Exploring classification strategies with the CoEPrA 2006 contest

Demir-Kavuk, Ozgur; Riedesel, Henning; Knapp, Ernst‐Walter

doi:10.1093/bioinformatics/btq021

Cited by 11 publications

(4 citation statements)

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“…Hence, matrices with a large XX value are suitable to compare closely related sequences whereas matrices with a small XX value are suitable to compare distantly related sequences. The BLOSUM62 matrix yielded good performances in previous research [ 1 ] and has been used in many sequence alignment applications [ 21 ]. Therefore, in this study the BLOSUM62 matrix has been used, where every amino acid is coded using the corresponding row of the BLOSUM matrix.…”

Section: Methodsmentioning

confidence: 99%

“…Usually the objective function contains also a so-called regularization term. It penalizes model details of unnecessary complexity, focuses on the most relevant features, and thus avoids over-fitting of the data used for training (parameter optimization) [ 1 ]. The most commonly used regularization methods are L1 regularization, also known as Lasso [ 2 ] and L2 regularization also known as ridge regression [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features

et al. 2011

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BackgroundMachine learning methods are nowadays used for many biological prediction problems involving drugs, ligands or polypeptide segments of a protein. In order to build a prediction model a so called training data set of molecules with measured target properties is needed. For many such problems the size of the training data set is limited as measurements have to be performed in a wet lab. Furthermore, the considered problems are often complex, such that it is not clear which molecular descriptors (features) may be suitable to establish a strong correlation with the target property. In many applications all available descriptors are used. This can lead to difficult machine learning problems, when thousands of descriptors are considered and only few (e.g. below hundred) molecules are available for training.ResultsThe CoEPrA contest provides four data sets, which are typical for biological regression problems (few molecules in the training data set and thousands of descriptors). We applied the same two-step training procedure for all four regression tasks. In the first stage, we used optimized L1 regularization to select the most relevant features. Thus, the initial set of more than 6,000 features was reduced to about 50. In the second stage, we used only the selected features from the preceding stage applying a milder L2 regularization, which generally yielded further improvement of prediction performance. Our linear model employed a soft loss function which minimizes the influence of outliers.ConclusionsThe proposed two-step method showed good results on all four CoEPrA regression tasks. Thus, it may be useful for many other biological prediction problems where for training only a small number of molecules are available, which are described by thousands of descriptors.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features

et al. 2011

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“…Several methods have been developed for reconstructing disease and drug response networks from gene expression data. However, even when integrated with general interaction datasets, these reconstruction methods tend to suffer from the scarcity data and the large parameter space which often leads to overfitting and other inaccuracies [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Reconstructing cancer drug response networks using multitask learning

et al. 2017

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BackgroundTranslating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer.ResultsThe reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug.ConclusionsPredictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0471-8) contains supplementary material, which is available to authorized users.

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“…Batt et al have used symbolic model checking to find parameters in a piecewise affine differential equation (PADE) model of the gene regulation IRMA network [ 35 ]. IRMA stands for in vivo "benchmarking" of reverse-engineering and modeling approaches [ 36 ].…”

Section: Related Workmentioning

confidence: 99%

Automated parameter estimation for biological models using Bayesian statistical model checking

Hussain

Langmead

et al. 2015

BMC Bioinformatics

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BackgroundProbabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem.Domain experts usually estimate the values of these parameters by fitting the model to experimental data. Model fitting is usually expressed as an optimization problem that requires minimizing a cost-function which measures some notion of distance between the model and the data. This optimization problem is often solved by combining local and global search methods that tend to perform well for the specific application domain. When some prior information about parameters is available, methods such as Bayesian inference are commonly used for parameter learning. Choosing the appropriate parameter search technique requires detailed domain knowledge and insight into the underlying system.ResultsUsing an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model.ConclusionsWe have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.

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Exploring classification strategies with the CoEPrA 2006 contest

Abstract: A stand alone application is available at the webpage http://agknapp.chemie.fu-berlin.de/agknapp/index.php?menu=software&page=PeptideClassifier that can be used to build a model for a peptide training set to be submitted.

Cited by 11 publications

References 26 publications

Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features

Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features

Reconstructing cancer drug response networks using multitask learning

Automated parameter estimation for biological models using Bayesian statistical model checking

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