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Modern Applied Statistics With S

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore

PL02-05 

All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.

Patterns of Somatic Mutation in Human Cancer Genomes

BACKGROUND: A software engineering systematic map is a defined method to build a classification scheme and structure a software engineering field of interest. The analysis of results focuses on frequencies of publications for categories within the scheme. Thereby, the coverage of the research field can be determined. Different facets of the scheme can also be combined to answer more specific research questions.

OBJECTIVE: We describe how to conduct a systematic mapping study in software engineering and provide guidelines. We also compare systematic maps and systematic reviews to clarify how to chose between them. This comparison leads to a set of guidelines for systematic maps.

METHOD: We have defined a systematic mapping process and applied it to complete a systematic mapping study. Furthermore, we compare systematic maps with systematic reviews by systematically analyzing existing systematic reviews.

RESULTS: We describe a process for software engineering systematic mapping studies and compare it to systematic reviews. Based on this, guidelines for conducting systematic maps are defined.

CONCLUSIONS: Systematic maps and reviews are different in terms of goals, breadth, validity issues and implications. Thus, they should be used complementarily and require different methods (e.g., for analysis).

/pdf/systematic-mapping-studies-in-software-engineering-4h4l7vgsw3.pdf

Systematic mapping studies in software engineering

Background: Systematic literature studies have become common in software engineering, and hence it is important to understand how to conduct them efficiently and reliably.Objective: This paper presents guidelines for conducting literature reviews using a snowballing approach, and they are illustrated and evaluated by replicating a published systematic literature review.Method: The guidelines are based on the experience from conducting several systematic literature reviews and experimenting with different approaches.Results: The guidelines for using snowballing as a way to search for relevant literature was successfully applied to a systematic literature review.Conclusions: It is concluded that using snowballing, as a first search strategy, may very well be a good alternative to the use of database searches.

/pdf/guidelines-for-snowballing-in-systematic-literature-studies-106rgo5oni.pdf

Guidelines for snowballing in systematic literature studies and a replication in software engineering

Provides the software estimation research community with a better understanding of the meaning of, and relationship between, two statistics that are often used to assess the accuracy of predictive models: the mean magnitude relative error (MMRE) and the number of predictions within 25% of the actual, pred(25). It is demonstrated that MMRE and pred(25) are, respectively, measures of the spread and the kurtosis of the variable z, where z=estimate/actual. Thus, z is considered to be a measure of accuracy, and statistics such as MMRE and pred(25) to be measures of properties of the distribution of z. It is suggested that measures of the central location and skewness of z, as well as measures of spread and kurtosis, are necessary. Furthermore, since the distribution of z is non-normal, non-parametric measures of these properties may be needed. For this reason, box-plots of z are useful alternatives to simple summary metrics. It is also noted that the simple residuals are better behaved than the z variable, and could also be used as the basis for comparing prediction systems.

/pdf/what-accuracy-statistics-really-measure-3bihiflsam.pdf

What accuracy statistics really measure

Determining the factors that have an influence on software systems development and deployment project outcomes has been the focus of extensive and ongoing research for more than 30 years. We provide here a survey of the research literature that has addressed this topic in the period 1996–2006, with a particular focus on empirical analyses. On the basis of this survey we present a new classification framework that represents an abstracted and synthesized view of the types of factors that have been asserted as influencing project outcomes.

Factors that affect software systems development project outcomes: A survey of research

The use of regression analysis to derive predictive equations for software metrics has recently been complemented by increasing numbers of studies using non-traditional methods, such as neural networks, fuzzy logic models, case-based reasoning systems, and regression trees. There has also been an increasing level of sophistication in the regression-based techniques used, including robust regression methods, factor analysis, and more effective validation procedures. This paper examines the implications of using these methods and provides some recommendations as to when they may be appropriate. A comparison of the various techniques is also made in terms of their modelling capabilities with specific reference to software metrics.

/pdf/a-comparison-of-techniques-for-developing-predictive-models-237yfbishn.pdf

A comparison of techniques for developing predictive models of software metrics

Determining the factors that have an influence on software systems development and deployment project outcomes has been the focus of extensive and ongoing research for more than 30 years. We provide here a survey of the research literature that has addressed this topic in the period 1996-2006, with a particular focus on empirical analyses. On the basis of this survey we present a new classification framework that represents an abstracted and synthesized view of the types of factors that have been asserted as influencing project outcomes.

Stephen G. MacDonell

Papers

What accuracy statistics really measure

What accuracy statistics really measure

Factors that affect software systems development project outcomes: A survey of research

A comparison of techniques for developing predictive models of software metrics

Factors that Affect Software Systems Development Project Outcomes: A Survey of Research