How well can machine learning predict the outcome of a soccer game, given the most commonly and freely available match data? To help answer this question and to facilitate machine learning research in soccer, we have developed the Open International Soccer Database. Version v1.0 of the Database contains essential information from 216,743 league soccer matches from 52 leagues in 35 countries. The earliest entries in the Database are from the year 2000, which is when football leagues generally adopted the "three points for a win" rule. To demonstrate the use of the Database for machine learning research, we organized the 2017 Soccer Prediction Challenge. One of the goals of the Challenge was to estimate where the limits of predictability lie, given the type of match data contained in the Database. Another goal of the Challenge was to pose a real-world machine learning problem with a fixed time line and a genuine prediction task: to develop a predictive model from the Database and then to predict the outcome of the 206 future soccer matches taking place from 31 March 2017 to the end of the regular season. The Open International Soccer Database is released as an open science project, providing a valuable resource for soccer analysts and a unique benchmark for advanced machine learning methods. Here, we describe the Database and the 2017 Soccer Prediction Challenge and its results.

/pdf/the-open-international-soccer-database-for-machine-learning-a9nxmelh52.pdf

The Open International Soccer Database for machine learning

Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information. The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations. The major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (e.g., LOOCV) and biologically (e.g., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.

/pdf/network-motif-based-identification-of-transcription-factor-4098vkqxaa.pdf

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

/pdf/an-overview-of-the-use-of-neural-networks-for-data-mining-2nackcxflx.pdf

An overview of the use of neural networks for data mining tasks

Human transthyretin (TTR) is an amyloidogenic protein whose aggregation is associated with several types of amyloid diseases The following mechanism of TTR amyloid formation has been proposed TTR tetramer at first dissociates into native monomers, which is the rate-limiting step in fibril formation The monomeric species then partially unfold to form amyloidogenic intermediates that subsequently undergo a downhill self-assembly process The amyloid deposit can be facilitated by disease-associated point mutations However, only subtle structural differences were observed between the crystal structures of the wild type and the disease-associated variants To investigate how single-point mutations influence the effective energy landscapes of TTR monomers, molecular dynamics (MD) simulations were performed on wild-type TTR and two pathogenic variants Principal coordinate analysis on MD-generated ensembles has revealed multiple unfolding pathways for each protein Amyloidogenic intermediates with the dislocated C strand-loop-D strand motif were observed only on the unfolding pathways of V30M and L55P variants and not for wild-type TTR Our study suggests that the sequence-dependent unfolding pathway plays a crucial role in the amyloidogenicity of TTR Analyses of side chain concerted motions indicate that pathogenic mutations on "edge strands" disrupt the delicate side chain correlated motions, which in turn may alter the sequence of unfolding events

/pdf/the-sequence-dependent-unfolding-pathway-plays-a-critical-1q1ab8jt8u.pdf

The sequence-dependent unfolding pathway plays a critical role in the amyloidogenicity of transthyretin.

In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.

/pdf/computationally-efficient-induction-of-classification-rules-36sk31vv9q.pdf

Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks

Objectives. The prediction of protein structure and the precise understanding of protein folding and unfolding processes remains one of the greatest challenges in structural biology and bioinformatics. Computer simulations based on molecular dynamics (MD) are at the forefront of the effort to gain a deeper understanding of these complex processes. Currently, these MD simulations are usually on the order of tens of nanoseconds, generate a large amount of conformational data and are computationally expensive. More and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data.Methods. To adequately organize, manage, and analyze the data generated by unfolding simulation studies, we designed a data warehouse system that is embedded in a grid environment to facilitate the seamless sharing of available computer resources and thus enable many groups to share complex molecular dynamics simulations on a more regular basis.Results.To gain insight into the conformational fluctuations and stability of the monomeric forms of the amyloidogenic protein transthyretin (TTR), molecular dynamics unfolding simulations of the monomer of human TTR have been conducted. Trajectory data and meta-data of the wild-type (WT) protein and the highly amyloidogenic variant L55P-TTR represent the test case for the data warehouse.Conclusions.Web and grid services, especially pre-defined data mining services that can run on or ‘near’ the data repository of the data warehouse, are likely to play a pivotal role in the analysis of molecular dynamics unfolding data.

/pdf/towards-data-warehousing-and-mining-of-protein-unfolding-8ninzu1hga.pdf

Towards data warehousing and mining of protein unfolding simulation data.

Tienda online donde Comprar Fundamentals of Data Mining in Genomics and Proteomics al precio 85,04 € de Dubitzky, Werner | Granzow, Martin | Berrar, Daniel P., tienda de Libros de Medicina, Libros de Biologia - Bioquimica

Comprar Fundamentals of Data Mining in Genomics and Proteomics | Dubitzky, Werner | 9780387475080 | Springer

The 2017 Soccer Prediction Challenge

Jeffreys-Lindley Paradox in Machine Learning

Knowledge Discovery in Life Science Literature: International Workshop, KDLL 2006, Singapore, April 9, 2006, Proceedings (Lecture Notes in Computer Science / Lecture Notes in Bioinformatics)

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