Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This article describes the software suite GROMACS (Groningen MAchine for Chemical Simulation) that was developed at the University of Groningen, The Netherlands, in the early 1990s. The software, written in ANSI C, originates from a parallel hardware project, and is well suited for parallelization on processor clusters. By careful optimization of neighbor searching and of inner loop performance, GROMACS is a very fast program for molecular dynamics simulation. It does not have a force field of its own, but is compatible with GROMOS, OPLS, AMBER, and ENCAD force fields. In addition, it can handle polarizable shell models and flexible constraints. The program is versatile, as force routines can be added by the user, tabulated functions can be specified, and analyses can be easily customized. Nonequilibrium dynamics and free energy determinations are incorporated. Interfaces with popular quantum-chemical packages (MOPAC, GAMES-UK, GAUSSIAN) are provided to perform mixed MM/QM simulations. The package includes about 100 utility and analysis programs. GROMACS is in the public domain and distributed (with source code and documentation) under the GNU General Public License. It is maintained by a group of developers from the Universities of Groningen, Uppsala, and Stockholm, and the Max Planck Institute for Polymer Research in Mainz. Its Web site is http://www.gromacs.org.

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GROMACS: Fast, flexible, and free

Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

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Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The human body is the collection of our own, endogenously produced enzymes, hormones, and antibodies that are responsible for maintaining homeostasis, stabilizing wounds, fighting infections, neutralizing toxins, keeping cancerous cells in check, and generally keeping us alive. Most of today's protein pharmaceuticals fall into 5 general classes, including hormones, vaccines, antibiotics, antibodies, and enzymes. This article describes the general features of pharmaceutically important hormones, antibodies, and enzymes.


Keywords:

enzymes;
hormones;
antibodies;
protein pharmaceuticals

Proteins: Hormones, Enzymes, and Monoclonal Antibodies—Background

ER-Tac, taken once per day, is associated with improved adherence. This study examined the potential patient and clinical factors that influence clinicians to convert pediatric patients from immediate-release to ER-Tac. This prospective multi-center observational study followed Canadian pediatric kidney transplant recipients up to 5 years post-transplant. Cox Proportional Hazards Regression was used to examine the influence of factors on conversion to ER-Tac. Sixty-six participants were included in this analysis. For every additional year of age at the time of transplant, the likelihood of conversion was more than doubled (HR 2.54, CI 1.83, 3.54, P < 0.001). The impact of age reduced by three percent for every month after transplant (HR 0.97, CI 0.95, 0.98, P < 0.001). Girls were more likely to be converted than boys (HR 3.78, CI 1.35, 10.6, P 0.01). Adherence measures (MAM-MM and tacrolimus trough variability), individual barriers to adherence, renal function, HLA mismatch, and rejection were not significant predictors of conversion in the final regression model. ER-Tac was preferentially prescribed to older age and female patients. Female sex and adolescence are both associated with worse graft outcomes, but we found no link between individualized markers of adherence/graft risk and conversion. Clinicians appeared to be using demographic features to distinguish patients at perceived higher risk and converted accordingly, without a case-by-case evaluation of who is more susceptible to poor outcomes.

Age and sex determine conversion from immediate-release to extended-release tacrolimus in a multi-center cohort of Canadian pediatric renal transplant recipients.

David S. Wishart

Papers

Proteins: Hormones, Enzymes, and Monoclonal Antibodies—Background

Age and sex determine conversion from immediate-release to extended-release tacrolimus in a multi-center cohort of Canadian pediatric renal transplant recipients.

Towards a Rosetta stone for metabolomics: recommendations to overcome inconsistent metabolite nomenclature

Development of a predictive algorithm to identify pre-school children at risk for behavior changes associated with sleep-related breathing disorders.