References of "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailAn efficient approach towards the source-target control of Boolean networks
Paul, Soumya UL; Su, Cui UL; Pang, Jun UL et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (in press)

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target ... [more ▼]

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking this view, we derive a decomposition-based solution to the minimal source-target control problem which can be significantly faster than the existing approaches on large networks. We then show that the solution can be further optimised if we take into account appropriate information about the source state. We apply our solutions to both real-life biological networks and randomly generated networks, demonstrating the efficiency and efficacy of our approach. [less ▲]

Detailed reference viewed: 35 (0 UL)
Full Text
Peer Reviewed
See detailTaming asynchrony for attractor detection in large Boolean networks
Mizera, Andrzej; Pang, Jun UL; Qu, Hongyang et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019), 16(1), 31-42

Detailed reference viewed: 55 (2 UL)
Full Text
Peer Reviewed
See detailAlgorithms for the Sequential Reprogramming of Boolean Networks
Mandon, Hugues; Su, Cui; Pang, Jun UL et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019), 16(5), 1610-1619

Detailed reference viewed: 23 (0 UL)
Full Text
Peer Reviewed
See detailMachine Learning to Support the Presentation of Complex Pathway Graphs.
Nielsen, Sune, S UL; Ostaszewski, Marek UL; McGee, Fintan et al

in IEEE/ACM transactions on computational biology and bioinformatics (2019)

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult ... [more ▼]

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts. [less ▲]

Detailed reference viewed: 31 (2 UL)
Full Text
Peer Reviewed
See detailASSA-PBN: A Toolbox for Probabilistic Boolean Networks
Mizera, Andrzej; Pang, Jun UL; Su, Cui et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018), 15(4), 1203-1216

Detailed reference viewed: 36 (3 UL)
Full Text
Peer Reviewed
See detailReviving the two-state Markov chain approach
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018), 15(5), 1525-1537

Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such ... [more ▼]

Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such systems. However, for large PBNs, which often arise in systems biology, obtaining the steady-state distribution poses a significant challenge. In this paper, we revive the two-state Markov chain approach to solve this problem. This paper contributes in three aspects. First, we identify a problem of generating biased results with the approach and we propose a few heuristics to avoid such a pitfall. Secondly, we conduct an extensive experimental comparison of the extended two-state Markov chain approach and another approach based on the Skart method. We analyse the results with machine learning techniques and we show that statistically the two-state Markov chain approach has a better performance. Finally, we demonstrate the potential of the extended two-state Markov chain approach on a case study of a large PBN model of apoptosis in hepatocytes. [less ▲]

Detailed reference viewed: 87 (4 UL)
Full Text
Peer Reviewed
See detailQuantitative analysis of the self-assembly strategies of intermediate filaments from tetrameric vimentin
Czeizler, Eugen; Mizera, Andrzej UL; Czeizler, Elena et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2012), 9(3), 885-898

Detailed reference viewed: 54 (1 UL)
Full Text
Peer Reviewed
See detailSelecting oligonucleotide probes for whole-genome tiling arrays with a cross-hybridization potential.
Hafemeister, Christoph; Krause, Roland UL; Schliep, Alexander

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2011), 8(6), 1642-52

For designing oligonucleotide tiling arrays popular, current methods still rely on simple criteria like Hamming distance or longest common factors, neglecting base stacking effects which strongly ... [more ▼]

For designing oligonucleotide tiling arrays popular, current methods still rely on simple criteria like Hamming distance or longest common factors, neglecting base stacking effects which strongly contribute to binding energies. Consequently, probes are often prone to cross-hybridization which reduces the signal-to-noise ratio and complicates downstream analysis. We propose the first computationally efficient method using hybridization energy to identify specific oligonucleotide probes. Our Cross-Hybridization Potential (CHP) is computed with a Nearest Neighbor Alignment, which efficiently estimates a lower bound for the Gibbs free energy of the duplex formed by two DNA sequences of bounded length. It is derived from our simplified reformulation of t-gap insertion-deletion-like metrics. The computations are accelerated by a filter using weighted ungapped q-grams to arrive at seeds. The computation of the CHP is implemented in our software OSProbes, available under the GPL, which computes sets of viable probe candidates. The user can choose a trade-off between running time and quality of probes selected. We obtain very favorable results in comparison with prior approaches with respect to specificity and sensitivity for cross-hybridization and genome coverage with high-specificity probes. The combination of OSProbes and our Tileomatic method, which computes optimal tiling paths from candidate sets, yields globally optimal tiling arrays, balancing probe distance, hybridization conditions, and uniqueness of hybridization. [less ▲]

Detailed reference viewed: 74 (0 UL)
Full Text
Peer Reviewed
See detailThe plexus model for the inference of ancestral multidomain proteins.
Wiedenhoeft, John; Krause, Roland UL; Eulenstein, Oliver

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2011), 8(4), 890-901

Interactions of protein domains control essential cellular processes. Thus, inferring the evolutionary histories of multidomain proteins in the context of their families can provide rewarding insights ... [more ▼]

Interactions of protein domains control essential cellular processes. Thus, inferring the evolutionary histories of multidomain proteins in the context of their families can provide rewarding insights into protein function. However, methods to infer these histories are challenged by the complexity of macroevolutionary events. Here, we address this challenge by describing an algorithm that computes a novel network-like structure, called plexus, which represents the evolution of domains and their combinations. Finally, we demonstrate the performance of this algorithm with empirical data sets. [less ▲]

Detailed reference viewed: 68 (0 UL)
Full Text
Peer Reviewed
See detailDesigning logical rules to model the response of biomolecular networks with complex interactions: an application to cancer modeling.
Guziolowski, Carito; Blachon, Sylvain; Baumuratova, Tatiana UL et al

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2011), 8(5), 1223-34

We discuss the propagation of constraints in eukaryotic interaction networks in relation to model prediction and the identification of critical pathways. In order to cope with posttranslational ... [more ▼]

We discuss the propagation of constraints in eukaryotic interaction networks in relation to model prediction and the identification of critical pathways. In order to cope with posttranslational interactions, we consider two types of nodes in the network, corresponding to proteins and to RNA. Microarray data provides very lacunar information for such types of networks because protein nodes, although needed in the model, are not observed. Propagation of observations in such networks leads to poor and nonsignificant model predictions, mainly because rules used to propagate information--usually disjunctive constraints--are weak. Here, we propose a new, stronger type of logical constraints that allow us to strengthen the analysis of the relation between microarray and interaction data. We use these rules to identify the nodes which are responsible for a phenotype, in particular for cell cycle progression. As the benchmark, we use an interaction network describing major pathways implied in Ewing's tumor development. The Python library used to obtain our results is publicly available on our supplementary web page. [less ▲]

Detailed reference viewed: 111 (3 UL)