Christos M. Dimitrakopoulos

Scale-space measures for graph topology link protein network architecture to function

Motivation: The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). Results: In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions—genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture. Availability and implementation: MatlabTM code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA Contact: j.deridder@tudelft.nl Supplementary information: Supplementary data are available at Bioinformatics online.

Computational approaches for the identification of cancer genes and pathways

High‐throughput DNA sequencing techniques enable large‐scale measurement of somatic mutations in tumors. Cancer genomics research aims at identifying all cancer‐related genes and solid interpretation of their contribution to cancer initiation and development. However, this venture is characterized by various challenges, such as the high number of neutral passenger mutations and the complexity of the biological networks affected by driver mutations. Based on biological pathway and network information, sophisticated computational methods have been developed to facilitate the detection of cancer driver mutations and pathways. They can be categorized into (1) methods using known pathways from public databases, (2) network‐based methods, and (3) methods learning cancer pathways de novo. Methods in the first two categories use and integrate different types of data, such as biological pathways, protein interaction networks, and gene expression measurements. The third category consists of de novo methods that detect combinatorial patterns of somatic mutations across tumor samples, such as mutual exclusivity and co‐occurrence. In this review, we discuss recent advances, current limitations, and future challenges of these approaches for detecting cancer genes and pathways. We also discuss the most important current resources of cancer‐related genes. WIREs Syst Biol Med 2017, 9:e1364. doi: 10.1002/wsbm.1364

Ectopic expression of Msx2 in mammalian myotubes recapitulates aspects of amphibian muscle dedifferentiation.

In contrast to urodele amphibians and teleost fish, mammals lack the regenerative responses to replace large body parts. Amphibian and fish regeneration uses dedifferentiation, i.e., reversal of differentiated state, as a means to produce progenitor cells to eventually replace damaged tissues. Therefore, induced activation of dedifferentiation responses in mammalian tissues holds an immense promise for regenerative medicine. Here we demonstrate that ectopic expression of Msx2 in cultured mouse myotubes recapitulates several aspects of amphibian muscle dedifferentiation. We found that MSX2, but not MSX1, leads to cellularization of myotubes and downregulates the expression of myotube markers, such as MHC, MRF4 and myogenin. RNA sequencing of myotubes ectopically expressing Msx2 showed downregulation of over 500 myotube-enriched transcripts and upregulation of over 300 myoblast-enriched transcripts. MSX2 selectively downregulated expression of Ptgs2 and Ptger4, two members of the prostaglandin pathway with important roles in myoblast fusion during muscle differentiation. Ectopic expression of Msx2, as well as Msx1, induced partial cell cycle re-entry of myotubes by upregulating CyclinD1 expression but failed to initiate S-phase. Finally, MSX2-induced dedifferentiation in mouse myotubes could be recapitulated by a pharmacological treatment with trichostatin A (TSA), bone morphogenetic protein 4 (BMP4) and fibroblast growth factor 1 (FGF1). Together, these observations indicate that MSX2 is a major driver of dedifferentiation in mammalian muscle cells.

Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology

OBJECTIVE Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. METHODS AND MATERIALS The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. RESULTS Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. CONCLUSIONS EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new potentially true human protein complexes were suggested as candidates for further validation using experimental techniques.

The Human Interactome Knowledge Base (HINT-KB): an integrative human protein interaction database enriched with predicted protein---protein interaction scores using a novel hybrid technique

Proteins are the functional components of many cellular processes and the identification of their physical protein–protein interactions (PPIs) is an area of mature academic research. Various databases have been developed containing information about experimentally and computationally detected human PPIs as well as their corresponding annotation data. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://biotools.ceid.upatras.gr/hint-kb/), a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, calculates a set of features of interest and computes a confidence score for every candidate protein interaction. This confidence score is essential for filtering the false positive interactions which are present in existing databases, predicting new protein interactions and measuring the frequency of each true protein interaction. For this reason, a novel machine learning hybrid methodology, called (Evolutionary Kalman Mathematical Modelling—EvoKalMaModel), was used to achieve an accurate and interpretable scoring methodology. The experimental results indicated that the proposed scoring scheme outperforms existing computational methods for the prediction of PPIs.

Efficient Computational Prediction and Scoring of Human Protein-Protein Interactions Using a Novel Gene Expression Programming Methodology

Proteins and their interactions have been proven to play a central role in many cellular processes. Thus, many experimental methods have been developed for their prediction. These experimental methods are uneconomic and time consuming in the case of low throughput methods or inaccurate in the case of high throughput methods. To overcome these limitations, many computational methods have been developed to predict and score Protein-Protein Interactions (PPIs) using a variety of functional, sequential and structural data for each protein pair. Existing computational methods can still be enhanced in terms of classification performance and interpretability. In the present paper we present a novel Gene Expression Programming (GEP) algorithm, named as jGEPModelling 2.0, and apply it to the problem of PPI prediction and scoring. jGEPModelling2.0 is a variation of the classic GEP algorithm to make it suitable for the problem of PPI prediction and enhance its classification performance. To test its efficiency, we applied it to a public available dataset and compared it to two other state-of-the-art PPI prediction models. Experimental results proved that jGEPModelling2.0 outperformed existing methodologies in terms of classification performance and interpretability. (This paper is submitted for the CIAB2012 workshop).

Network-based integration of multi-omics data for prioritizing cancer genes

Motivation: Several molecular events are known to be cancer‐related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression. Results: We developed NetICS (Network‐based Integration of Multi‐omics Data), a new graph diffusion‐based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS’ competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types. Availability and implementation: NetICS is available at https://github.com/cbg‐ethz/netics. Supplementary information: Supplementary data are available at Bioinformatics online.

HINT-KB: The Human Interactome Knowledge Base

Proteins and their interactions are considered to play a significant role in many cellular processes. The identification of Protein-Protein interactions (PPIs) in human is an open research area. Many Databases, which contain information about experimentally and computationally detected human PPIs as well as their corresponding annotation data, have been developed. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://150.140.142.24:84/Default.aspx) which is a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, estimates a set of features of interest and computes a confidence score for every candidate protein interaction using a modern computational hybrid methodology.

A New Framework for Bridging the Gap from Protein-Protein Interactions to Biological Process Interactions

Proteins and their interactions have been proven to play a central role in many cellular processes and have been extensively studied so far. However of great importance, little work has been conducted for the identification of biological process interactions in the higher cellular level which could provide knowledge about the high level cellular functionalities and maybe enable researchers to explain mechanisms that lead to diseases. Existing computational approaches for predicting Biological Process interactions used PPI graphs of low quality and coverage but failed to utilize weighted PPI graphs to quantify the quality of the interactions. In the present paper, we propose a unified two-step framework to reach the goal of predicting biological process interactions. After conducting a comparative study we selected as a first step the EVOKALMA model as a very promising algorithm for robust PPI prediction and scoring. Then, in order to be able to handle weights, we combined it with a novel variation of an existing algorithm for predicting biological processes interactions. The overall methodology was applied for predicting biological processes interactions for Saccharomyces Cerevisiae and Homo Sapiens organisms, uncovering thousands of interactions for both organisms. Most of the linked processes come in agreement with the existing knowledge but many of them should be further studied.

Mining Student Learning Behavior and Self-assessment for Adaptive Learning Management System

The specific contribution aims to provide a web-based adaptive Learning Management System (LMS), named EVMATHEIA, which integrates specific innovative fundamental aspects of Student Learning Style and Intelligent Self-Assessment Mechanisms. More specifically the proposed adaptive system encapsulates an integrated student model that facilitates the decision about the learning style of the student monitoring his/her behavior. Furthermore, the platform utilizes semantic modeling techniques for the representation of the knowledge, semantically annotated educational material and an intelligent mechanism for the self-assessment and recommendation process.