Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics最新文献

{"title":"Graph-based network analysis of transcriptional regulation pattern divergence in duplicated yeast gene pairs","authors":"Gatis Melkus, Peteris Rucevskis, E. Celms, Kārlis Čerāns, Kārlis Freivalds, Paulis Kikusts, Lelde Lace, Mārtiņš Opmanis, Darta Rituma, Juris Viksna","doi":"10.1145/3365953.3365954","DOIUrl":"https://doi.org/10.1145/3365953.3365954","url":null,"abstract":"The genome and interactome of Saccharomyces cerevisiae have been characterized extensively over the course of the past few decades. However, despite many insights gained over the years, both functional studies and evolutionary analyses continue to reveal many complexities and confounding factors in the construction of reliable transcriptional regulatory network models. We present here a graph-based technique for comparing transcriptional regulatory networks based on network motif similarity for gene pairs. We construct interaction graphs for duplicated transcription factor pairs traceable to the ancestral whole-genome duplication as well as other paralogues in Saccharomyces cerevisiae. We create a set of network divergence metrics predicated on the presence and size of bi-fan arrays that are associated in the literature with gene duplication, within other network motifs. We compare the developed metrics to paralogue protein, gene and promoter alignment-free sequence dissimilarity to validate our results. We observe that our network divergence metrics generally agree with paralogue protein and gene sequence dissimilarity, and notice a weaker agreement with promoter dissimilarity. Our findings indicate that genetic divergence between paralogues is accompanied by a corresponding divergence in their interaction networks, and that our approach may be useful for investigating structural similarity in the interaction networks of paralogous genes.","PeriodicalId":158189,"journal":{"name":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","volume":"13 51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126917736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeted unsupervised features learning for gene expression data analysis to predict cancer stage","authors":"Imene Zenbout, Abdelkrim Bouramoul, S. Meshoul","doi":"10.1145/3365953.3365958","DOIUrl":"https://doi.org/10.1145/3365953.3365958","url":null,"abstract":"The intensive explosion in the generation of large scale cancer gene expression data brought several computational challenges, yet opened great opportunities in exploring different pathways in order to improve cancer prognosis, diagnosis and treatment. In this paper, we propose a targeted unsupervised learning model, based on deep autoencoders (TAE) to learn significant cancer representation based on the gene expression omnibus(GEO) integrated expO data set, for the ultimate goal of constructing an accurate cancer stage predictive model. Where, the trained model was tested on two gene expression cancer data sets namely, lung cancer for clinical stage and intensive breast cancer (IBC) for pathological stage. In which, the model extracted new features space for the two cancer type based on the knowledge built from the expO data set. The generated features were used to train classifiers to predict the cancer stage of each sample. We evaluated the effectiveness of our proposal by comparison to the principal component analysis (PCA) unsupervised dimensionality reduction, as well as to the supervised univariate features selection method. The experimental results, show a promising performance of our analysis model to build a collaborative knowledge from different cancer type to enhance the prediction rate of different cancer stage.","PeriodicalId":158189,"journal":{"name":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115950646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeneRHi-C: 3D GENomE Reconstruction from Hi-C data","authors":"Kimberly MacKay, M. Carlsson, A. Kusalik","doi":"10.1145/3365953.3365962","DOIUrl":"https://doi.org/10.1145/3365953.3365962","url":null,"abstract":"Background: Many computational methods have been developed that leverage the results from biological experiments (such as Hi-C) to infer the 3D organization of the genome. Formally, this is referred to as the 3D genome reconstruction problem (3D-GRP). Hi-C data is now being generated at increasingly high resolutions. As this resolution increases, it has become computationally infeasible to predict a 3D genome organization with the majority of existing methods. None of the existing solution methods have utilized a non-procedural programming approach (such as integer programming) despite the established advantages and successful applications of such approaches for predicting high-resolution 3D structures of other biomolecules. Our objective was to develop a new solution to the 3D-GRP that utilizes non-procedural programming to realize the same advantages. Results: In this paper, we present a three-step consensus method (called GeneRHi-C; pronounced \"generic\") for solving the 3D-GRP which utilizes both new and existing techniques. Briefly, (1) the dimensionality of the 3D-GRP is reduced by identifying a biologically plausible, ploidy-dependent subset of interactions from the Hi-C data. This is performed by modelling the task as an optimization problem and solving it efficiently with an implementation in a non-procedural programming language. The second step (2) generates a biological network (graph) that represents the subset of interactions identified in the previous step. Briefly, genomic bins are represented as nodes in the network with weighted-edges representing known and detected interactions. Finally, the third step (3) uses the ForceAtlas 3D network layout algorithm to calculate (x, y, z) coordinates for each genomic region in the contact map. The resultant predicted genome organization represents the interactions of a population-averaged consensus structure. The overall workflow was tested with Hi-C data from Schizosaccharomyces pombe (fission yeast). The resulting 3D structure clearly recapitulated previously established features of fission yeast 3D genome organization. Conclusion: Overall, GeneRHi-C demonstrates the power of non-procedural programming and graph theoretic techniques for providing an efficient, generalizable solution to the 3D-GRP. Project Homepage: https://github.com/kimmackay/GeneRHi-C","PeriodicalId":158189,"journal":{"name":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129081745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BENIN: combining knockout data with time series gene expression data for the gene regulatory network inference","authors":"Stephanie Kamgnia, G. Butler","doi":"10.1145/3365953.3365955","DOIUrl":"https://doi.org/10.1145/3365953.3365955","url":null,"abstract":"Gene regulatory network inference is one of the central problems in computational biology. The limited availability of biological data as well as the intrinsic noise they contain have triggered the need of models that integrate the vast variety of data available to take advantage of the complementarity of the information they provide about regulation. With this idea in mind, we propose BENIN: Biologically Enhanced Network INference. BENIN is a general framework that jointly considers prior knowledge with expression data to boost the network inference. This method considers network inference as a feature selection problem. To solve it, BENIN uses a penalized regression method, elastic net, combined with bootstrap resampling. Using the benchmark dataset from the DREAM 4 challenge, we demonstrate that, when using times series expression data with knockout gene expression data, BENIN significantly outperforms other methods.","PeriodicalId":158189,"journal":{"name":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114581873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature selection in GSNFS-based marker identification","authors":"Sivakorn Kozuevanich, Jonathan H. Chan, A. Meechai","doi":"10.1145/3365953.3365964","DOIUrl":"https://doi.org/10.1145/3365953.3365964","url":null,"abstract":"Gene Sub-Network-based Feature Selection (GSNFS) is a method capable of handling case-control and multiclass studies for gene sub-network biomarker identification by an integrated analysis of gene expression, gene-set and network data. It has previously been shown to reasonably identify sub-network markers for lung cancer. However, previous studies have not assessed the importance of each subnetwork identified by GSNFS. In this work, we applied correlation-based and information gain feature selection techniques to rank the identified sub-network biomarkers (gene-set). First, the top- and bottom- 5 ranked gene-sets were selected and investigated the classification performance. Expectedly, the top-ranked gene-sets provided an excellent performance while the bottom-ranked gene-sets showed a poor performance. The identified top-ranked gene-sets such as MAPK signalling pathway were known to relate to cancer. Furthermore, combined top-ranked gene-sets from top 2 up to top 30 showed a further improvement on the performance when compared to using individual gene-sets. The results in this study are promising as significantly fewer subnetworks were needed to build a classifier and gave a comparable performance to a full data-set classifier.","PeriodicalId":158189,"journal":{"name":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131779566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Period control of the coupled clock and cell cycle systems","authors":"S. Almeida, M. Chaves, F. Delaunay","doi":"10.1145/3365953.3365956","DOIUrl":"https://doi.org/10.1145/3365953.3365956","url":null,"abstract":"The mammalian clock and cell cycle are two essential biological oscillators. In this work we investigate the coupling of these oscillators via non-linear dynamical modeling. We use previously developed reduced models of these systems and study a molecular interaction of MPF (mitosis promoting factor) repression by the CLOCK:BMAL1 protein complex, via induction of the repressor wee1. Furthermore, we propose an hypothesis whereby the clock responds to cell cycle Growth Factors (GFs) via a pathway involving the non-essential cell cycle complex cyclin D/cdk4 and study this interaction in the context of unidirectional clock → cell cycle coupling. We observe 1:1, 3:2, 4:3, 5:4 ratios of clock to cell cycle period and identify GF and the coupling strength cb as decisive control parameters for the system's state of synchronization. Synchronization ratios differing from 1:1, namely 3:2 and 5:4, have been observed in cells treated with the corticosteroid Dexamethasone (Dex). Here, we study Dex application and are able to reproduce the induction of ratios differing from 1:1. Finally, because slowing down the cell cycle is very relevant in the context of cancer therapies, we devise particular protocols of cell cycle period control with the use of clock inputs that are successful in substantially slowing down the cell cycle by the use of the system's synchronization dynamics, obtaining 2:3, 3:4, 4:5 ratios of clock to cell cycle period.","PeriodicalId":158189,"journal":{"name":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130811870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pineplot","authors":"K. Ovens, D. J. Hogan, F. Maleki, Ian McQuillan, A. Kusalik","doi":"10.1145/3365953.3365959","DOIUrl":"https://doi.org/10.1145/3365953.3365959","url":null,"abstract":"An effective publication-quality visualization tells a concise story from data. Methods and tools that facilitate making such visualizations are valuable to the scientific community. In this paper, we introduce pineplot, an R package for generating insightful visualizations called pine plots. Pine plots are applicable to a wide variety of datasets and create a holistic picture of the relationship between variables across different experimental conditions. A pine plot provides a means to visualize a group of symmetric matrices, each represented by triangular heat maps. Pine plots can be used to visualize large datasets for exploratory data analysis while controlling for different potentially confounding factors. The utility of the package is demonstrated by visualizing gene expression values of tissue-specific genes from RNA-seq data and the clinical factors in a liver disease and a heart disease dataset. The implementation of pineplot offers a straightforward procedure for generating pine plots; full control of the aesthetic elements of generated plots; and the possibility of augmenting generated plots with extra layers of graphical elements to further extend their usability.","PeriodicalId":158189,"journal":{"name":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121473212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Canard bifurcation in the FitzHugh-Nagumo model for spikes generation in neurons","authors":"Marc J. Diener, F. Diener","doi":"10.1145/3365953.3365961","DOIUrl":"https://doi.org/10.1145/3365953.3365961","url":null,"abstract":"We provide a new insight of the all-or-none-spike behavior of the solutions of the FitzHugh-Nagumo model for axon-current. Using the various tools introduced by nonstandard analysis for studying canards behavior we show how the firing of oscillations takes place by the appearance of a canard-without-head cycle that splits into two \"concentric\" cycles, the larger being stable and becoming a canard-with-head and the the smaller being unstable and collapsing down to the equilibrium, all this taking place within an exponentially small parameter interval.","PeriodicalId":158189,"journal":{"name":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131259784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scaffold of N-(2-(2-(tosylcarbamoyl)hydrazinyl)ethyl)isonicotinamidereveals anticancer effects through selective inhibition of FAP","authors":"E. Kabir, Mohammad Kawsar Sharif Siam, N. Mustafa","doi":"10.1145/3365953.3365963","DOIUrl":"https://doi.org/10.1145/3365953.3365963","url":null,"abstract":"The limited expression of fibroblast activation protein (FAP) makes it an alluring target for cancer therapy in the activated epithelial stroma and is related to more than 90% of epithelial cancer. Among the three-enzymatic activities of FAP, the dipeptidyl peptidase activity particularly contributes to tumor progression. Repurposing of small-molecule inhibitors can be a potential therapeutic strategy in both the prevention and treatment of cancer. Drug repurposing was used for this study and doxorubicin was considered a reference drug. Due to similar domain structure and high homologous structure of FAP and dipeptidyl peptidase-4 (DPP IV), the inhibitors of DPP IV were chosen for the study. Previous studies revealed that some drugs of the gliptin and sulfonylureas families are potential DPP IV inhibitors and hence, could be enzymatic inhibitors of FAP. The aim of this study was to predict a new therapeutic indication of the drug(s) from the gliptin family that will regulate fibroblast activation protein (FAP), responsible for tumor growth. An in silico study was carried out with some anti-diabetic drugs. They binding affinities after structural modifications showed significant improvements. Binding affinity values of the substituted structures of some antidiabetic drugs were found using PyRx and interactions were observed using Discovery Studio. The ADMET properties of the compounds were also studied. The most promising drug found from this study, tolbutamide showed a binding affinity of 9.4 kcal/mol and exhibited the following ADMET properties: it did not cross the blood brain barrier and had impressive human intestinal absorption. It was observed to be a non-inhibitor of p glycoprotein inhibitor. Furthermore, the results of AMES toxicity demonstrated the substituted compound was non-AMES toxic. It also interacted with important key residues lining the binding pockets. Overall, the drug seemed to be a selective inhibitor of Fibroblast Activation Protein. It could possibly suppress dipeptidyl peptidase activity of FAP and may play a pragmatic role in epithelial cancer.","PeriodicalId":158189,"journal":{"name":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129303121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","authors":"","doi":"10.1145/3365953","DOIUrl":"https://doi.org/10.1145/3365953","url":null,"abstract":"","PeriodicalId":158189,"journal":{"name":"Proceedings of the Tenth International Conference on Computational Systems-Biology and Bioinformatics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130179663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}