The wisdom of the commons: ensemble tree classifiers for prostate cancer prognosis

Koziol, James A.; Feng, Anne C.; Jia, Zhenyu; Wang, Yipeng; Goodison, Seven; McClelland, Michael; Mercola, Dan

doi:10.1093/bioinformatics/btn354

Cited by 30 publications

(18 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of analysis tools have been developed to measure differences in local phylogenies, including but not limited to Phylo-HMM [10], SiPhy [11], and Coal-HMM [4,12]. While these methods detect changes in phylogenetic tree size and branch lengths, or match local regions with a set of phylogenetic hypotheses, they lack a component to learn hypotheses directly from the data and without supervision.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised genome-wide recognition of local relationship patterns

et al. 2013

View full text Add to dashboard Cite

BackgroundPhenomena such as incomplete lineage sorting, horizontal gene transfer, gene duplication and subsequent sub- and neo-functionalisation can result in distinct local phylogenetic relationships that are discordant with species phylogeny. In order to assess the possible biological roles for these subdivisions, they must first be identified and characterised, preferably on a large scale and in an automated fashion.ResultsWe developed Saguaro, a combination of a Hidden Markov Model (HMM) and a Self Organising Map (SOM), to characterise local phylogenetic relationships among aligned sequences using cacti, matrices of pair-wise distance measures. While the HMM determines the genomic boundaries from aligned sequences, the SOM hypothesises new cacti in an unsupervised and iterative fashion based on the regions that were modelled least well by existing cacti. After testing the software on simulated data, we demonstrate the utility of Saguaro by testing two different data sets: (i) 181 Dengue virus strains, and (ii) 5 primate genomes. Saguaro identifies regions under lineage-specific constraint for the first set, and genomic segments that we attribute to incomplete lineage sorting in the second dataset. Intriguingly for the primate data, Saguaro also classified an additional ~3% of the genome as most incompatible with the expected species phylogeny. A substantial fraction of these regions was found to overlap genes associated with both the innate and adaptive immune systems.ConclusionsSaguaro detects distinct cacti describing local phylogenetic relationships without requiring any a priori hypotheses. We have successfully demonstrated Saguaro’s utility with two contrasting data sets, one containing many members with short sequences (Dengue viral strains: n = 181, genome size = 10,700 nt), and the other with few members but complex genomes (related primate species: n = 5, genome size = 3 Gb), suggesting that the software is applicable to a wide variety of experimental populations. Saguaro is written in C++, runs on the Linux operating system, and can be downloaded from http://saguarogw.sourceforge.net/.

show abstract

Section: Introductionmentioning

confidence: 99%

Unsupervised genome-wide recognition of local relationship patterns

et al. 2013

View full text Add to dashboard Cite

show abstract

“…First, the normal and disease states can be distinguished by differential expression of the identified marker genes [24]. Second, a common discriminative gene group can be mined from multiple datasets [25], or multiple classifiers can be integrated from sets of marker genes [26,27]. These approaches enhance the robustness or consensus of sets of expression-dependent marker genes in heterogeneous disease cohorts.…”

Section: Node Biomarkers For Classification and Prediction Without Nementioning

confidence: 99%

Edge biomarkers for classification and prediction of phenotypes

Zeng

Zhang

et al. 2014

Sci. China Life Sci.

View full text Add to dashboard Cite

In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.biomarker, edge biomarker, dynamical network biomarker, classification, prediction, phenotype, disease diagnosis, disease prognosis Citation:

show abstract

“…We have also applied a panel of advanced computational strategies, including our feature-selection algorithm, to prostate cancer datasets for the purposes of identifying cell-type signatures [45], time-to-progression predictors [46] and accurate prognostic signatures [38,47]. Prostate cancer is the most common male cancer by incidence and as with breast cancer, it is the ability to predict the metastatic behavior of a patient's cancer that is of utmost importance in prostate oncology.…”

Section: Prostate Cancer Prognostic Signaturesmentioning

confidence: 99%

Derivation of cancer diagnostic and prognostic signatures from gene expression data

Goodison¹,

Sun

Urquidi

2010

Bioanalysis

View full text Add to dashboard Cite

The ability to compare genome-wide expression profiles in human tissue samples has the potential to add an invaluable molecular pathology aspect to the detection and evaluation of multiple diseases. Applications include initial diagnosis, evaluation of disease subtype, monitoring of response to therapy and the prediction of disease recurrence. The derivation of molecular signatures that can predict tumor recurrence in breast cancer has been a particularly intense area of investigation and a number of studies have shown that molecular signatures can outperform currently used clinicopathologic factors in predicting relapse in this disease. However, many of these predictive models have been derived using relatively simple computational algorithms and whether these models are at a stage of development worthy of large-cohort clinical trial validation is currently a subject of debate. In this review, we focus on the derivation of optimal molecular signatures from high-dimensional data and discuss some of the expected future developments in the field.

show abstract

The wisdom of the commons: ensemble tree classifiers for prostate cancer prognosis

Cited by 30 publications

References 25 publications

Unsupervised genome-wide recognition of local relationship patterns

Unsupervised genome-wide recognition of local relationship patterns

Edge biomarkers for classification and prediction of phenotypes

Derivation of cancer diagnostic and prognostic signatures from gene expression data

Contact Info

Product

Resources

About