bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental design schemes for learning Boolean network models
Authors: Atias Nir , Gershenzon Michal , Labazin Katia , Sharan Roded 
Published: August 2014

Abstract
Motivation: A holy grail of biological research is a working model of the cell. Current modeling frameworks, especially in the protein–protein interaction domain, are mostly topological in nature, calling for stronger and more expressive network models. One promising alternative is logic-based or Boolean network modeling, which was successfully applied to model signaling regulatory circuits in human. Learning such models requires observing the system under a sufficient number of different conditions. To date, the amount of measured data is the main bottleneck in learning informative Boolean models, underscoring the need for efficient experimental design strategies.
Results: We developed novel design approaches that greedily select an experiment to be performed so as to maximize the difference or the entropy in the results it induces with respect to current best-fit models. Unique to our maximum difference approach is the ability to account for all (possibly exponential number of) Boolean models displaying high fit to the available data. We applied both approaches to simulated and real data from the EFGR and IL1 signaling systems in human. We demonstrate the utility of the developed strategies in substantially improving on a random selection approach. Our design schemes highlight the redundancy in these datasets, leading up to 11-fold savings in the number of experiments to be performed.
Availability and implementation: Source code will be made available upon acceptance of the manuscript.
Contact: roded@post.tau.ac.il
Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis
Authors: Khan Suleiman A. , Virtanen Seppo , Kallioniemi Olli P. , Wennerberg Krister , Poso Antti , Kaski Samuel 
Published: August 2014

Abstract
Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.
Results: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids.
Availability and implementation: Source Code implementing the method is available at: http://research.ics.aalto.fi/mi/software/GFAsparse
Contact: suleiman.khan@aalto.fi or samuel.kaski@aalto.fi
Supplementary Information: Supplementary data are available at Bioinformatics online.
Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane)
Authors: Studer Gabriel , Biasini Marco , Schwede Torsten 
Published: August 2014

Abstract
Motivation: Membrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and &gt;50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited.
Results: We developed a local model quality estimation method for membrane proteins (‘QMEANBrane’) by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules.
Availability and implementation: Source code and datasets are available on request.
Contact: torsten.schwede@unibas.ch
Supplementary Information: Supplementary data are available at Bioinformatics online.
PconsFold: improved contact predictions improve protein models
Authors: Michel Mirco , Hayat Sikander , Skwark Marcin J. , Sander Chris , Marks Debora S. , Elofsson Arne 
Published: August 2014

Abstract
Motivation: Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used.
Results: In a small benchmark of 15 proteins, we show that the TM-scores of top-ranked models are improved by on average 33% using PconsFold compared with the original version of EVfold. In a larger benchmark, we find that the quality is improved with 15–30% when using PconsC in comparison with earlier contact prediction methods. Further, using Rosetta instead of CNS does not significantly improve global model accuracy, but the chemistry of models generated with Rosetta is improved.
Availability: PconsFold is a fully automated pipeline for ab initio protein structure prediction based on evolutionary information. PconsFold is based on PconsC contact prediction and uses the Rosetta folding protocol. Due to its modularity, the contact prediction tool can be easily exchanged. The source code of PconsFold is available on GitHub at https://www.github.com/ElofssonLab/pcons-fold under the MIT license. PconsC is available from http://c.pcons.net/.
Contact: arne@bioinfo.se
Supplementary information: Supplementary data are available at Bioinformatics online.
CRISPRstrand: predicting repeat orientations to determine the crRNA-encoding strand at CRISPR loci
Authors: Alkhnbashi Omer S. , Costa Fabrizio , Shah Shiraz A. , Garrett Roger A. , Saunders Sita J. , Backofen Rolf 
Published: August 2014

Abstract
Motivation: The discovery of CRISPR-Cas systems almost 20 years ago rapidly changed our perception of the bacterial and archaeal immune systems. CRISPR loci consist of several repetitive DNA sequences called repeats, inter-spaced by stretches of variable length sequences called spacers. This CRISPR array is transcribed and processed into multiple mature RNA species (crRNAs). A single crRNA is integrated into an interference complex, together with CRISPR-associated (Cas) proteins, to bind and degrade invading nucleic acids. Although existing bioinformatics tools can recognize CRISPR loci by their characteristic repeat-spacer architecture, they generally output CRISPR arrays of ambiguous orientation and thus do not determine the strand from which crRNAs are processed. Knowledge of the correct orientation is crucial for many tasks, including the classification of CRISPR conservation, the detection of leader regions, the identification of target sites (protospacers) on invading genetic elements and the characterization of protospacer-adjacent motifs.
Results: We present a fast and accurate tool to determine the crRNA-encoding strand at CRISPR loci by predicting the correct orientation of repeats based on an advanced machine learning approach. Both the repeat sequence and mutation information were encoded and processed by an efficient graph kernel to learn higher-order correlations. The model was trained and tested on curated data comprising &gt;4500 CRISPRs and yielded a remarkable performance of 0.95 AUC ROC (area under the curve of the receiver operator characteristic). In addition, we show that accurate orientation information greatly improved detection of conserved repeat sequence families and structure motifs. We integrated CRISPRstrand predictions into our CRISPRmap web server of CRISPR conservation and updated the latter to version 2.0.
Availability: CRISPRmap and CRISPRstrand are available at http://rna.informatik.uni-freiburg.de/CRISPRmap.
Contact: backofen@informatik.uni-freiburg.de
Supplementary information: Supplementary data are available at Bioinformatics online.
ASTRAL: genome-scale coalescent-based species tree estimation
Authors: Mirarab S. , Reaz R. , Bayzid Md. S. , Zimmermann T. , Swenson M. S. , Warnow T. 
Published: August 2014

Abstract
Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions.
Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees.
Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral.
Contact: warnow@illinois.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Polytomy refinement for the correction of dubious duplications in gene trees
Authors: Lafond Manuel , Chauve Cedric , Dondi Riccardo , El-Mabrouk Nadia 
Published: August 2014

Abstract
Motivation: Large-scale methods for inferring gene trees are error-prone. Correcting gene trees for weakly supported features often results in non-binary trees, i.e. trees with polytomies, thus raising the natural question of refining such polytomies into binary trees. A feature pointing toward potential errors in gene trees are duplications that are not supported by the presence of multiple gene copies.
Results: We introduce the problem of refining polytomies in a gene tree while minimizing the number of created non-apparent duplications in the resulting tree. We show that this problem can be described as a graph-theoretical optimization problem. We provide a bounded heuristic with guaranteed optimality for well-characterized instances. We apply our algorithm to a set of ray-finned fish gene trees from the Ensembl database to illustrate its ability to correct dubious duplications.
Availability and implementation: The C++ source code for the algorithms and simulations described in the article are available at http://www-ens.iro.umontreal.ca/~lafonman/software.php.
Contact: lafonman@iro.umontreal.ca or mabrouk@iro.umontreal.ca
Supplementary information: Supplementary data are available at Bioinformatics online.
Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning
Authors: Gönen Mehmet , Margolin Adam A. 
Published: August 2014

Abstract
Motivation: Human immunodeficiency virus (HIV) and cancer require personalized therapies owing to their inherent heterogeneous nature. For both diseases, large-scale pharmacogenomic screens of molecularly characterized samples have been generated with the hope of identifying genetic predictors of drug susceptibility. Thus, computational algorithms capable of inferring robust predictors of drug responses from genomic information are of great practical importance. Most of the existing computational studies that consider drug susceptibility prediction against a panel of drugs formulate a separate learning problem for each drug, which cannot make use of commonalities between subsets of drugs.
Results: In this study, we propose to solve the problem of drug susceptibility prediction against a panel of drugs in a multitask learning framework by formulating a novel Bayesian algorithm that combines kernel-based non-linear dimensionality reduction and binary classification (or regression). The main novelty of our method is the joint Bayesian formulation of projecting data points into a shared subspace and learning predictive models for all drugs in this subspace, which helps us to eliminate off-target effects and drug-specific experimental noise. Another novelty of our method is the ability of handling missing phenotype values owing to experimental conditions and quality control reasons. We demonstrate the performance of our algorithm via cross-validation experiments on two benchmark drug susceptibility datasets of HIV and cancer. Our method obtains statistically significantly better predictive performance on most of the drugs compared with baseline single-task algorithms that learn drug-specific models. These results show that predicting drug susceptibility against a panel of drugs simultaneously within a multitask learning framework improves overall predictive performance over single-task learning approaches.
Availability and implementation: Our Matlab implementations for binary classification and regression are available at https://github.com/mehmetgonen/kbmtl.
Contact: mehmet.gonen@sagebase.org
Supplementary Information: Supplementary data are available at Bioinformatics online.
Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images
Authors: Frasconi Paolo , Silvestri Ludovico , Soda Paolo , Cortini Roberto , Pavone Francesco S. , Iannello Giulio 
Published: August 2014

Abstract
Motivation: Recently, confocal light sheet microscopy has enabled high-throughput acquisition of whole mouse brain 3D images at the micron scale resolution. This poses the unprecedented challenge of creating accurate digital maps of the whole set of cells in a brain.
Results: We introduce a fast and scalable algorithm for fully automated cell identification. We obtained the whole digital map of Purkinje cells in mouse cerebellum consisting of a set of 3D cell center coordinates. The method is accurate and we estimated an F1 measure of 0.96 using 56 representative volumes, totaling 1.09 GVoxel and containing 4138 manually annotated soma centers.
Availability and implementation: Source code and its documentation are available at http://bcfind.dinfo.unifi.it/. The whole pipeline of methods is implemented in Python and makes use of Pylearn2 and modified parts of Scikit-learn. Brain images are available on request.
Contact: paolo.frasconi@unifi.it
Supplementary information: Supplementary data are available at Bioinformatics online.
The impact of incomplete knowledge on the evaluation of protein function prediction: a structured-output learning perspective
Authors: Jiang Yuxiang , Clark Wyatt T. , Friedberg Iddo , Radivojac Predrag 
Published: August 2014

Abstract
Motivation: The automated functional annotation of biological macromolecules is a problem of computational assignment of biological concepts or ontological terms to genes and gene products. A number of methods have been developed to computationally annotate genes using standardized nomenclature such as Gene Ontology (GO). However, questions remain about the possibility for development of accurate methods that can integrate disparate molecular data as well as about an unbiased evaluation of these methods. One important concern is that experimental annotations of proteins are incomplete. This raises questions as to whether and to what degree currently available data can be reliably used to train computational models and estimate their performance accuracy.
Results: We study the effect of incomplete experimental annotations on the reliability of performance evaluation in protein function prediction. Using the structured-output learning framework, we provide theoretical analyses and carry out simulations to characterize the effect of growing experimental annotations on the correctness and stability of performance estimates corresponding to different types of methods. We then analyze real biological data by simulating the prediction, evaluation and subsequent re-evaluation (after additional experimental annotations become available) of GO term predictions. Our results agree with previous observations that incomplete and accumulating experimental annotations have the potential to significantly impact accuracy assessments. We find that their influence reflects a complex interplay between the prediction algorithm, performance metric and underlying ontology. However, using the available experimental data and under realistic assumptions, our results also suggest that current large-scale evaluations are meaningful and almost surprisingly reliable.
Contact: predrag@indiana.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Fast randomization of large genomic datasets while preserving alteration counts
Authors: Gobbi Andrea , Iorio Francesco , Dawson Kevin J. , Wedge David C. , Tamborero David , Alexandrov Ludmil B. , Lopez-Bigas Nuria , Garnett Mathew J. , Jurman Giuseppe , Saez-Rodriguez Julio 
Published: August 2014

AbstractMotivation: Studying combinatorial patterns in cancer genomic datasets has recently emerged as a tool for identifying novel cancer driver networks. Approaches have been devised to quantify, for example, the tendency of a set of genes to be mutated in a ‘mutually exclusive’ manner. The significance of the proposed metrics is usually evaluated by computing P-values under appropriate null models. To this end, a Monte Carlo method (the switching-algorithm) is used to sample simulated datasets under a null model that preserves patient- and gene-wise mutation rates. In this method, a genomic dataset is represented as a bipartite network, to which Markov chain updates (switching-steps) are applied. These steps modify the network topology, and a minimal number of them must be executed to draw simulated datasets independently under the null model. This number has previously been deducted empirically to be a linear function of the total number of variants, making this process computationally expensive.Results: We present a novel approximate lower bound for the number of switching-steps, derived analytically. Additionally, we have developed the R package BiRewire, including new efficient implementations of the switching-algorithm. We illustrate the performances of BiRewire by applying it to large real cancer genomics datasets. We report vast reductions in time requirement, with respect to existing implementations/bounds and equivalent P-value computations. Thus, we propose BiRewire to study statistical properties in genomic datasets, and other data that can be modeled as bipartite networks.Availability and implementation: BiRewire is available on BioConductor at http://www.bioconductor.org/packages/2.13/bioc/html/BiRewire.htmlContact: iorio@ebi.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
RidgeRace: ridge regression for continuous ancestral character estimation on phylogenetic trees
Authors: Kratsch Christina , McHardy Alice C. 
Published: August 2014

AbstractMotivation: Ancestral character state reconstruction describes a set of techniques for estimating phenotypic or genetic features of species or related individuals that are the predecessors of those present today. Such reconstructions can reach into the distant past and can provide insights into the history of a population or a set of species when fossil data are not available, or they can be used to test evolutionary hypotheses, e.g. on the co-evolution of traits. Typical methods for ancestral character state reconstruction of continuous characters consider the phylogeny of the underlying data and estimate the ancestral process along the branches of the tree. They usually assume a Brownian motion model of character evolution or extensions thereof, requiring specific assumptions on the rate of phenotypic evolution.Results: We suggest using ridge regression to infer rates for each branch of the tree and the ancestral values at each inner node. We performed extensive simulations to evaluate the performance of this method and have shown that the accuracy of its reconstructed ancestral values is competitive to reconstructions using other state-of-the-art software. Using a hierarchical clustering of gene mutation profiles from an ovarian cancer dataset, we demonstrate the use of the method as a feature selection tool.Availability and implementation: The algorithm described here is implemented in C++ as a stand-alone program, and the source code is freely available at http://algbio.cs.uni-duesseldorf.de/software/RidgeRace.tar.gz.Contact: mchardy@hhu.deSupplementary information: Supplementary data are available at Bioinformatics online.
Probabilistic single-individual haplotyping
Authors: Kuleshov Volodymyr 
Published: August 2014

Abstract
Motivation: Accurate haplotyping—determining from which parent particular portions of the genome are inherited—is still mostly an unresolved problem in genomics. This problem has only recently started to become tractable, thanks to the development of new long read sequencing technologies. Here, we introduce ProbHap, a haplotyping algorithm targeted at such technologies. The main algorithmic idea of ProbHap is a new dynamic programming algorithm that exactly optimizes a likelihood function specified by a probabilistic graphical model and which generalizes a popular objective called the minimum error correction. In addition to being accurate, ProbHap also provides confidence scores at phased positions.
Results: On a standard benchmark dataset, ProbHap makes 11% fewer errors than current state-of-the-art methods. This accuracy can be further increased by excluding low-confidence positions, at the cost of a small drop in haplotype completeness.
Availability: Our source code is freely available at: https://github.com/kuleshov/ProbHap.
Contact: kuleshov@stanford.edu