighbors in Protein-Protein Interaction Networks

Clustering Methods in a Protein–Protein Interaction Network

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene–sample–time microarray data sets that records the expression levels of various genes under a set of samples during a series of time points. In particular, we propose the mining of coherent gene clusters from such data sets. Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. The coherent gene clusters may identify the samples corresponding to some phenotypes (e.g., diseases), and suggest the candidate genes correlated to the phenotypes. We present two efficient algorithms, namely the Sample-Gene Search and the Gene–Sample Search, to mine the complete set of coherent gene clusters. We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets. The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters.

/pdf/mining-gene-sample-time-microarray-data-a-coherent-gene-1sjit8nh5w.pdf

Chuan Lin

Papers

Clustering methods in protein-protein interaction network

Prediction of Protein Function Using Common-Neighbors in Protein-Protein Interaction Networks

Clustering Methods in a Protein–Protein Interaction Network

Mining gene–sample–time microarray data: a coherent gene cluster discovery approach