20,104 research outputs found
Random Forests for Big Data
Big Data is one of the major challenges of statistical science and has
numerous consequences from algorithmic and theoretical viewpoints. Big Data
always involve massive data but they also often include online data and data
heterogeneity. Recently some statistical methods have been adapted to process
Big Data, like linear regression models, clustering methods and bootstrapping
schemes. Based on decision trees combined with aggregation and bootstrap ideas,
random forests were introduced by Breiman in 2001. They are a powerful
nonparametric statistical method allowing to consider in a single and versatile
framework regression problems, as well as two-class and multi-class
classification problems. Focusing on classification problems, this paper
proposes a selective review of available proposals that deal with scaling
random forests to Big Data problems. These proposals rely on parallel
environments or on online adaptations of random forests. We also describe how
related quantities -- such as out-of-bag error and variable importance -- are
addressed in these methods. Then, we formulate various remarks for random
forests in the Big Data context. Finally, we experiment five variants on two
massive datasets (15 and 120 millions of observations), a simulated one as well
as real world data. One variant relies on subsampling while three others are
related to parallel implementations of random forests and involve either
various adaptations of bootstrap to Big Data or to "divide-and-conquer"
approaches. The fifth variant relates on online learning of random forests.
These numerical experiments lead to highlight the relative performance of the
different variants, as well as some of their limitations
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
A computational pipeline for the diagnosis of CVID patients
Common variable immunodeficiency (CVID) is one of the most frequently diagnosed primary antibody deficiencies (PADs), a group of disorders characterized by a decrease in one or more immunoglobulin (sub) classes and/or impaired antibody responses caused by inborn defects in B cells in the absence of other major immune defects. CVID patients suffer from recurrent infections and disease-related, non-infectious, complications such as autoimmune manifestations, lymphoproliferation, and malignancies. A timely diagnosis is essential for optimal follow-up and treatment. However, CVID is by definition a diagnosis of exclusion, thereby covering a heterogeneous patient population and making it difficult to establish a definite diagnosis. To aid the diagnosis of CVID patients, and distinguish them from other PADs, we developed an automated machine learning pipeline which performs automated diagnosis based on flow cytometric immunophenotyping. Using this pipeline, we analyzed the immunophenotypic profile in a pediatric and adult cohort of 28 patients with CVID, 23 patients with idiopathic primary hypogammaglobulinemia, 21 patients with IgG subclass deficiency, six patients with isolated IgA deficiency, one patient with isolated IgM deficiency, and 100 unrelated healthy controls. Flow cytometry analysis is traditionally done by manual identification of the cell populations of interest. Yet, this approach has severe limitations including subjectivity of the manual gating and bias toward known populations. To overcome these limitations, we here propose an automated computational flow cytometry pipeline that successfully distinguishes CVID phenotypes from other PADs and healthy controls. Compared to the traditional, manual analysis, our pipeline is fully automated, performing automated quality control and data pre-processing, automated population identification (gating) and deriving features from these populations to build a machine learning classifier to distinguish CVID from other PADs and healthy controls. This results in a more reproducible flow cytometry analysis, and improves the diagnosis compared to manual analysis: our pipelines achieve on average a balanced accuracy score of 0.93 (+/- 0.07), whereas using the manually extracted populations, an averaged balanced accuracy score of 0.72 (+/- 0.23) is achieved
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Several efforts to predict student failure rate (SFR) at school accurately
still remains a core problem area faced by many in the educational sector. The
procedure for forecasting SFR are rigid and most often times require data
scaling or conversion into binary form such as is the case of the logistic
model which may lead to lose of information and effect size attenuation. Also,
the high number of factors, incomplete and unbalanced dataset, and black boxing
issues as in Artificial Neural Networks and Fuzzy logic systems exposes the
need for more efficient tools. Currently the application of Genetic Programming
(GP) holds great promises and has produced tremendous positive results in
different sectors. In this regard, this study developed GPSFARPS, a software
application to provide a robust solution to the prediction of SFR using an
evolutionary algorithm known as multi-gene genetic programming. The approach is
validated by feeding a testing data set to the evolved GP models. Result
obtained from GPSFARPS simulations show its unique ability to evolve a suitable
failure rate expression with a fast convergence at 30 generations from a
maximum specified generation of 500. The multi-gene system was also able to
minimize the evolved model expression and accurately predict student failure
rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap
with arXiv:1403.0623 by other author
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