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Relevant bibliographies by topics / Prediction of transcription factor binding sites

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Academic literature on the topic 'Prediction of transcription factor binding sites'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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Journal articles on the topic "Prediction of transcription factor binding sites"

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Jun-tao Guo, Shane Lofgren, and Alvin Farrel. "Structure-based prediction of transcription factor binding sites." Tsinghua Science and Technology 19, no. 6 (December 2014): 568–77. http://dx.doi.org/10.1109/tst.2014.6961027.

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Wang, Guohua, Fang Wang, Qian Huang, Yu Li, Yunlong Liu, and Yadong Wang. "Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites." BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/757530.

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Transcription factors are proteins that bind to DNA sequences to regulate gene transcription. The transcription factor binding sites are short DNA sequences (5–20 bp long) specifically bound by one or more transcription factors. The identification of transcription factor binding sites and prediction of their function continue to be challenging problems in computational biology. In this study, by integrating the DNase I hypersensitive sites with known position weight matrices in the TRANSFAC database, the transcription factor binding sites in gene regulatory region are identified. Based on the global gene expression patterns in cervical cancer HeLaS3 cell and HelaS3-ifnα4h cell (interferon treatment on HeLaS3 cell for 4 hours), we present a model-based computational approach to predict a set of transcription factors that potentially cause such differential gene expression. Significantly, 6 out 10 predicted functional factors, including IRF, IRF-2, IRF-9, IRF-1 and IRF-3, ICSBP, belong to interferon regulatory factor family and upregulate the gene expression levels responding to the interferon treatment. Another factor, ISGF-3, is also a transcriptional activator induced by interferon alpha. Using the different transcription factor binding sites selected criteria, the prediction result of our model is consistent. Our model demonstrated the potential to computationally identify the functional transcription factors in gene regulation.

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Talebzadeh, Mohammad, and Fatemeh Zare-Mirakabad. "Transcription Factor Binding Sites Prediction Based on Modified Nucleosomes." PLoS ONE 9, no. 2 (February 21, 2014): e89226. http://dx.doi.org/10.1371/journal.pone.0089226.

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VON ROHR, PETER, MARKUS T. FRIBERG, and HAJA N. KADARMIDEEN. "PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES USING GENETICAL GENOMICS METHODS." Journal of Bioinformatics and Computational Biology 05, no. 03 (June 2007): 773–93. http://dx.doi.org/10.1142/s0219720007002680.

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In this paper, we wanted to test whether it is possible to use genetical genomics information such as expression quantitative trait loci (eQTL) mapping results as input to a transcription factor binding site (TFBS) prediction algorithm. Furthermore, this new approach was compared to the more traditional cluster based TFBS prediction. The results of eQTL mapping are used as input to one of the top ranking TFBS prediction algorithms. Genes with observed expression profiles showing the same eQTL region are collected into eQTL groups. The promoter sequences of all the genes within the same eQTL group are used as input in the transcription factor binding site search. This approach is tested with a real data set of a recombinant inbred line population of Arabidopsis thaliana. The predicted motifs are compared to results obtained from the conventional approach of first clustering the gene expression values and then using the promoter sequences of the genes within the same cluster as input for the transcription factor binding site prediction. Our eQTL based approach produced different motifs compared to the cluster based method. Furthermore the score of the eQTL based motifs was higher than the score of the cluster based motifs. In a comparison to already predicted motifs from the AtcisDB database, the eQTL based and the cluster based method produced about the same number of hits with binding sites from AtcisDB. In conclusion, the results of this study clearly demonstrate the usefulness of eQTL to predict transcription factor binding sites.

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Li, Hongyang, Daniel Quang, and Yuanfang Guan. "Anchor: trans-cell type prediction of transcription factor binding sites." Genome Research 29, no. 2 (December 19, 2018): 281–92. http://dx.doi.org/10.1101/gr.237156.118.

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Li, X., S. Zhong, and W. H. Wong. "Reliable prediction of transcription factor binding sites by phylogenetic verification." Proceedings of the National Academy of Sciences 102, no. 47 (November 14, 2005): 16945–50. http://dx.doi.org/10.1073/pnas.0504201102.

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Yi, Xianfu, Yu-Dong Cai, Zhisong He, WeiRen Cui, and Xiangyin Kong. "Prediction of Nucleosome Positioning Based on Transcription Factor Binding Sites." PLoS ONE 5, no. 9 (September 1, 2010): e12495. http://dx.doi.org/10.1371/journal.pone.0012495.

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Zhong, Shan, Xin He, and Ziv Bar-Joseph. "Predicting tissue specific transcription factor binding sites." BMC Genomics 14, no. 1 (2013): 796. http://dx.doi.org/10.1186/1471-2164-14-796.

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Mahmoud, Maiada M., Nahla A. Belal, and Aliaa Youssif. "Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1." Applied Sciences 11, no. 11 (May 31, 2021): 5123. http://dx.doi.org/10.3390/app11115123.

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Transcription factors (TFs) are proteins that control the transcription of a gene from DNA to messenger RNA (mRNA). TFs bind to a specific DNA sequence called a binding site. Transcription factor binding sites have not yet been completely identified, and this is considered to be a challenge that could be approached computationally. This challenge is considered to be a classification problem in machine learning. In this paper, the prediction of transcription factor binding sites of SP1 on human chromosome1 is presented using different classification techniques, and a model using voting is proposed. The highest Area Under the Curve (AUC) achieved is 0.97 using K-Nearest Neighbors (KNN), and 0.95 using the proposed voting technique. However, the proposed voting technique is more efficient with noisy data. This study highlights the applicability of the voting technique for the prediction of binding sites, and highlights the outperformance of KNN on this type of data. The study also highlights the significance of using voting.

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FRIBERG, MARKUS T. "PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES USING ChIP-chip AND PHYLOGENETIC FOOTPRINTING DATA." Journal of Bioinformatics and Computational Biology 05, no. 01 (February 2007): 105–16. http://dx.doi.org/10.1142/s0219720007002540.

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We present an algorithm for predicting transcription factor binding sites based on ChIP-chip and phylogenetic footprinting data. Our algorithm is robust against low promoter sequence similarity and motif rearrangements, because it does not depend on multiple sequence alignments. This, in turn, allows us to incorporate information from more distant species. Representative random data sets are used to estimate the score significance. Our algorithm is fully automatic, and does not require human intervention. On a recent S. cerevisiae data set, it achieves higher accuracy than the previously best algorithms. Adaptive ChIP-chip threshold and the modular positional bias score are two general features of our algorithm that increase motif prediction accuracy and could be implemented in other algorithms as well. In addition, since our algorithm works partly orthogonally to other algorithms, combining several algorithms can increase prediction accuracy even further. Specifically, our method finds 6 motifs not found by the 2nd best algorithm.

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Dissertations / Theses on the topic "Prediction of transcription factor binding sites"

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Robert, Christelle L. R. S. "Computational Prediction of Transcription Factor Binding Sites in Bacterial Genomes." Thesis, University of Dundee, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.521672.

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Morozov, Vyacheslav. "Computational Methods for Inferring Transcription Factor Binding Sites." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23382.

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Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. PWMs are compiled from experimentally verified and aligned binding sequences. PWMs are then used to computationally discover novel putative binding sites for a given protein. DNA-binding proteins often show degeneracy in their binding requirement, the overall binding specificity of many proteins is unknown and remains an active area of research. Although PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. A previous study introduced a novel method to PWM training based on the known motifs to sample additional putative binding sites from a proximal promoter area. The core idea was further developed, implemented and tested in this thesis with a large scale application. Improved mono- and dinucleotide PWMs were computed for Drosophila melanogaster. The Matthews correlation coefficient was used as an optimization criterion in the PWM refinement algorithm. New PWMs keep an account of non-uniform background nucleotide distributions on the promoters and consider a larger number of new binding sites during the refinement steps. The optimization included the PWM motif length, the position on the promoter, the threshold value and the binding site location. The obtained predictions were compared for mono- and dinucleotide PWM versions with initial matrices and with conventional tools. The optimized PWMs predicted new binding sites with better accuracy than conventional PWMs.

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Sealfon, Rachel (Rachel Sima). "Predicting enhancer regions and transcription factor binding sites in D. melanogaster." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62434.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 71-75).
Identifying regions in the genome that have regulatory function is important to the fundamental biological problem of understanding the mechanisms through which a regulatory sequence drives specific spatial and temporal patterns of gene expression in early development. The modENCODE project aims to comprehensively identify functional elements in the C. elegans and D. melanogaster genomes. The genome- wide binding locations of all known transcription factors as well as of other DNA- binding proteins are currently being mapped within the context of this project [8]. The large quantity of new data that is becoming available through the modENCODE project and other experimental efforts offers the potential for gaining insight into the mechanisms of gene regulation. Developing improved approaches to identify functional regions and understand their architecture based on available experimental data represents a critical part of the modENCODE effort. Towards this goal, I use a machine learning approach to study the predictive power of experimental and sequence-based combinations of features for predicting enhancers and transcription factor binding sites.
by Rachel Sealfon.
S.M.

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Sandelin, Albin. "In silico prediction of CIS-regulatory elements /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7349-879-3/.

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Jayaram, N. "Improving the prediction of transcription factor binding sites to aid the interpretation of non-coding single nucleotide variants." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/1556214/.

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Single nucleotide variants (SNVs) that occur in transcription factor binding sites (TFBSs) can disrupt the binding of transcription factors and alter gene expression which can cause inherited diseases and act as driver SNVs in cancer. The identification of SNVs in TFBSs has historically been challenging given the limited number of experimentally characterised TFBSs. The recent ENCODE project has resulted in the availability of ChIP-Seq data that provides genome wide sets of regions bound by transcription factors. These data have the potential to improve the identification of SNVs in TFBSs. However, as the ChIP-Seq data identify a broader range of DNA in which a transcription factor binds, computational prediction is required to identify the precise TFBS. Prediction of TFBSs involves scanning a DNA sequence with a Position Weight Matrix (PWM) using a pattern matching tool. This thesis focusses on the prediction of TFBSs by: (a) evaluating a set of locally-installable pattern-matching tools and identifying the best performing tool (FIMO), (b) using the ENCODE ChIP-Seq data to evaluate a set of de novo motif discovery tools that are used to derive PWMs which can handle large volumes of data, (c) identifying the best performing tool (rGADEM), (d) using rGADEM to generate a set of PWMs from the ENCODE ChIP-Seq data and (e) by finally checking that the selection of the best pattern matching tool is not unduly influenced by the choice of PWMs. These analyses were exploited to obtain a set of predicted TFBSs from the ENCODE ChIP-Seq data. The predicted TFBSs were utilised to analyse somatic cancer driver, and passenger SNVs that occur in TFBSs. Clear signals in conservation and therefore Shannon entropy values were identified, and subsequently exploited to identify a threshold that can be used to prioritize somatic cancer driver SNVs for experimental validation.

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Rezwan, Faisal Ibne. "Improving computational predictions of Cis-regulatory binding sites in genomic data." Thesis, University of Hertfordshire, 2011. http://hdl.handle.net/2299/7133.

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Cis-regulatory elements are the short regions of DNA to which specific regulatory proteins bind and these interactions subsequently influence the level of transcription for associated genes, by inhibiting or enhancing the transcription process. It is known that much of the genetic change underlying morphological evolution takes place in these regions, rather than in the coding regions of genes. Identifying these sites in a genome is a non-trivial problem. Experimental (wet-lab) methods for finding binding sites exist, but all have some limitations regarding their applicability, accuracy, availability or cost. On the other hand computational methods for predicting the position of binding sites are less expensive and faster. Unfortunately, however, these algorithms perform rather poorly, some missing most binding sites and others over-predicting their presence. The aim of this thesis is to develop and improve computational approaches for the prediction of transcription factor binding sites (TFBSs) by integrating the results of computational algorithms and other sources of complementary biological evidence. Previous related work involved the use of machine learning algorithms for integrating predictions of TFBSs, with particular emphasis on the use of the Support Vector Machine (SVM). This thesis has built upon, extended and considerably improved this earlier work. Data from two organisms was used here. Firstly the relatively simple genome of yeast was used. In yeast, the binding sites are fairly well characterised and they are normally located near the genes that they regulate. The techniques used on the yeast genome were also tested on the more complex genome of the mouse. It is known that the regulatory mechanisms of the eukaryotic species, mouse, is considerably more complex and it was therefore interesting to investigate the techniques described here on such an organism. The initial results were however not particularly encouraging: although a small improvement on the base algorithms could be obtained, the predictions were still of low quality. This was the case for both the yeast and mouse genomes. However, when the negatively labeled vectors in the training set were changed, a substantial improvement in performance was observed. The first change was to choose regions in the mouse genome a long way (distal) from a gene over 4000 base pairs away - as regions not containing binding sites. This produced a major improvement in performance. The second change was simply to use randomised training vectors, which contained no meaningful biological information, as the negative class. This gave some improvement over the yeast genome, but had a very substantial benefit for the mouse data, considerably improving on the aforementioned distal negative training data. In fact the resulting classifier was finding over 80% of the binding sites in the test set and moreover 80% of the predictions were correct. The final experiment used an updated version of the yeast dataset, using more state of the art algorithms and more recent TFBSs annotation data. Here it was found that using randomised or distal negative examples once again gave very good results, comparable to the results obtained on the mouse genome. Another source of negative data was tried for this yeast data, namely using vectors taken from intronic regions. Interestingly this gave the best results.

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Parmar, Victor. "Predicting transcription factor binding sites using phylogenetic footprinting and a probabilistic framework for evolutionary turnover." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=87000.

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Identifying genomic locations of transcription-factor binding sites (TFBS), particularly in higher eukaryotic genomes, has been an enormous challenge. Computational methods involving identification of sequence conservation between related genomes have been the most successful since sites found in such highly conserved regions are more likely to be functional, i.e. are bound and regulate protein production. In this thesis, we present such a probabilistic algorithm for predicting TFBSs which also takes evolutionary turnovers into account. Our algorithm is validated via simulations and the results of its application on ChIP-chip data are presented.
L'identification des sites de fixation des facteurs de transcription (TFBS), particulièrement sur les génomes eucaryotiques plus élevés, a été un énorme défi. Les méthodes informatiques comportant l'identification de la conservation de séquence entre les génomes de différentes espèces ont eu beaucoup de succès parce que les sites trouvés dans de telles régions fortement conservées sont probablement fonctionnels (les facteurs de transcription se rajoutent sur le génome à ces sites-là et réglent la production de protéine). Dans cette thèse, nous présentons un algorithme probabiliste pour la prédiction de TFBSs qui prend en considération également le remuement évolutionnaire. Notre algorithme est validé par l'intermédiare des simulations et le résultats de son application sur des données ChIP-chip sont présentés

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Kiełbasa, Szymon M. "Bioinformatics of eukaryotic gene regulation." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2006. http://dx.doi.org/10.18452/15562.

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Die Aufklärung der Mechanismen zur Kontrolle der Genexpression ist eines der wichtigsten Probleme der modernen Molekularbiologie. Detaillierte experimentelle Untersuchungen sind enorm aufwändig aufgrund der komplexen und kombinatorischen Wechselbeziehungen der beteiligten Moleküle. Infolgedessen sind bioinformatische Methoden unverzichtbar. Diese Dissertation stellt drei Methoden vor, die die Vorhersage der regulatorischen Elementen der Gentranskription verbessern. Der erste Ansatz findet Bindungsstellen, die von den Transkriptionsfaktoren erkannt werden. Dieser sucht statistisch überrepräsentierte kurze Motive in einer Menge von Promotersequenzen und wird erfolgreich auf das Genom der Bäckerhefe angewandt. Die Analyse der Genregulation in höheren Eukaryoten benötigt jedoch fortgeschrittenere Techniken. In verschiedenen Datenbanken liegen Hunderte von Profilen vor, die von den Transkriptionsfaktoren erkannt werden. Die Ähnlichkeit zwischen ihnen resultiert in mehrfachen Vorhersagen einer einzigen Bindestelle, was im nachhinein korrigiert werden muss. Es wird eine Methode vorgestellt, die eine Möglichkeit zur Reduktion der Anzahl von Profilen bietet, indem sie die Ähnlichkeiten zwischen ihnen identifiziert. Die komplexe Natur der Wechselbeziehung zwischen den Transkriptionsfaktoren macht jedoch die Vorhersage von Bindestellen schwierig. Auch mit einer Verringerung der zu suchenden Profile sind die Resultate der Vorhersagen noch immer stark fehlerbehafted. Die Zuhilfenahme der unabhängigen Informationsressourcen reduziert die Häufigkeit der Falschprognosen. Die dritte beschriebene Methode schlägt einen neuen Ansatz vor, die die Gen-Anotation mit der Regulierung von multiplen Transkriptionsfaktoren und den von ihnen erkannten Bindestellen assoziiert. Der Nutzen dieser Methode wird anhand von verschiedenen wohlbekannten Sätzen von Transkriptionsfaktoren demonstriert.
Understanding the mechanisms which control gene expression is one of the fundamental problems of molecular biology. Detailed experimental studies of regulation are laborious due to the complex and combinatorial nature of interactions among involved molecules. Therefore, computational techniques are used to suggest candidate mechanisms for further investigation. This thesis presents three methods improving the predictions of regulation of gene transcription. The first approach finds binding sites recognized by a transcription factor based on statistical over-representation of short motifs in a set of promoter sequences. A succesful application of this method to several gene families of yeast is shown. More advanced techniques are needed for the analysis of gene regulation in higher eukaryotes. Hundreds of profiles recognized by transcription factors are provided by libraries. Dependencies between them result in multiple predictions of the same binding sites which need later to be filtered out. The second method presented here offers a way to reduce the number of profiles by identifying similarities between them. Still, the complex nature of interaction between transcription factors makes reliable predictions of binding sites difficult. Exploiting independent sources of information reduces the false predictions rate. The third method proposes a novel approach associating gene annotations with regulation of multiple transcription factors and binding sites recognized by them. The utility of the method is demonstrated on several well-known sets of transcription factors. RNA interference provides a way of efficient down-regulation of gene expression. Difficulties in predicting efficient siRNA sequences motivated the development of a library containing siRNA sequences and related experimental details described in the literature. This library, presented in the last chapter, is publicly available at http://www.human-sirna-database.net

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Gebhardt, Marie Luise. "Enrichment of miRNA targets in REST-regulated genes allows filtering of miRNA target predictions." Doctoral thesis, Humboldt-Universität zu Berlin, Lebenswissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17407.

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Vorhersagen von miRNA-Bindestellen enthalten oft einen hohen Prozentsatz an falsch positiven Ergebnissen (24-70%). Gleichzeitig ist es schwierig die biologischen Interaktionen von miRNAs und ihren Zieltranskripten auf experimentellem Wege und Genom weit zu messen. Daher wurde in der vorliegenden Arbeit die Frage beantwortet, ob ChIP-Sequenzierungsdaten, von denen es immer mehr gibt, verwendet werden können, um Vorhersagen von miRNA-Bindestellen zu filtern. Dabei wurde von einem Netzwerk aus miRNAs und Transkriptionsfaktoren gebraucht gemacht, die Zieltranskripte gemeinsam regulieren. Zunächst wurden verschiedene Methoden getestet, mit denen „Peaks“ aus der ChIP-Sequenzierung Zielgenen zugeordnet werden können. Zielgenlisten des transkriptionalen Repressors RE1-silencing transcription factor (REST/NRSF) wurden mithilfe von ChIP-Sequenzierungsdaten erzeugt. Ein Algorithmus zur Suche nach überrepräsentierten miRNA-Zielgenen in REST-Genlisten basierend auf Vorhersagen von TargetScanHuman wurde entwickelt und angewandt. Die detektierten „enrichment“-miRNAs waren Teil eines vielfältig regulierten REST-miRNA-Netzwerks. Mögliche Funktionen von miRNAs wurden vorgeschlagen und ihre Rolle im gemeinsamen Netzwerk mit REST und im damit gebildeten Netzwerkmotiv (Inkoherente Schleife zur Vorwärtskopplung Typ 2) wurde analysiert. Es stellte sich heraus, dass ein Filtern der Vorhersagen tatsächlich möglich ist, da Gene, die sowohl von REST als auch von einer oder mehreren „enrichment“-miRNAs reguliert werden, einen höheren Anteil an wahren miRNA-Transkript-Interaktionen haben.
Predictions of miRNA binding sites suffer from high false positive rates (24-70%) and measuring biological interactions of miRNAs and target transcripts on a genome wide scale remains challenging. In the thesis at hand the question was answered if the ever growing body of ChIP-sequencing data can be applied to filter miRNA target predictions by making use of the underlying regulatory network of miRNAs and transcription factors. First different methods for association of ChIP-sequencing peaks to target genes were tested. Target gene lists of the transcriptional repressor RE1-silencing transcription factor (REST/NRSF) were generated by means of ChIP-sequencing data. An enrichment analysis tool based on predictions from TargetScanHuman was developed and applied to find ‘enrichment’-miRNAs with over-represented targets in the REST gene lists. The detected miRNAs were shown to be part of a highly regulated REST-miRNA network. Possible functions could be assigned to them and their role in the regulatory network and special network motifs (incoherent feedforward loop of type 2) was analyzed. It turned out that miRNA target predictions of genes shared by enrichment-miRNAs and REST had a higher proportion of true positive associations than the TargetScanHuman background, thus the procedure made a filtering possible.

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Pape, Utz J. [Verfasser]. "Statistics for transcription factor binding sites / Utz J. Pape." Berlin : Freie Universität Berlin, 2009. http://d-nb.info/1023329476/34.

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Books on the topic "Prediction of transcription factor binding sites"

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Quon, Gerald T. The landscape of false-positive transcription factor binding site predictions in yeast. 2007.

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Turner, William Joseph. AP-1 and SP1 transcription factor binding sites modulate DNA replication efficiency. 1997.

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Turner, William Joseph. AP-1 and SP1 transcription factor binding sites modulate DNA replication efficiency. 1997.

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Wilderman, Paula Jo. The effects of T-antigen and transcription factor binding sites on simian virus 40 DNA replication. 1998.

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Wilderman, Paula Jo. The effects of T-antigen and transcription factor binding sites on simian virus 40 DNA replication. 1998.

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Teo, William J. Screening of potential upstream regulators and identification of DNA binding sites for the tooth transcription factor Krox-26. 2001.

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Book chapters on the topic "Prediction of transcription factor binding sites"

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Sim, Jeong Seop, and Soo-Jun Park. "Transcription Factor Binding Sites Prediction Based on Sequence Similarity." In Fuzzy Systems and Knowledge Discovery, 1058–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_131.

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Liu, L. Angela, and Joel S. Bader. "Structure-Based Ab Initio Prediction of Transcription Factor–Binding Sites." In Methods in Molecular Biology, 23–41. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-243-4_2.

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Kaplan, Tommy, Nir Friedman, and Hanah Margalit. "Predicting Transcription Factor Binding Sites Using Structural Knowledge." In Lecture Notes in Computer Science, 522–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11415770_40.

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Posch, Stefan, Jan Grau, André Gohr, Jens Keilwagen, and Ivo Grosse. "Probabilistic Approaches to Transcription Factor Binding Site Prediction." In Methods in Molecular Biology, 97–119. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-854-6_7.

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Oshchepkov, Dmitry Y., and Victor G. Levitsky. "In Silico Prediction of Transcriptional Factor-Binding Sites." In Methods in Molecular Biology, 251–67. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-176-5_16.

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Song, Yinglei, Changbao Wang, and Junfeng Qu. "A Parameterized Algorithm for Predicting Transcription Factor Binding Sites." In Intelligent Computing in Bioinformatics, 339–50. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09330-7_41.

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Yu, Chun-Ping, and Wen-Hsiung Li. "Predicting Transcription Factor Binding Sites and Their Cognate Transcription Factors Using Gene Expression Data." In Methods in Molecular Biology, 271–82. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7125-1_17.

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Gusmão, Eduardo G., Christoph Dieterich, and Ivan G. Costa. "Prediction of Transcription Factor Binding Sites by Integrating DNase Digestion and Histone Modification." In Advances in Bioinformatics and Computational Biology, 109–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31927-3_10.

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Lee, Wook, Byungkyu Park, Daesik Choi, Chungkeun Lee, Hanju Chae, and Kyungsook Han. "Predicting Transcription Factor Binding Sites in DNA Sequences Without Prior Knowledge." In Intelligent Computing Theories and Application, 386–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42291-6_38.

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Orent, William, and Wassim Elyaman. "Prediction and Validation of Transcription Factors Binding Sites in the Il9 Locus." In Methods in Molecular Biology, 111–25. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6877-0_9.

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Conference papers on the topic "Prediction of transcription factor binding sites"

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LIU, L. ANGELA, and JOEL S. BADER. "AB INITIO PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772435_0046.

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Chang, Xiaoyu, Wengang Zhou, Chunguang Zhou, and Yanchun Liang. "Prediction of Transcription Factor Binding Sites Using Genetic Algorithm." In 2006 1ST IEEE Conference on Industrial Electronics and Applications. IEEE, 2006. http://dx.doi.org/10.1109/iciea.2006.257271.

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Sichtig, Heike, J. David Schaffer, and Alberto Riva. "Evolving Spiking Neural Networks for predicting transcription factor binding sites." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596642.

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Fan, Guoliang, Qianzhong Li, and Keli Yang. "TFBSs: A web server for predicting transcription factor binding sites." In 2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2012. http://dx.doi.org/10.1109/cyber.2012.6319888.

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Ahsan, Faizy, Doina Precup, and Mathieu Blanchette. "Prediction of Cell Type Specific Transcription Factor Binding Site Occupancy." In BCB '16: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2975167.2985652.

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Desai, V., P. Khatri, A. Done, A. Fridman, M. Tainsky, and S. Draghici. "A Novel Bioinformatics Technique For Predicting Condition-Specific Transcription Factor Binding Sites." In 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2005. http://dx.doi.org/10.1109/cibcb.2005.1594918.

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ZENG, YUANQI, WUZHONG DONG, QINGYUAN CHEN, YONGQING ZHANG, and DONGRUI GAO. "A Transcription Factor Binding Site Prediction Algorithm Based on Semi-Supervised Learning." In 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2019. http://dx.doi.org/10.1109/iccwamtip47768.2019.9067698.

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Chen, Jialong, and Lei Deng. "DeepARC: An Attention-based Hybrid Model for Predicting Transcription Factor Binding Sites from Positional Embedded DNA Sequence." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313249.

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Feng, Wangsen, Lusheng Wang, Wanling Qu, and Hanpin Wang. "Finding Transcription Factor Binding Sites with Indels." In 2007 1st International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icbbe.2007.69.

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Wirawan, Adrianto, and Bertil Schmidt. "Parallel Discovery of Transcription Factor Binding Sites." In APCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems. IEEE, 2006. http://dx.doi.org/10.1109/apccas.2006.342103.

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