Principal Component Analysis of Lack of Cohesion in Methods (LCOM) metrics

In this report, we study the Lack of Cohesion in Methods (LCOM) metric for an object-oriented system and examine the suitability of eight variations of this metric through a principal component analysis. 1

Principal Component Analysis of Lack of Cohesion in Methods (LCOM) metrics

In this report, we study the Lack of Cohesion in Methods (LCOM) metric for an object-oriented system and examine the suitability of eight variations of this metric through a principal component analysis. 1. Introduction One concern in software engineering is how high-quality software can be produced with predictable costs and time. Software metrics provide a quantitative means to predict the software development process and evaluate the quality of the software products. Several software metrics have been proposed to measure the complexity in the procedural paradigm. Some of the metrics which are frequently used in the procedural paradigm are McCabe's cyclomatic complexity metric [1] and Halstead's software science metric [2]. The object-oriented programming paradigm is often claimed to allow a faster development pace and higher quality of software. However, software metrics are less well studied in the object-oriented paradigm. A small number of metrics have been proposed to measur..

The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies.

BACKGROUND: Precision medicine in oncology relies on rapid associations between patient-specific variations and targeted therapeutic efficacy. Due to the advancement of genomic analysis, a vast literature characterizing cancer-associated molecular aberrations and relative therapeutic relevance has been published. However, data are not uniformly reported or readily available, and accessing relevant information in a clinically acceptable time-frame is a daunting proposition, hampering connections between patients and appropriate therapeutic options. One important therapeutic avenue for oncology patients is through clinical trials. Accordingly, a global view into the availability of targeted clinical trials would provide insight into strengths and weaknesses and potentially enable research focus. However, data regarding the landscape of clinical trials in oncology is not readily available, and as a result, a comprehensive understanding of clinical trial availability is difficult. RESULTS: To support clinical decision-making, we have developed a data loader and mapper that connects sequence information from oncology patients to data stored in an in-house database, the JAX Clinical Knowledgebase (JAX-CKB), which can be queried readily to access comprehensive data for clinical reporting via customized reporting queries. JAX-CKB functions as a repository to house expertly curated clinically relevant data surrounding our 358-gene panel, the JAX Cancer Treatment Profile (JAX CTP), and supports annotation of functional significance of molecular variants. Through queries of data housed in JAX-CKB, we have analyzed the landscape of clinical trials relevant to our 358-gene targeted sequencing panel to evaluate strengths and weaknesses in current molecular targeting in oncology. Through this analysis, we have identified patient indications, molecular aberrations, and targeted therapy classes that have strong or weak representation in clinical trials. CONCLUSIONS: Here, we describe the development and disseminate system methods for associating patient genomic sequence data with clinically relevant information, facilitating interpretation and providing a mechanism for informing therapeutic decision-making. Additionally, through customized queries, we have the capability to rapidly analyze the landscape of targeted therapies in clinical trials, enabling a unique view into current therapeutic availability in oncology. Hum Genomics 2016; 10(1):4

SBML level 3 package: spatial processes, version 1, release 1

While many biological processes can be modeled by abstracting away the space in which those processes occur, some modeling (particularly at the cellular level) requires space itself to be modeled, with processes happening not in well-mixed compartments, but spatially-defined compartments. The SBML Level 3 Core specification does not include an explicit mechanism to encode geometries and spatial processes in a model, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactic constructs. The SBML Spatial Processes package for SBML Level 3 adds the necessary features to allow models to encode geometries and other spatial information about the elements and processes it describes

Molecular Genetic Diversity of Major Indian Rice Cultivars over Decadal Periods

<div><p>Genetic diversity in representative sets of high yielding varieties of rice released in India between 1970 and 2010 was studied at molecular level employing hypervariable microsatellite markers. Of 64 rice SSR primer pairs studied, 52 showed polymorphism, when screened in 100 rice genotypes. A total of 184 alleles was identified averaging 3.63 alleles per locus. Cluster analysis clearly grouped the 100 genotypes into their respective decadal periods i.e., 1970s, 1980s, 1990s and 2000s. The trend of diversity over the decadal periods estimated based on the number of alleles (<i>Na</i>), allelic richness (<i>Rs</i>), Nei’s genetic diversity index (<i>He</i>), observed heterozygosity (<i>Ho</i>) and polymorphism information content (PIC) revealed increase of diversity over the periods in year of releasewise and longevitywise classification of rice varieties. Analysis of molecular variance (AMOVA) suggested more variation in within the decadal periods than among the decades. Pairwise comparison of population differentiation (<i>Fst</i>) among decadal periods showed significant difference between all the pairs except a few. Analysis of trends of appearing and disappearing alleles over decadal periods showed an increase in the appearance of alleles and decrease in disappearance in both the categories of varieties. It was obvious from the present findings, that genetic diversity was progressively on the rise in the varieties released during the decadal periods, between 1970s and 2000s.</p></div

Changes in number of alleles (<i>Na</i>), Nei’s genetic diversity (<i>He</i>) and PIC values over decadal periods.

<p>Changes in number of alleles (<i>Na</i>), Nei’s genetic diversity (<i>He</i>) and PIC values over decadal periods.</p

Molecular diversity parameters of the microsatellite markers used in the study.

<p>Chr.- Chromosome; <i>Na</i>-Number of alleles; PIC-Polymorphism Information Content; <i>Rs</i>: Allelic richness; <i>Ho</i>- Observed heterozygosity; <i>He</i>- Nei’s genetic diversity; <i>I</i>- Shannon Index; SD-Standard Deviation.</p

Rice varieties used in the present study.

<p>Rice varieties used in the present study.</p

A representative gel picture of screening of rice varieties with RM562.

<p>For decoding of the numbers refer Table S1.</p