bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting genetic regulatory response using classification
Authors: Middendorf Manuel , Kundaje Anshul , Wiggins Chris , Freund Yoav , Leslie Christina 
Published: July 2004

Abstract
Motivation: Studying gene regulatory mechanisms in simple model organisms through analysis of high-throughput genomic data has emerged as a central problem in computational biology. Most approaches in the literature have focused either on finding a few strong regulatory patterns or on learning descriptive models from training data. However, these approaches are not yet adequate for making accurate predictions about which genes will be up- or down-regulated in new or held-out experiments. By introducing a predictive methodology for this problem, we can use powerful tools from machine learning and assess the statistical significance of our predictions.
Results: We present a novel classification-based method for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences (‘motifs’) in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment (‘parents’). Thus, our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. We convert the regression task of predicting real-valued gene expression measurements to a classification task of predicting +1 and −1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. The learning algorithm employed is boosting with a margin-based generalization of decision trees, alternating decision trees. This large-margin classifier is sufficiently flexible to allow complex logical functions, yet sufficiently simple to give insight into the combinatorial mechanisms of gene regulation. We observe encouraging prediction accuracy on experiments based on the Gasch S.cerevisiae dataset, and we show that we can accurately predict up- and down-regulation on held-out experiments. We also show how to extract significant regulators, motifs and motif-regulator pairs from the learned models for various stress responses. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks.
Availability: The MLJava package is available upon request to the authors.
Supplementary: Additional results are available from http://www.cs.columbia.edu/compbio/geneclass
Statistical modeling of sequencing errors in SAGE libraries
Authors: Beißbarth Tim , Hyde Lavinia , Smyth Gordon K. , Job Chris , Boon Wee-Ming , Tan Seong-Seng , Scott Hamish S. , Speed Terence P. 
Published: July 2004

Abstract
Motivation: Sequencing errors may bias the gene expression measurements made by Serial Analysis of Gene Expression (SAGE). They may introduce non-existent tags at low abundance and decrease the real abundance of other tags. These effects are increased in the longer tags generated in LongSAGE libraries. Current sequencing technology generates quite accurate estimates of sequencing error rates. Here we make use of the sequence neighborhood of SAGE tags and error estimates from the base-calling software to correct for such errors.
Results: We introduce a statistical model for the propagation of sequencing errors in SAGE and suggest an Expectation-Maximization (EM) algorithm to correct for them given observed sequences in a library and base-calling error estimates. We tested our method using simulated and experimental SAGE libraries. When comparing SAGE libraries, we found that sequencing errors can introduce considerable bias. High abundance tags may be falsely called as significantly differentially expressed, especially when comparing libraries with different levels of sequencing errors and/or of different size. Truly, differentially expressed tags have decreased significance as ‘true’-tag counts are generally underestimated. This may alter if tags near the threshold of differential expression are called significant. Moreover, the number of different transcripts present in a library is overestimated as false tags are introduced at low abundance. Our correction method adjusts the tag counts to be closer to the true counts and is able to partly correct for biases introduced by sequencing errors.
Availability: An implementation using R is distributed as an R package. An online version is available at http://tagcalling.mbgproject.org
Exploiting conserved structure for faster annotation of non-coding RNAs without loss of accuracy
Authors: Weinberg Zasha , Ruzzo Walter L. 
Published: July 2004

Abstract
Motivation: Non-coding RNAs (ncRNAs)—functional RNA molecules not coding for proteins—are grouped into hundreds of families of homologs. To find new members of an ncRNA gene family in a large genome database, covariance models (CMs) are a useful statistical tool, as they use both sequence and RNA secondary structure information. Unfortunately, CM searches are slow. Previously, we introduced ‘rigorous filters’, which provably sacrifice none of CMs' accuracy, although often scanning much faster. A rigorous filter, using a profile hidden Markov model (HMM), is built based on the CM, and filters the genome database, eliminating sequences that provably could not be annotated as homologs. The CM is run only on the remainder. Some biologically important ncRNA families could not be scanned efficiently with this technique, largely due to the significance of conserved secondary structure relative to primary sequence in identifying these families. Current heuristic filters are also expected to perform poorly on such families.
Results: By augmenting profile HMMs with limited secondary structure information, we obtain rigorous filters that accelerate CM searches for virtually all known ncRNA families from the Rfam Database and tRNA models in tRNAscan-SE. These filters scan an 8 gigabase database in weeks instead of years, and uncover homologs missed by heuristic techniques to speed CM searches.
Availability: Software in development; contact the authors.
Supplementary information: http://bio.cs.washington.edu/supplements/zasha-ISMB-2004 (Additional technical details on the method; predicted homologs.)
Reconstructing phylogeny by Quadratically Approximated Maximum Likelihood
Authors: Woodhams M. D. , Hendy M. D. 
Published: July 2004

Abstract
Summary: Maximum likelihood (ML) for phylogenetic inference from sequence data remains a method of choice, but has computational limitations. In particular, it cannot be applied for a global search through all potential trees when the number of taxa is large, and hence a heuristic restriction in the search space is required. In this paper, we derive a quadratic approximation, QAML, to the likelihood function whose maximum is easily determined for a given tree. The derivation depends on Hadamard conjugation, and hence is limited to the simple symmetric models of Kimura and of Jukes and Cantor. Preliminary testing has demonstrated the accuracy of QAML is close to that of ML.
Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data
Authors: de Hoon M. J. L. , Makita Y. , Imoto S. , Kobayashi K. , Ogasawara N. , Nakai K. , Miyano S. 
Published: July 2004

Abstract
Motivation: Sigma factors regulate the expression of genes in Bacillus subtilis at the transcriptional level. We assess the accuracy of a fold-change analysis, Bayesian networks, dynamic models and supervised learning based on coregulation in predicting gene regulation by sigma factors from gene expression data. To improve the prediction accuracy, we combine sequence information with expression data by adding their log-likelihood scores and by using a logistic regression model. We use the resulting score function to discover currently unknown gene regulations by sigma factors.
Results: The coregulation-based supervised learning method gave the most accurate prediction of sigma factors from expression data. We found that the logistic regression model effectively combines expression data with sequence information. In a genome-wide search, highly significant logistic regression scores were found for several genes whose transcriptional regulation is currently unknown. We provide the corresponding RNA polymerase binding sites to enable a straightforward experimental verification of these predictions.
A neural-network-based method for predicting protein stability changes upon single point mutations
Authors: Capriotti Emidio , Fariselli Piero , Casadio Rita 
Published: July 2004

Abstract
Motivation: One important requirement for protein design is to be able to predict changes of protein stability upon mutation. Different methods addressing this task have been described and their performance tested considering global linear correlation between predicted and experimental data. Neither is direct statistical evaluation of their prediction performance available, nor is a direct comparison among different approaches possible. Recently, a significant database of thermodynamic data on protein stability changes upon single point mutation has been generated (ProTherm). This allows the application of machine learning techniques to predicting free energy stability changes upon mutation starting from the protein sequence.
Results: In this paper, we present a neural-network-based method to predict if a given mutation increases or decreases the protein thermodynamic stability with respect to the native structure. Using a dataset consisting of 1615 mutations, our predictor correctly classifies &gt;80% of the mutations in the database. On the same task and using the same data, our predictor performs better than other methods available on the Web. Moreover, when our system is coupled with energy-based methods, the joint prediction accuracy increases up to 90%, suggesting that it can be used to increase also the performance of pre-existing methods, and generally to improve protein design strategies.
Availability: The server is under construction and will be available at http://www.biocomp.unibo.it
Using MoBIoS' scalable genome join to find conserved primer pair candidates between two genomes
Authors: Xu Weijia , Briggs Willard J. , Padolina Joanna , Timme Ruth E. , Liu Wenguo , Linder C. Randal , Miranker Daniel P. 
Published: July 2004

Abstract
Motivation: For the purpose of identifying evolutionary reticulation events in flowering plants, we determine a large number of paired, conserved DNA oligomers that may be used as primers to amplify orthologous DNA regions using the polymerase chain reaction (PCR).
Results: We develop an initial candidate set by comparing the Arabidopsis and rice genomes using MoBIoS (Molecular Biological Information System). MoBIoS is a metric-space database management system targeting life science data. Through the use of metric-space indexing techniques, two genomes can be compared in O(mlog n), where m and n are the lengths of the genomes, versus O(mn) for BLAST-based analysis. The filtering of low-complexity regions may also be accomplished by directly assessing the uniqueness of the region. We describe mSQL, a SQL extension being developed for MoBIoS that encapsulates the algorithmic details in a common database programming language, shielding end-users from esoteric programming.
Availability: Available upon request from authors.
Filling gaps in a metabolic network using expression information
Authors: Kharchenko Peter , Vitkup Dennis , Church George M. 
Published: July 2004

Abstract
Motivation: The metabolic models of both newly sequenced and well-studied organisms contain reactions for which the enzymes have not been identified yet. We present a computational approach for identifying genes encoding such missing metabolic enzymes in a partially reconstructed metabolic network.
Results: The metabolic expression placement (MEP) method relies on the coexpression properties of the metabolic network and is complementary to the sequence homology and genome context methods that are currently being used to identify missing metabolic genes. The MEP algorithm predicts over 20% of all known Saccharomyces cerevisiae metabolic enzyme-encoding genes within the top 50 out of 5594 candidates for their enzymatic function, and 70% of metabolic genes whose expression level has been significantly perturbed across the conditions of the expression dataset used.
Availability: Freely available (in Supplementary information).
Supplementary information: Available at the following URL http://arep.med.harvard.edu/kharchenko/mep/supplements.html
Reconstructing tumor amplisomes
Authors: Raphael Benjamin J. , Pevzner Pavel A. 
Published: July 2004

Abstract
Motivation: Duplication of genomic sequences is a common phenomenon in tumor cells. While many duplications associated with tumors have been identified (e.g. via techniques such as CGH), both the organization of the duplicated sequences and the process that leads to these duplications are less clear. One mechanism that has been observed to lead to duplication is the extraction of DNA from the chromosomes and aggregation of this DNA into small, independently replicating linear or circular DNA sequences (amplisomes). Parts of these amplisomes may later be reinserted back into the main chromosomes leading to duplication. Although amplisomes are known to play an important role in tumorigenesis, their architecture and even size remain largely unknown.
Results: We reconstruct the structure of tumor amplisomes by analyzing duplications in the tumor genome. Our approach relies on recently generated data from End Sequence Profiling (ESP) experiments, which allow us to examine the fine structure of duplications in a tumor on a genome-wide scale. Using ESP data, we formulate the Amplisome Reconstruction Problem, describe an algorithm for its solution, and derive a putative architecture of a tumor amplisome that is the source for duplicated material in the MCF7 breast tumor cell line.
Splice site identification by idlBNs
Authors: Castelo Robert , Guigó Roderic 
Published: July 2004

Abstract
Motivation: Computational identification of functional sites in nucleotide sequences is at the core of many algorithms for the analysis of genomic data. This identification is based on the statistical parameters estimated from a training set. Often, because of the huge number of parameters, it is difficult to obtain consistent estimators. To simplify the estimation problem, one imposes independent assumptions between the nucleotides along the site. However, this can potentially limit the minimum value of the estimation error.
Results: In this paper, we introduce a novel method in the context of identifying functional sites, that finds a reasonable set of independence assumptions supported by the data, among the nucleotides, and uses it to perform the identification of the sites by their likelihood ratio. More importantly, in many practical situations it is capable of improving its performance as the training sample size increases. We apply the method to the identification of splice sites, and further evaluate its effect within the context of exon and gene prediction.
Supplementary information: The datasets built specifically for this paper as well as the full set of results are available at http://genome.imim.es/datasets/splidlbns2004
The cell graphs of cancer
Authors: Gunduz Cigdem , Yener Bülent , Gultekin S. Humayun 
Published: July 2004

Abstract
Summary: We report a novel, proof-of-concept, computational method that models a type of brain cancer (glioma) only by using the topological properties of its cells in the tissue image. From low-magnification (80×) tissue images of 384 × 384 pixels, we construct the graphs of the cells based on the locations of the cells within the images. We generate such cell graphs of 1000–3000 cells (nodes) with 2000–10 000 links, each of which is calculated as a decaying exponential function of the Euclidean distance between every pair of cells in accordance with the Waxman model. At the cellular level, we compute the graph metrics of the cell graphs, including the degree, clustering coefficient, eccentricity and closeness for each cell. Working with a total of 285 tissue samples surgically removed from 12 different patients, we demonstrate that the self-organizing clusters of cancerous cells exhibit distinctive graph metrics that distinguish them from the healthy cells and the unhealthy inflamed cells at the cellular level with an accuracy of at least 85%. At the tissue level, we accomplish correct tissue classifications of cancerous, healthy and non-neoplastic inflamed tissue samples with an accuracy of 100% by requiring correct classification for the majority of the cells within the tissue sample.
Genomic features in the breakpoint regions between syntenic blocks
Authors: Trinh Phil , McLysaght Aoife , Sankoff David 
Published: July 2004

Abstract
Motivation: We study the largely unaligned regions between the syntenic blocks conserved in humans and mice, based on data extracted from the UCSC genome browser. These regions contain evolutionary breakpoints caused by inversion, translocation and other processes.
Results: We suggest explanations for the limited amount of genomic alignment in the neighbourhoods of breakpoints. We discount inferences of extensive breakpoint reuse as artefacts introduced during the reconstruction of syntenic blocks. We find that the number, size and distribution of small aligned fragments in the breakpoint regions depend on the origin of the neighbouring blocks and the other blocks on the same chromosome. We account for this and for the generalized loss of alignment in the regions partially by artefacts due to alignment protocols and partially by mutational processes operative only after the rearrangement event. These results are consistent with breakpoints occurring randomly over virtually the entire genome.
Predicting protein folding pathways
Authors: Zaki Mohammed J. , Nadimpally Vinay , Bardhan Deb , Bystroff Chris 
Published: July 2004

Abstract
Summary: A structured folding pathway, which is a time ordered sequence of folding events, plays an important role in the protein folding process and hence, in the conformational search. Pathway prediction, thus gives more insight into the folding process and is a valuable guiding tool to search the conformation space. In this paper, we propose a novel ‘unfolding’ approach to predict the folding pathway. We apply graph-based methods on a weighted secondary structure graph of a protein to predict the sequence of unfolding events. When viewed in reverse this yields the folding pathway. We demonstrate the success of our approach on several proteins whose pathway is partially known.
Optimal robust non-unique probe selection using Integer Linear Programming
Authors: Klau Gunnar W. , Rahmann Sven , Schliep Alexander , Vingron Martin , Reinert Knut 
Published: July 2004

Abstract
Motivation: Besides their prevalent use for analyzing gene expression, microarrays are an efficient tool for biological, medical and industrial applications due to their ability to assess the presence or absence of biological agents, the targets, in a sample. Given a collection of genetic sequences of targets one faces the challenge of finding short oligonucleotides, the probes, which allow detection of targets in a sample. Each hybridization experiment determines whether the probe binds to its corresponding sequence in the target. Depending on the problem, the experiments are conducted using either unique or non-unique probes and usually assume that only one target is present in the sample. The problem at hand is to compute a design, i.e. a minimal set of probes that allows to infer the targets in the sample from the result of the hybridization experiment. If we allow to test for more than one target in the sample, the design of the probe set becomes difficult in the case of non-unique probes.
Results: Building upon previous work on group testing for microarrays, we describe the first approach to select a minimal probe set for the case of non-unique probes in the presence of a small number of multiple targets in the sample. The approach is based on an ILP formulation and a branch-and-cut algorithm. Our preliminary implementation greatly reduces the number of probes needed while preserving the decoding capabilities.
Availability: http://www.inf.fu-berlin.de/inst/ag-bio
Robust inference of groups in gene expression time-courses using mixtures of HMMs
Authors: Schliep Alexander , Steinhoff Christine , Schönhuth Alexander 
Published: July 2004

Abstract
Motivation: Genetic regulation of cellular processes is frequently investigated using large-scale gene expression experiments to observe changes in expression over time. This temporal data poses a challenge to classical distance-based clustering methods due to its horizontal dependencies along the time-axis. We propose to use hidden Markov models (HMMs) to explicitly model these time-dependencies. The HMMs are used in a mixture approach that we show to be superior over clustering. Furthermore, mixtures are a more realistic model of the biological reality, as an unambiguous partitioning of genes into clusters of unique functional assignment is impossible. Use of the mixture increases robustness with respect to noise and allows an inference of groups at varying level of assignment ambiguity. A simple approach, partially supervised learning, allows to benefit from prior biological knowledge during the training. Our method allows simultaneous analysis of cyclic and non-cyclic genes and copes well with noise and missing values.
Results: We demonstrate biological relevance by detection of phase-specific groupings in HeLa time-course data. A benchmark using simulated data, derived using assumptions independent of those in our method, shows very favorable results compared to the baseline supplied by k-means and two prior approaches implementing model-based clustering. The results stress the benefits of incorporating prior knowledge, whenever available.
Availability: A software package implementing our method is freely available under the GNU general public license (GPL) at http://ghmm.org/gql
Supplementary information: Supplemental material can be found at http://algorithmics.molgen.mpg.de/ExpMix
Filtering erroneous protein annotation
Authors: Wieser D. , Kretschmann E. , Apweiler R. 
Published: July 2004

Abstract
Motivation: Automatically generated annotation on protein data of UniProt (Universal Protein Resource) is planned to be publicly available on the UniProt web pages in April 2004. It is expected that the data content of over 500 000 protein entries in the TrEMBL section will be enhanced by the output of an automated annotation pipeline. However, a part of the automatically added data will be erroneous, as are parts of the information coming from other sources. We present a post-processing system called Xanthippe that is based on a simple exclusion mechanism and a decision tree approach using the C4.5 data-mining algorithm.
Results: It is shown that Xanthippe detects and flags a large part of the annotation errors and considerably increases the reliability of both automatically generated data and annotation from other sources. As a cross-validation to Swiss-Prot shows, errors in protein descriptions, comments and keywords are successfully filtered out. Xanthippe is a contradictive application that can be combined seamlessly with predictive systems. It can be used either to improve the precision of automated annotation at a constant level of recall or increase the recall at a constant level of precision.
Availability: The application of the Xanthippe rules can be browsed at http://www.ebi.uniprot.org/