Propagation-Based Biclustering Algorithm for Extracting Inclusion-Maximal Motifs

Biclustering, which is simultaneous clustering of columns and rows in data matrix, became an issue when classical clustering algorithms proved not to be good enough to detect similar expressions of genes under subset of conditions. Biclustering algorithms may be also applied to different datasets, such as medical, economical, social networks etc. In this article we explain the concept beneath hybrid biclustering algorithms and present details of propagation-based biclustering, a novel approach for extracting inclusion-maximal gene expression motifs conserved in gene microarray data. We prove that this approach may successfully compete with other well-recognized biclustering algorithms

Plsi: A Computational Software Pipeline For Pathway Level Disease Subtype Identification

It is accepted that many complex diseases, like cancer, consist in collections of distinct genetic diseases. Clinical advances in treatments are attributed to molecular treatments aimed at specific genes resulting in greater ecacy and fewer debilitating side effects. This proves that it is important to identify and appropriately treat each individual disease subtype. Our current understanding of subtypes is limited: despite targeted treatment advances, targeted therapies often fail for some patients. The main limitation of current methods for subtype identification is that they focus on gene expression, and they are subject to its intrinsic noise. Signaling pathways describe biological processes that are carried out by networks of genes interacting with each other. We developed PLSI, a software that allows to identify the specific pathways impacted in individual patients, subgroups of patients, or a given subtype of disease. The expected impact includes a better understanding of disease and resistance to treatment

Construction of gene regulatory networks using biclustering and bayesian networks

<p>Abstract</p> <p>Background</p> <p>Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling.</p> <p>Results</p> <p>In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method.</p> <p>Conclusions</p> <p>Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.</p

Mining Biological Networks towards Protein complex Detection and Gene-Disease Association

Large amounts of biological data are continuously generated nowadays, thanks to the advancements of high-throughput experimental techniques. Mining valuable knowledge from such data still motivates the design of suitable computational methods, to complement the experimental work which is often bound by considerable time and cost requirements. Protein complexes or groups of interacting proteins, are key players in most cellular events. The identification of complexes not only allows to better understand normal biological processes but also to uncover Disease-triggering malfunctions. Ultimately, findings in this research branch can highly enhance the design of effective medical treatments. The aim of this research is to detect protein complexes in protein-protein interaction networks and to associate the detected entities to diseases. The work is divided into three main objectives: first, develop a suitable method for the identification of protein complexes in static interaction networks; second, model the dynamic aspect of protein interaction networks and detect complexes accordingly; and third, design a learning model to link proteins, and subsequently protein complexes, to diseases. In response to these objectives, we present, ProRank+, a novel complex-detection approach based on a ranking algorithm and a merging procedure. Then, we introduce DyCluster, which uses gene expression data, to model the dynamics of the interaction networks, and we adapt the detection algorithm accordingly. Finally, we integrate network topology attributes and several biological features of proteins to form a classification model for gene-disease association. The reliability of the proposed methods is supported by various experimental studies conducted to compare them with existing approaches. Pro Rank+ detects more protein complexes than other state-of-the-art methods. DyCluster goes a step further and achieves a better performance than similar techniques. Then, our learning model shows that combining topological and biological features can greatly enhance the gene-disease association process. Finally, we present a comprehensive case study of breast cancer in which we pinpoint disease genes using our learning model; subsequently, we detect favorable groupings of those genes in a protein interaction network using the Pro-rank+ algorithm

Development of Biclustering Techniques for Gene Expression Data Modeling and Mining

The next-generation sequencing technologies can generate large-scale biological data with higher resolution, better accuracy, and lower technical variation than the arraybased counterparts. RNA sequencing (RNA-Seq) can generate genome-scale gene expression data in biological samples at a given moment, facilitating a better understanding of cell functions at genetic and cellular levels. The abundance of gene expression datasets provides an opportunity to identify genes with similar expression patterns across multiple conditions, i.e., co-expression gene modules (CEMs). Genomescale identification of CEMs can be modeled and solved by biclustering, a twodimensional data mining technique that allows clustering of rows and columns in a gene expression matrix, simultaneously. Compared with traditional clustering that targets global patterns, biclustering can predict local patterns. This unique feature makes biclustering very useful when applied to big gene expression data since genes that participate in a cellular process are only active in specific conditions, thus are usually coexpressed under a subset of all conditions. The combination of biclustering and large-scale gene expression data holds promising potential for condition-specific functional pathway/network analysis. However, existing biclustering tools do not have satisfied performance on high-resolution RNA-Seq data, majorly due to the lack of (i) a consideration of high sparsity of RNA-Seq data, especially for scRNA-Seq data, and (ii) an understanding of the underlying transcriptional regulation signals of the observed gene expression values. QUBIC2, a novel biclustering algorithm, is designed for large-scale bulk RNA-Seq and single-cell RNA-seq (scRNA-Seq) data analysis. Critical novelties of the algorithm include (i) used a truncated model to handle the unreliable quantification of genes with low or moderate expression; (ii) adopted the Gaussian mixture distribution and an information-divergency objective function to capture shared transcriptional regulation signals among a set of genes; (iii) utilized a Dual strategy to expand the core biclusters, aiming to save dropouts from the background; and (iv) developed a statistical framework to evaluate the significances of all the identified biclusters. Method validation on comprehensive data sets suggests that QUBIC2 had superior performance in functional modules detection and cell type classification. The applications of temporal and spatial data demonstrated that QUBIC2 could derive meaningful biological information from scRNA-Seq data. Also presented in this dissertation is QUBICR. This R package is characterized by an 82% average improved efficiency compared to the source C code of QUBIC. It provides a set of comprehensive functions to facilitate biclustering-based biological studies, including the discretization of expression data, query-based biclustering, bicluster expanding, biclusters comparison, heatmap visualization of any identified biclusters, and co-expression networks elucidation. In the end, a systematical summary is provided regarding the primary applications of biclustering for biological data and more advanced applications for biomedical data. It will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency

Data Mining Using the Crossing Minimization Paradigm

Our ability and capacity to generate, record and store multi-dimensional, apparently unstructured data is increasing rapidly, while the cost of data storage is going down. The data recorded is not perfect, as noise gets introduced in it from different sources. Some of the basic forms of noise are incorrect recording of values and missing values. The formal study of discovering useful hidden information in the data is called Data Mining. Because of the size, and complexity of the problem, practical data mining problems are best attempted using automatic means. Data Mining can be categorized into two types i.e. supervised learning or classification and unsupervised learning or clustering. Clustering only the records in a database (or data matrix) gives a global view of the data and is called one-way clustering. For a detailed analysis or a local view, biclustering or co-clustering or two-way clustering is required involving the simultaneous clustering of the records and the attributes. In this dissertation, a novel fast and white noise tolerant data mining solution is proposed based on the Crossing Minimization (CM) paradigm; the solution works for one-way as well as two-way clustering for discovering overlapping biclusters. For decades the CM paradigm has traditionally been used for graph drawing and VLSI (Very Large Scale Integration) circuit design for reducing wire length and congestion. The utility of the proposed technique is demonstrated by comparing it with other biclustering techniques using simulated noisy, as well as real data from Agriculture, Biology and other domains. Two other interesting and hard problems also addressed in this dissertation are (i) the Minimum Attribute Subset Selection (MASS) problem and (ii) Bandwidth Minimization (BWM) problem of sparse matrices. The proposed CM technique is demonstrated to provide very convincing results while attempting to solve the said problems using real public domain data. Pakistan is the fourth largest supplier of cotton in the world. An apparent anomaly has been observed during 1989-97 between cotton yield and pesticide consumption in Pakistan showing unexpected periods of negative correlation. By applying the indigenous CM technique for one-way clustering to real Agro-Met data (2001-2002), a possible explanation of the anomaly has been presented in this thesis

Data mining using the crossing minimization paradigm

Our ability and capacity to generate, record and store multi-dimensional, apparently unstructured data is increasing rapidly, while the cost of data storage is going down. The data recorded is not perfect, as noise gets introduced in it from different sources. Some of the basic forms of noise are incorrect recording of values and missing values. The formal study of discovering useful hidden information in the data is called Data Mining. Because of the size, and complexity of the problem, practical data mining problems are best attempted using automatic means. Data Mining can be categorized into two types i.e. supervised learning or classification and unsupervised learning or clustering. Clustering only the records in a database (or data matrix) gives a global view of the data and is called one-way clustering. For a detailed analysis or a local view, biclustering or co-clustering or two-way clustering is required involving the simultaneous clustering of the records and the attributes. In this dissertation, a novel fast and white noise tolerant data mining solution is proposed based on the Crossing Minimization (CM) paradigm; the solution works for one-way as well as two-way clustering for discovering overlapping biclusters. For decades the CM paradigm has traditionally been used for graph drawing and VLSI (Very Large Scale Integration) circuit design for reducing wire length and congestion. The utility of the proposed technique is demonstrated by comparing it with other biclustering techniques using simulated noisy, as well as real data from Agriculture, Biology and other domains. Two other interesting and hard problems also addressed in this dissertation are (i) the Minimum Attribute Subset Selection (MASS) problem and (ii) Bandwidth Minimization (BWM) problem of sparse matrices. The proposed CM technique is demonstrated to provide very convincing results while attempting to solve the said problems using real public domain data. Pakistan is the fourth largest supplier of cotton in the world. An apparent anomaly has been observed during 1989-97 between cotton yield and pesticide consumption in Pakistan showing unexpected periods of negative correlation. By applying the indigenous CM technique for one-way clustering to real Agro-Met data (2001-2002), a possible explanation of the anomaly has been presented in this thesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Bayesian nonparametric clusterings in relational and high-dimensional settings with applications in bioinformatics.

Recent advances in high throughput methodologies offer researchers the ability to understand complex systems via high dimensional and multi-relational data. One example is the realm of molecular biology where disparate data (such as gene sequence, gene expression, and interaction information) are available for various snapshots of biological systems. This type of high dimensional and multirelational data allows for unprecedented detailed analysis, but also presents challenges in accounting for all the variability. High dimensional data often has a multitude of underlying relationships, each represented by a separate clustering structure, where the number of structures is typically unknown a priori. To address the challenges faced by traditional clustering methods on high dimensional and multirelational data, we developed three feature selection and cross-clustering methods: 1) infinite relational model with feature selection (FIRM) which incorporates the rich information of multirelational data; 2) Bayesian Hierarchical Cross-Clustering (BHCC), a deterministic approximation to Cross Dirichlet Process mixture (CDPM) and to cross-clustering; and 3) randomized approximation (RBHCC), based on a truncated hierarchy. An extension of BHCC, Bayesian Congruence Measuring (BCM), is proposed to measure incongruence between genes and to identify sets of congruent loci with identical evolutionary histories. We adapt our BHCC algorithm to the inference of BCM, where the intended structure of each view (congruent loci) represents consistent evolutionary processes. We consider an application of FIRM on categorizing mRNA and microRNA. The model uses latent structures to encode the expression pattern and the gene ontology annotations. We also apply FIRM to recover the categories of ligands and proteins, and to predict unknown drug-target interactions, where latent categorization structure encodes drug-target interaction, chemical compound similarity, and amino acid sequence similarity. BHCC and RBHCC are shown to have improved predictive performance (both in terms of cluster membership and missing value prediction) compared to traditional clustering methods. Our results suggest that these novel approaches to integrating multi-relational information have a promising future in the biological sciences where incorporating data related to varying features is often regarded as a daunting task

A Computational Framework for Learning from Complex Data: Formulations, Algorithms, and Applications

Many real-world processes are dynamically changing over time. As a consequence, the observed complex data generated by these processes also evolve smoothly. For example, in computational biology, the expression data matrices are evolving, since gene expression controls are deployed sequentially during development in many biological processes. Investigations into the spatial and temporal gene expression dynamics are essential for understanding the regulatory biology governing development. In this dissertation, I mainly focus on two types of complex data: genome-wide spatial gene expression patterns in the model organism fruit fly and Allen Brain Atlas mouse brain data. I provide a framework to explore spatiotemporal regulation of gene expression during development. I develop evolutionary co-clustering formulation to identify co-expressed domains and the associated genes simultaneously over different temporal stages using a mesh-generation pipeline. I also propose to employ the deep convolutional neural networks as a multi-layer feature extractor to generate generic representations for gene expression pattern in situ hybridization (ISH) images. Furthermore, I employ the multi-task learning method to fine-tune the pre-trained models with labeled ISH images. My proposed computational methods are evaluated using synthetic data sets and real biological data sets including the gene expression data from the fruit fly BDGP data sets and Allen Developing Mouse Brain Atlas in comparison with baseline existing methods. Experimental results indicate that the proposed representations, formulations, and methods are efficient and effective in annotating and analyzing the large-scale biological data sets