Random forests are widely recognized for their robustness and generalization across diverse datasets. However, their performance can be hindered by the inclusion of redundant or weakly contributing trees, which may dilute ensemble accuracy and reduce model diversity. This paper presents WRFO , a novel approach for Weighted Random Forest Optimization. WRFO aims to improve random forests mainly through dynamic (1) tree weighting and (2) accuracy and diversity-preserving pruning. WRFO leverages particle swarm optimization to selectively preserve the most informative trees where the latter are assigned higher weights based on their diversity and predictive contribution. This results in a leaner, more accurate, and more diverse ensemble. We present comprehensive experiments on 24 UCI benchmark datasets as well as on a real-world scenario for network intrusion detection. The obtained results demonstrate the effectiveness of WRFO in real-world scenarios and show that it consistently outperforms three state-of-the-art Random Forest methods.

Learning low-dimensional representations for nodes is fundamental to various graph learning tasks. Existing approaches often struggle to comprehensively capture the connections between local structure, contextual information, and global node positioning in the graph. This paper introduces GLOW, a novel approach for node representation learning that jointly maximizes the likelihood of preserving these crucial aspects of the graph structure. GLOW integrates 1) local structural and contextual information, captured through random walks, with 2) global node positioning, approximated using a set of selected landmark nodes. This strategy allows GLOW to learn node embeddings that are structurally aware and informative for downstream tasks. We evaluate GLOW's performance on several benchmark datasets for node classification and link prediction, and compare it to a wide range of established baseline methods. The obtained results consistently demonstrate that GLOW achieves substantial performance improvements. This highlights the advantages of integrating local and global structural learning in effectively capturing graph structure and generating highly resilient node embeddings.

A key step in the integration of data stored across independent knowledge graphs is to match instances that refer to the same real-world object (e.g., the same person). In this paper, we propose DAP-Walk, a novel approach for instance matching that is based on a dynamic and affinity-preserving Markov random walk. Our approach takes into account the local and global information mutually calculated from an association graph of candidate pairs of co-referents. Precisely, we leverage this graph to rank each candidate pair through the stationary distribution computed from the random walk on the association graph. We provide a scalable Spark-based implementation for DAP-Walk where the ranking of nodes of candidate co-referents is obtained by aggregating the distributed ranks calculated across spark workers. Experimental results on benchmark datasets show the efficiency and scalability of DAP-Walk compared to several state-of-the-art instance matching approaches.

BACKGROUND: With the recent advancements in high-throughput experimental procedures, biologists are gathering huge quantities of data. A main priority in bioinformatics and computational biology is to provide system level analytical tools capable of meeting an ever-growing production of high-throughput biological data while taking into account its biological context. In gene expression data analysis, genes have widely been considered as independent components. However, a systemic view shows that they act synergistically in living cells, forming functional complexes and more generally a biological system. RESULTS: In this paper, we propose LATNET, a signal transformation framework that, starting from an initial large-scale gene expression data, allows to generate new representations based on latent network-based relationships between the genes. LATNET aims to leverage system level relations between the genes as an underlying hidden structure to derive the new transformed latent signals. We present a concrete implementation of our framework, based on a gene regulatory network structure and two signal transformation approaches, to quantify latent network-based activity of regulators, as well as gene perturbation signals. The new gene/regulator signals are at the level of each sample of the input data and, thus, could directly be used instead of the initial expression signals for major bioinformatics analysis, including diagnosis and personalized medicine. CONCLUSION: Multiple patterns could be hidden or weakly observed in expression data. LATNET helps in uncovering latent signals that could emphasize hidden patterns based on the relations between the genes and, thus, enhancing the performance of gene expression-based analysis algorithms. We use LATNET for the analysis of real-world gene expression data of bladder cancer and we show the efficiency of our transformation framework as compared to using the initial expression data.

Graph clustering is one of the most important research topics in graph mining and network analysis. With the abundance of data in many real-world applications, the graph nodes and edges could be annotated with multiple sets of attributes that could be derived from heterogeneous data sources. Considering these attributes during the graph clustering could help in generating graph clusters with balanced and cohesive intra-cluster structure and nodes having homogeneous properties. In this paper, we propose a genetic algorithm-based graph clustering approach for mining skyline clusters over large attributed graphs based on the dominance relationship. Each skyline solution is optimized with respect to multiple fitness functions simultaneously where each function is defined over the graph topology or over a particular set of attributes that are derived from multiple data sources. We experimentally evaluate our approach on a real-world large protein-protein interaction network of the human interactome enriched with large sets of heterogeneous cancer associated attributes. The obtained results show the efficiency of our approach and how integrating node attributes of multiple data sources allows to obtain a more robust graph clustering than by considering only the graph topology.

The annotation of protein provides a considerable knowledge for the biologists in order to understand life at the molecular level. The computational annotation of protein function has therefore emerged as an important alternative given that the biological experiments are extremely laborious. A number of methods have been developed to computationally annotate proteins using standardized nomenclatures such as Gene Ontology. These methods are based on various independency assumptions for modeling the annotation problem. However, the recent network analysis reveals that the same protein with different interactions may perform different functions. In this paper, we take into account the topology of the protein-protein interaction network in order to propose a new representation of functions' ontology. We use the Bayesian network in order to model and to alter the structure of this ontology so as to create the new context specific ontology. We use this newly proposed structure for predicting the functions of the unlabeled proteins. We evaluate our method, called Context-Specific Ontology by the use of the Bayesian Network (ConSOn-BN), on the Saccharomyces cerevisiae protein-protein interaction network and we find that ConSOn-BN has enhanced results as compared to some known methods.

BACKGROUND: Genome layout and gene regulation appear to be interdependent. Understanding this interdependence is key to exploring the dynamic nature of chromosome conformation and to engineering functional genomes. Evidence for non-random genome layout, defined as the relative positioning of either co-functional or co-regulated genes, stems from two main approaches. Firstly, the analysis of contiguous genome segments across species, has highlighted the conservation of gene arrangement (synteny) along chromosomal regions. Secondly, the study of long-range interactions along a chromosome has emphasised regularities in the positioning of microbial genes that are co-regulated, co-expressed or evolutionarily correlated. While one-dimensional pattern analysis is a mature field, it is often powerless on biological datasets which tend to be incomplete, and partly incorrect. Moreover, there is a lack of comprehensive, user-friendly tools to systematically analyse, visualise, integrate and exploit regularities along genomes. RESULTS: Here we present the Genome REgulatory and Architecture Tools SCAN (GREAT:SCAN) software for the systematic study of the interplay between genome layout and gene expression regulation. GREAT: SCAN is a collection of related and interconnected applications currently able to perform systematic analyses of genome regularities as well as to improve transcription factor binding sites (TFBS) and gene regulatory network predictions based on gene positional information. CONCLUSIONS: We demonstrate the capabilities of these tools by studying on one hand the regular patterns of genome layout in the major regulons of the bacterium Escherichia coli. On the other hand, we demonstrate the capabilities to improve TFBS prediction in microbes. Finally, we highlight, by visualisation of multivariate techniques, the interplay between position and sequence information for effective transcription regulation.

UNLABELLED: CoRegNet is an R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, CoRegNet can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sites and ChIP data, estimate sample-specific regulator activity, identify cooperative transcription factors and analyze the sample-specific combinations of active regulators through an interactive visualization tool. In this study CoRegNet was used to identify driver regulators of bladder cancer. AVAILABILITY: CoRegNet is available at http://bioconductor.org/packages/CoRegNet CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: We introduce Pepper (Protein complex Expansion using Protein-Protein intERactions), a Cytoscape app designed to identify protein complexes as densely connected subnetworks from seed lists of proteins derived from proteomic studies. Pepper identifies connected subgraph by using multi-objective optimization involving two functions: (i) the coverage, a solution must contain as many proteins from the seed as possible, (ii) the density, the proteins of a solution must be as connected as possible, using only interactions from a proteome-wide interaction network. Comparisons based on gold standard yeast and human datasets showed Pepper's integrative approach as superior to standard protein complex discovery methods. The visualization and interpretation of the results are facilitated by an automated post-processing pipeline based on topological analysis and data integration about the predicted complex proteins. Pepper is a user-friendly tool that can be used to analyse any list of proteins. AVAILABILITY: Pepper is available from the Cytoscape plug-in manager or online (http://apps.cytoscape.org/apps/pepper) and released under GNU General Public License version 3.

Reconstruction of large scale gene regulatory networks (GRNs in the following) is an important step for understanding the complex regulatory mechanisms within the cell. Many modeling approaches have been introduced to find the causal relationship between genes using expression data. However, they have been suffering from high dimensionality-large number of genes but a small number of samples, overfitting, heavy computation time and low interpretability. We have previously proposed an algorithm LICORN, which uses the discrete expression data to find cooperative regulation relationships that are out of the scope of most GRN inference methods. However, as many other methods, LICORN suffers from a large number of false positives. We propose here a hybrid inference method H-Licorn that combines Licorn with a numerical selection step, expressed as a linear regression problem, that effectively complements the discrete search of Licorn. We evaluate a bootstrapped version of H-LICORN on the in silico DREAM5 dataset and show that H-LICORN has significantly higher performance than LICORN, and is competitive or outperforms state of the art GRN inference algorithms, especially when operating on small data sets. We also applied H-LICORN on a real dataset of human bladder cancer and show that it performs better than other methods in finding candidate regulatory interactions. In particular, solely based on gene expression data, H-LICORN is able to identify experimentally validated regulator cooperative relationships involved in cancer.

Reconstruction of gene regulatory networks (GRNs) is an important step for understanding the complex regulatory mechanisms within the cell. Many modeling approaches have been introduced to find the causal relationship between genes using expression data. However, they have been suffering from high dimensionality - large number of genes but a small number of samples -, over fitting, and heavy computation time. In this work 1, we present a novel method, namely SETNET, to improve the stability and accuracy of GRN inference using ensemble techniques. For a given target gene, SETNET extract an ensemble of regulation networks from discretized expression data instead of a single one. Inferred networks are then assessed by ranking individual regulation relationships using a regression based technique and continuous expression data. Evaluation on DREAM5 data demonstrates that SETNET is efficient, specially when operating on a small data set.

Classical approaches to analyze transcriptomic data usually produce average classification models that have very low reproducibility. In this work, genome wide gene expression is considered through the activity of large regulatory networks. We introduce a new measure of regulatory influence based on the variations of expression of genes in a large inferred regulatory network. This methodology can be used to transform transcriptomic data into a smaller influence data set on which feature selection and classification models show similar predictive performance and increased stability and reproducibility, especially when comparing models trained on different datasets. The methodology was tested on two distinct bladder cancer data sets.

MOTIVATION: One of the most challenging tasks in the post-genomic era is the reconstruction of transcriptional regulation networks. The goal is to identify, for each gene expressed in a particular cellular context, the regulators affecting its transcription, and the co-ordination of several regulators in specific types of regulation. DNA microarrays can be used to investigate relationships between regulators and their target genes, through simultaneous observations of their RNA levels. RESULTS: We propose a data mining system for inferring transcriptional regulation relationships from RNA expression values. This system is particularly suitable for the detection of cooperative transcriptional regulation. We model regulatory relationships as labelled two-layer gene regulatory networks, and describe a method for the efficient learning of these bipartite networks from discretized expression data sets. We also evaluate the statistical significance of such inferred networks and validate our methods on two public yeast expression data sets. AVAILABILITY: http://www.lri.fr/~elati/licorn.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.