The Experts below are selected from a list of 106365 Experts worldwide ranked by ideXlab platform
Oliver Kohlbacher - One of the best experts on this subject based on the ideXlab platform.
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yloc an interpretable web server for predicting Subcellular Localization
Nucleic Acids Research, 2010Co-Authors: Sebastian Briesemeister, Jörg Rahnenführer, Oliver KohlbacherAbstract:Predicting Subcellular Localization has become a valuable alternative to time-consuming experimental methods. Major drawbacks of many of these predictors is their lack of interpretability and the fact that they do not provide an estimate of the confidence of an individual prediction. We present YLoc, an interpretable web server for predicting Subcellular Localization. YLoc uses natural language to explain why a prediction was made and which biological property of the protein was mainly responsible for it. In addition, YLoc estimates the reliability of its own predictions. YLoc can, thus, assist in understanding protein Localization and in location engineering of proteins. The YLoc web server is available online at www.multiloc.org/YLoc.
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YLoc—an interpretable web server for predicting Subcellular Localization
Nucleic acids research, 2010Co-Authors: Sebastian Briesemeister, Jörg Rahnenführer, Oliver KohlbacherAbstract:Predicting Subcellular Localization has become a valuable alternative to time-consuming experimental methods. Major drawbacks of many of these predictors is their lack of interpretability and the fact that they do not provide an estimate of the confidence of an individual prediction. We present YLoc, an interpretable web server for predicting Subcellular Localization. YLoc uses natural language to explain why a prediction was made and which biological property of the protein was mainly responsible for it. In addition, YLoc estimates the reliability of its own predictions. YLoc can, thus, assist in understanding protein Localization and in location engineering of proteins. The YLoc web server is available online at www.multiloc.org/YLoc.
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Going from where to why—interpretable prediction of protein Subcellular Localization
Bioinformatics (Oxford England), 2010Co-Authors: Sebastian Briesemeister, Jörg Rahnenführer, Oliver KohlbacherAbstract:Motivation: Protein Subcellular Localization is pivotal in understanding a protein’s function. Computational prediction of Subcellular Localization has become a viable alternative to experimental approaches. While current machine learning-based methods yield good prediction accuracy, most of them suffer from two key problems: lack of interpretability and dealing with multiple locations. Results: We present YLoc, a novel method for predicting protein Subcellular Localization that addresses these issues. Due to its simple architecture, YLoc can identify the relevant features of a protein sequence contributing to its Subcellular Localization, e.g. Localization signals or motifs relevant to protein sorting. We present several example applications where YLoc identifies the sequence features responsible for protein Localization, and thus reveals not only to which location a protein is transported to, but also why it is transported there. YLoc also provides a confidence estimate for the prediction. Thus, the user can decide what level of error is acceptable for a prediction. Due to a probabilistic approach and the use of several thousands of dual-targeted proteins, YLoc is able to predict multiple locations per protein. YLoc was benchmarked using several independent datasets for protein Subcellular Localization and performs on par with other state-of-the-art predictors. Disregarding low-confidence predictions, YLoc can achieve prediction accuracies of over 90%. Moreover, we show that YLoc is able to reliably predict multiple locations and outperforms the best predictors in this area. Availability: www.multiloc.org/YLoc
Rita Casadio - One of the best experts on this subject based on the ideXlab platform.
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BUSCA: an integrative web server to predict Subcellular Localization of proteins.
Nucleic acids research, 2018Co-Authors: Castrense Savojardo, Pier Luigi Martelli, Piero Fariselli, Giuseppe Profiti, Rita CasadioAbstract:Here, we present BUSCA (http://busca.biocomp.unibo.it), a novel web server that integrates different computational tools for predicting protein Subcellular Localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating Subcellular Localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating Subcellular Localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein Subcellular Localization.
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eSLDB: eukaryotic Subcellular Localization database
Nucleic Acids Research, 2006Co-Authors: Andrea Pierleoni, Pier Luigi Martelli, Piero Fariselli, Rita CasadioAbstract:Eukaryotic Subcellular Localization DataBase collects the annotations of Subcellular Localization of eukaryotic proteomes. So far five proteomes have been processed and stored: Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidopsis thaliana. For each sequence, the database lists Localization obtained adopting three different approaches: (i) experimentally determined (when available); (ii) homology-based (when possible); and (iii) predicted. The latter is computed with a suite of machine learning based methods, developed in house. All the data are available at our website and can be searched by sequence, by protein code and/or by protein description. Furthermore, a more complex search can be performed combining different search fields and keys. All the data contained in the database can be freely downloaded in flat file format. The database is available at http://gpcr.biocomp.unibo.it/esldb/.
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bacello a balanced Subcellular Localization predictor
Intelligent Systems in Molecular Biology, 2006Co-Authors: Andrea Pierleoni, Pier Luigi Martelli, Piero Fariselli, Rita CasadioAbstract:Motivation. The knowledge of the Subcellular Localization of a protein is fundamental for elucidating its function. It is difficult to determine the Subcellular location for eukaryotic cells with experimental highthroughput procedures. Computational procedures are then needed forannotatingtheSubcellularlocationofproteinsinlargescalegenomic projects. Results. BaCelLo is a predictor for five classes of Subcellular Localization (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) and it is based on different SVMs organized in a decision tree. The system exploits the information derived from the residue sequenceandfromtheevolutionaryinformationcontainedinalignment profiles. It analyzes the whole sequence composition and the compositions of both the N- and C-termini. The training set is curated in order to avoid redundancy. For the first time a balancing procedure is introduced in order to mitigate the effect of biased training sets. Three kingdom-specific predictors are implemented: for animals, plants and fungi, respectively. When distributing the proteins from animals and fungi into four classes, accuracy of BaCelLo reach 74% and 76%, respectively; a score of 67% is obtained when proteins from plants are distributed into five classes. BaCelLo outperforms the other presently available methods for the same task and gives more balanced accuracy and coverage values for each class. We also predict the Subcellular Localization of five whole proteomes, Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidopsis thaliana, comparing the protein content in each different compartment. Availability.BaCelLocanbeaccessedathttp://www.biocomp.unibo.it/ bacello/
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BaCelLo: a balanced Subcellular Localization predictor.
Bioinformatics (Oxford England), 2006Co-Authors: Andrea Pierleoni, Pier Luigi Martelli, Piero Fariselli, Rita CasadioAbstract:The knowledge of the Subcellular Localization of a protein is fundamental for elucidating its function. It is difficult to determine the Subcellular location for eukaryotic cells with experimental high-throughput procedures. Computational procedures are then needed for annotating the Subcellular location of proteins in large scale genomic projects. BaCelLo is a predictor for five classes of Subcellular Localization (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) and it is based on different SVMs organized in a decision tree. The system exploits the information derived from the residue sequence and from the evolutionary information contained in alignment profiles. It analyzes the whole sequence composition and the compositions of both the N- and C-termini. The training set is curated in order to avoid redundancy. For the first time a balancing procedure is introduced in order to mitigate the effect of biased training sets. Three kingdom-specific predictors are implemented: for animals, plants and fungi, respectively. When distributing the proteins from animals and fungi into four classes, accuracy of BaCelLo reach 74% and 76%, respectively; a score of 67% is obtained when proteins from plants are distributed into five classes. BaCelLo outperforms the other presently available methods for the same task and gives more balanced accuracy and coverage values for each class. We also predict the Subcellular Localization of five whole proteomes, Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidopsis thaliana, comparing the protein content in each different compartment. BaCelLo can be accessed at http://www.biocomp.unibo.it/bacello/.
Sebastian Briesemeister - One of the best experts on this subject based on the ideXlab platform.
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yloc an interpretable web server for predicting Subcellular Localization
Nucleic Acids Research, 2010Co-Authors: Sebastian Briesemeister, Jörg Rahnenführer, Oliver KohlbacherAbstract:Predicting Subcellular Localization has become a valuable alternative to time-consuming experimental methods. Major drawbacks of many of these predictors is their lack of interpretability and the fact that they do not provide an estimate of the confidence of an individual prediction. We present YLoc, an interpretable web server for predicting Subcellular Localization. YLoc uses natural language to explain why a prediction was made and which biological property of the protein was mainly responsible for it. In addition, YLoc estimates the reliability of its own predictions. YLoc can, thus, assist in understanding protein Localization and in location engineering of proteins. The YLoc web server is available online at www.multiloc.org/YLoc.
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YLoc—an interpretable web server for predicting Subcellular Localization
Nucleic acids research, 2010Co-Authors: Sebastian Briesemeister, Jörg Rahnenführer, Oliver KohlbacherAbstract:Predicting Subcellular Localization has become a valuable alternative to time-consuming experimental methods. Major drawbacks of many of these predictors is their lack of interpretability and the fact that they do not provide an estimate of the confidence of an individual prediction. We present YLoc, an interpretable web server for predicting Subcellular Localization. YLoc uses natural language to explain why a prediction was made and which biological property of the protein was mainly responsible for it. In addition, YLoc estimates the reliability of its own predictions. YLoc can, thus, assist in understanding protein Localization and in location engineering of proteins. The YLoc web server is available online at www.multiloc.org/YLoc.
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Going from where to why—interpretable prediction of protein Subcellular Localization
Bioinformatics (Oxford England), 2010Co-Authors: Sebastian Briesemeister, Jörg Rahnenführer, Oliver KohlbacherAbstract:Motivation: Protein Subcellular Localization is pivotal in understanding a protein’s function. Computational prediction of Subcellular Localization has become a viable alternative to experimental approaches. While current machine learning-based methods yield good prediction accuracy, most of them suffer from two key problems: lack of interpretability and dealing with multiple locations. Results: We present YLoc, a novel method for predicting protein Subcellular Localization that addresses these issues. Due to its simple architecture, YLoc can identify the relevant features of a protein sequence contributing to its Subcellular Localization, e.g. Localization signals or motifs relevant to protein sorting. We present several example applications where YLoc identifies the sequence features responsible for protein Localization, and thus reveals not only to which location a protein is transported to, but also why it is transported there. YLoc also provides a confidence estimate for the prediction. Thus, the user can decide what level of error is acceptable for a prediction. Due to a probabilistic approach and the use of several thousands of dual-targeted proteins, YLoc is able to predict multiple locations per protein. YLoc was benchmarked using several independent datasets for protein Subcellular Localization and performs on par with other state-of-the-art predictors. Disregarding low-confidence predictions, YLoc can achieve prediction accuracies of over 90%. Moreover, we show that YLoc is able to reliably predict multiple locations and outperforms the best predictors in this area. Availability: www.multiloc.org/YLoc
Kenta Nakai - One of the best experts on this subject based on the ideXlab platform.
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protein Subcellular Localization prediction with wolf psort
Asia-Pacific Bioinformatics Conference, 2005Co-Authors: Paul Horton, Keunjoon Park, Takeshi Obayashi, Kenta NakaiAbstract:We present a new program for predicting protein Subcellular Localization from amino acid sequence. WoLF PSORT is a major update to the PSORTII program, based on new sequence
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APBC - PROTEIN Subcellular Localization PREDICTION WITH WOLF PSORT
Proceedings of the 4th Asia-Pacific Bioinformatics Conference, 2005Co-Authors: Paul Horton, Keunjoon Park, Takeshi Obayashi, Kenta NakaiAbstract:We present a new program for predicting protein Subcellular Localization from amino acid sequence. WoLF PSORT is a major update to the PSORTII program, based on new sequence
Qiang Yang - One of the best experts on this subject based on the ideXlab platform.
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Semi-supervised protein Subcellular Localization
BMC Bioinformatics, 2009Co-Authors: Hong Xue, Qiang YangAbstract:Protein Subcellular Localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of Subcellular Localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein Subcellular Localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the Subcellular Localization of a protein and prepare instances of labeled data. In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions. Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.
