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The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training...

Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis p...

This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and...

Diagnostic systems of several kinds are used to distinguish between two classes of events, essentially "signals" and "noise". For them, analysis in terms of the "relative operating characteristic" of signal detection the...

Watershed models are powerful tools for simulating the effect of watershed processes and management on soil and water resources. However, no comprehensive guidance is available to facilitate model evaluation in terms of ...

Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including sk...

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same...

The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provid...

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-...

Summary: After more than fifteen years of existence, the R package ape has continuously grown its contents, and has been used by a growing community of users. The release of version 5.0 has marked a leap towards a modern...

Motivation: Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter tri...

Materias / palabras clave: Cluster analysis; Spectral clustering; CURE data clustering algorithm; Computer science; Canopy clustering algorithm; Correlation clustering; Intuition; Theoretical computer science; Algorithm; Data mining; Artificial in...

Materias / palabras clave: Computer science; Statistical analysis; Computational statistics; Statistical graphics; Statistics; Data science; Data mining; Mathematics; Machine learning; Computer graphics (images); Graphics

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are t...

Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existin...

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for informatio...

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of gr...

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this...

Materias / palabras clave: data mining, business analytics, data-driven management decisions, digital economy, data quality and security, data science; Science: Mathematics: Instruments and machines: Electronic computers. Computer science

Materias / palabras clave: computer science, data mining, mobile computing, artificial intelligence, information technology, computer networking; Technology: Technology (General): Industrial engineering. Management engineering: Information technol...

Materias / palabras clave: data analysis, data mining, data enrichment; Medicine: Medicine (General): Computer applications to medicine. Medical informatics

Materias / palabras clave: computational mathematics, data science, numerical methods, theoretical analysis, machine learning, data mining; Science: Mathematics: Instruments and machines: Electronic computers. Computer science

Materias / palabras clave: data mining, information systems, biotechnology, artificial intelligence, software engineering, computer science; Science: Mathematics: Instruments and machines: Electronic computers. Computer science

Materias / palabras clave: data science, data mining, big data, artificial intelligence, computer science, machine learning; Science: Mathematics: Instruments and machines: Electronic computers. Computer science