Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography.

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross-Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

Simulation-based estimation of branching models for LTR retrotransposons.

Motivation: LTR retrotransposons are mobile elements that are able, like retroviruses, to copy and move inside eukaryotic genomes. In the present work, we propose a branching model for studying the propagation of LTR retrotransposons in these genomes. This model allows us to take into account both the positions and the degradation level of LTR retrotransposons copies. In our model, the duplication rate is also allowed to vary with the degradation level. Results: Various functions have been implemented in order to simulate their spread and visualization tools are proposed. Based on these simulation tools, we have developed a first method to evaluate the parameters of this propagation model. We applied this method to the study of the spread of the transposable elements ROO, GYPSY and DM412 on a chromosome of Drosophila melanogaster . Availability and Implementation: Our proposal has been implemented using Python software. Source code is freely available on the web at https://github.com/SergeMOULIN/retrotransposons-spread . Contact: serge.moulin@univ-fcomte.fr. Supplementary information: are available at Bioinformatics online.

A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis.

BACKGROUND: Non-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing and/or corrupted data due to outliers. RESULTS: An essential property for missing data imputation and detection of outliers is that the uncorrupted data matrix is low rank, i.e. has only a small number of degrees of freedom. We devise a new version of the Bregman proximal idea which preserves nonnegativity and mix it with the Augmented Lagrangian approach for simultaneous reconstruction of the features of interest and detection of the outliers using a sparsity promoting ℓ 1 penality. CONCLUSIONS: An application to the analysis of gene expression data of patients with bladder cancer is finally proposed.

Dendrochemical assessment of mercury releases from a pond and dredged-sediment landfill impacted by a chlor-alkali plant.

Although current Hg emissions from industrial activities may be accurately monitored, evidence of past releases to the atmosphere must rely on one or more environmental proxies. We used Hg concentrations in tree cores collected from poplars and willows to investigate the historical changes of Hg emissions from a dredged sediment landfill and compared them to a nearby control location. Our results demonstrated the potential value of using dendrochemistry to record historical Hg emissions from past industrial activities.

Mixtures of GAMs for habitat suitability analysis with overdispersed presence / absence data.

A new approach to species distribution modelling based on unsupervised classification via a finite mixture of GAMs incorporating habitat suitability curves is proposed. A tailored EM algorithm is outlined for computing maximum likelihood estimates. Several submodels incorporating various parameter constraints are explored. Simulation studies confirm, that under certain constraints, the habitat suitability curves are recovered with good precision. The method is also applied to a set of real data concerning presence/absence of observable small mammal indices collected on the Tibetan plateau. The resulting classification was found to correspond to species-level differences in habitat preference described in previous ecological work.

SpCLUST: Towards a fast and reliable clustering for potentially divergent biological sequences.

This paper presents SpCLUST, a new C++ package that takes a list of sequences as input, aligns them with MUSCLE, computes their similarity matrix in parallel and then performs the clustering. SpCLUST extends a previously released software by integrating additional scoring matrices which enables it to cover the clustering of amino-acid sequences. The similarity matrix is now computed in parallel according to the master/slave distributed architecture, using MPI. Performance analysis, realized on two real datasets of 100 nucleotide sequences and 1049 amino-acids ones, show that the resulting library substantially outperforms the original Python package. The proposed package was also intensively evaluated on simulated and real genomic and protein data sets. The clustering results were compared to the most known traditional tools, such as UCLUST, CD-HIT and DNACLUST. The comparison showed that SpCLUST outperforms the other tools when clustering divergent sequences, and contrary to the others, it does not require any user intervention or prior knowledge about the input sequences.

A clustering package for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Model.

In this article, a new Python package for nucleotide sequences clustering is proposed. This package, freely available on-line, implements a Laplacian eigenmap embedding and a Gaussian Mixture Model for DNA clustering. It takes nucleotide sequences as input, and produces the optimal number of clusters along with a relevant visualization. Despite the fact that we did not optimise the computational speed, our method still performs reasonably well in practice. Our focus was mainly on data analytics and accuracy and as a result, our approach outperforms the state of the art, even in the case of divergent sequences. Furthermore, an a priori knowledge on the number of clusters is not required here. For the sake of illustration, this method is applied on a set of 100 DNA sequences taken from the mitochondrially encoded NADH dehydrogenase 3 (ND3) gene, extracted from a collection of Platyhelminthes and Nematoda species. The resulting clusters are tightly consistent with the phylogenetic tree computed using a maximum likelihood approach on gene alignment. They are coherent too with the NCBI taxonomy. Further test results based on synthesized data are then provided, showing that the proposed approach is better able to recover the clusters than the most widely used software, namely Cd-hit-est and BLASTClust.

Systematic investigations of gene effects on both topologies and supports: An Echinococcus illustration.

In this paper, we propose a high performance computing toolbox implementing efficient statistical methods for the study of phylogenies. This toolbox, which implements logit models and LASSO-type penalties, gives a way to better understand, measure, and compare the impact of each gene on a global phylogeny. As an application, we study the Echinococcus phylogeny, which is often considered as a particularly difficult example. Mitochondrial and nuclear genomes (19 coding sequences) of nine Echinococcus species are considered in order to investigate the molecular phylogeny of this genus. First, we check that the 19 gene trees lead to 19 totally different unsupported topologies (a topology is the sister relationship when both branch lengths and supports are ignored in a phylogenetic tree), while using the 19 genes as a whole are not sufficient for estimating the phylogeny. In order to circumvent this issue and understand the impact of the genes, we computed 43,796 trees using combinations ranging from 13 to 19 genes. By doing so, 15 topologies are obtained. Four particular topologies, appearing more robust and frequent, are then selected for more precise investigation. Refining further our statistical analysis, a particularly robust topology is extracted. We also carefully demonstrate the influence of nuclear genes on the likelihood of the phylogeny.

Gene-expression signature functional annotation of breast cancer tumours in function of age.

BACKGROUND: Breast cancer biological characteristics change as age advances. Today, there is a lack of knowledge regarding age-specific molecular alterations that characterize breast tumours, notably in elderly patients. The vast majority of studies that aimed at exploring breast cancer in function of age are based on clinico-pathological data. Gene-expression signatures (GES), which in some ways capture biological information in a non-reductionist manner, represent powerful tools able to explore tumour heterogeneity. METHODS: Twenty-five GES were used for functional annotation of breast tumours in function of age: five for molecular subtyping, seven for immune response, three for metabolism, seven for critical pathways in cancer and three for prognosis. Affymetrix® genomics datasets were exclusively used to avoid cross-platform normalization issues. Available corresponding clinico-pathological data were also retrieved and analysed. RESULTS: Fifteen publicly available datasets were pooled for a total of 2378 breast cancer patients (whole cohort), out of whom 1413 were of Caucasian origin. Three age groups were defined: ≤ 40 years (AG1), > 40 to < 70 years (AG2) and ≥ 70 years (AG3). We confirmed that age influenced the incidence of molecular subtypes. We found a significant growing incidence of luminal B and a decreasing kinetics for basal-like in function of age. We showed that AG3 luminal B tumours were less aggressive than AG1 luminal B tumours based on different GES (iron metabolism, mitochondrial oxidative phosphorylation and reactive stroma), recurrence score prognostic GES and histological grade (SBR). Contrary to tumours of young patients, tumours of elderly patients concentrated favourable GES scores: high oestrogen receptor and mitochondrial oxidative phosphorylation, low proliferation, basal-like, glycolysis, chromosomal instability and iron metabolism, and low GES prognostic scores (van't Veer 70-GES, genomic grade index and recurrence score). CONCLUSIONS: Functional annotation of breast tumours by means of 25 GES demonstrated a decreasing aggressiveness of breast tumours in function of age. This strategy, which can be strengthened by increasing the number of representative GES to gain more insight into biological systems involved in this disease, provides a framework to develop rational therapeutic strategies in function of age.