GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest

<p>Abstract</p> <p>Background</p> <p>Microarray data are often used for patient classification and gene selection. An appropriate tool for end users and biomedical researchers should combine user friendliness with statistical rigor, including carefully avoiding selection biases and allowing analysis of multiple solutions, together with access to additional functional information of selected genes. Methodologically, such a tool would be of greater use if it incorporates state-of-the-art computational approaches and makes source code available.</p> <p>Results</p> <p>We have developed GeneSrF, a web-based tool, and varSelRF, an R package, that implement, in the context of patient classification, a validated method for selecting very small sets of genes while preserving classification accuracy. Computation is parallelized, allowing to take advantage of multicore CPUs and clusters of workstations. Output includes bootstrapped estimates of prediction error rate, and assessments of the stability of the solutions. Clickable tables link to additional information for each gene (GO terms, PubMed citations, KEGG pathways), and output can be sent to PaLS for examination of PubMed references, GO terms, KEGG and and Reactome pathways characteristic of sets of genes selected for class prediction. The full source code is available, allowing to extend the software. The web-based application is available from <url>http://genesrf2.bioinfo.cnio.es</url>. All source code is available from Bioinformatics.org or The Launchpad. The R package is also available from CRAN.</p> <p>Conclusion</p> <p>varSelRF and GeneSrF implement a validated method for gene selection including bootstrap estimates of classification error rate. They are valuable tools for applied biomedical researchers, specially for exploratory work with microarray data. Because of the underlying technology used (combination of parallelization with web-based application) they are also of methodological interest to bioinformaticians and biostatisticians.</p

SignS: a parallelized, open-source, freely available, web-based tool for gene selection and molecular signatures for survival and censored data

<p>Abstract</p> <p>Background</p> <p>Censored data are increasingly common in many microarray studies that attempt to relate gene expression to patient survival. Several new methods have been proposed in the last two years. Most of these methods, however, are not available to biomedical researchers, leading to many re-implementations from scratch of ad-hoc, and suboptimal, approaches with survival data.</p> <p>Results</p> <p>We have developed SignS (Signatures for Survival data), an open-source, freely-available, web-based tool and R package for gene selection, building molecular signatures, and prediction with survival data. SignS implements four methods which, according to existing reviews, perform well and, by being of a very different nature, offer complementary approaches. We use parallel computing via MPI, leading to large decreases in user waiting time. Cross-validation is used to asses predictive performance and stability of solutions, the latter an issue of increasing concern given that there are often several solutions with similar predictive performance. Biological interpretation of results is enhanced because genes and signatures in models can be sent to other freely-available on-line tools for examination of PubMed references, GO terms, and KEGG and Reactome pathways of selected genes.</p> <p>Conclusion</p> <p>SignS is the first web-based tool for survival analysis of expression data, and one of the very few with biomedical researchers as target users. SignS is also one of the few bioinformatics web-based applications to extensively use parallelization, including fault tolerance and crash recovery. Because of its combination of methods implemented, usage of parallel computing, code availability, and links to additional data bases, SignS is a unique tool, and will be of immediate relevance to biomedical researchers, biostatisticians and bioinformaticians.</p

Multidentate Terephthalamidate and Hydroxypyridonate Ligands: Towards New Orally Active Chelators

The limitations of current therapies for the treatment of iron overload or radioisotope contamination have stimulated efforts to develop new orally bioavailable iron and actinide chelators. Siderophore-inspired tetradentate, hexadentate and octadentate terephthalamidate and hydroxypyridonate ligands were evaluated in vivo as selective and efficacious iron or actinide chelating agents, with several metal loading and ligand assessment procedures, using {sup 59}Fe, {sup 238}Pu, and {sup 241}Am as radioactive tracers. The compounds presented in this study were compared to commercially available therapeutic sequestering agents [deferoxamine (DFO) for iron and diethylenetriaminepentaacetic acid (DPTA) for actinides] and are unrivaled in terms of affinity, selectivity and decorporation efficacy, which attests to the fact that high metal affinity may overcome the low bioavailability properties commonly associated to multidenticity

Lipophilic aroylhydrazone chelator HNTMB and its multiple effects on ovarian cancer cells

<p>Abstract</p> <p>Background</p> <p>Metal chelators have gained much attention as potential anti-cancer agents. However, the effects of chelators are often linked solely to their capacity to bind iron while the potential complexation of other trace metals has not been fully investigated. In present study, we evaluated the effects of various lipophilic aroylhydrazone chelators (AHC), including novel compound HNTMB, on various ovarian cancer cell lines (SKOV-3, OVCAR-3, NUTU-19).</p> <p>Methods</p> <p>Cell viability was analyzed via MTS cytotoxicity assays and NCI60 cancer cell growth screens. Apoptotic events were monitored via Western Blot analysis, fluorescence microscopy and TUNEL assay. FACS analysis was carried out to study Cell Cycle regulation and detection of intracellular Reactive Oxygen Species (ROS)</p> <p>Results</p> <p>HNTMB displayed high cytotoxicity (IC50 200-400 nM) compared to previously developed AHC (oVtBBH, HNtBBH, StBBH/206, HNTh2H/315, HNI/311; IC50 0.8-6 μM) or cancer drug Deferoxamine, a hexadentate iron-chelator (IC50 12-25 μM). In a NCI60 cancer cell line screen HNTMB exhibited growth inhibitory effects with remarkable differences in specificity depending on the cell line studied (GI50 10 nM-2.4 μM). In SKOV-3 ovarian cancer cells HNTMB treatment led to chromatin fragmentation and activation of the extrinsic and intrinsic pathways of apoptosis with specific down-regulation of Bcl-2. HNTMB caused delayed cell cycle progression of SKOV-3 through G2/M phase arrest. HNTMB can chelate iron and copper of different oxidation states. Complexation with copper lead to high cytotoxicity via generation of reactive oxygen species (ROS) while treatment with iron complexes of the drug caused neither cytotoxicity nor increased ROS levels.</p> <p>Conclusions</p> <p>The present report suggests that both, non-complexed HNTMB as a chelator of intracellular trace-metals as well as a cytotoxic HNTMB/copper complex may be developed as potential therapeutic drugs in the treatment of ovarian and other solid tumors.</p

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

A Generalized Singular Value Decomposition Strategy for Estimating the Block Recursive Simultaneous Equations Model

A new strategy for deriving the three-stage least squares (3SLS) estimator of the simultaneous equations model (SEM) is proposed. The main numerical tool employed is the generalized singular value decomposition. This provides a numerical estimation procedure which can tackle efficiently the particular case when the variance-covariance matrix is singular. The proposed algorithm is further adapted to deal with the special case of the block-recursive SEM. The block diagonal structure of the variance-covariance matrix is exploited in order to reduce significantly the computational burden. Experimental results are presented to illustrate the computational efficiency of the new estimation strategy when compared with the equivalent method that ignores the block-recursive structure of the SEM