Multi-Class Clustering of Cancer Subtypes through SVM Based Ensemble of Pareto-Optimal Solutions for Gene Marker Identification

With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes

Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordLOD 2019: Fifth International Conference on Machine Learning, Optimization, and Data Science, 10-13 September 2019, Siena, ItalyGaussian processes (GPs) belong to a class of probabilistic techniques that have been successfully used in different domains of machine learning and optimization. They are popular because they provide uncertainties in predictions, which sets them apart from other modelling methods providing only point predictions. The uncertainty is particularly useful for decision making as we can gauge how reliable a prediction is. One of the fundamental challenges in using GPs is that the efficacy of a model is conferred by selecting an appropriate kernel and the associated hyperparameter values for a given problem. Furthermore, the training of GPs, that is optimizing the hyperparameters using a data set is traditionally performed using a cost function that is a weighted sum of data fit and model complexity, and the underlying trade-off is completely ignored. Addressing these challenges and shortcomings, in this article, we propose the following automated training scheme. Firstly, we use a weighted product of multiple kernels with a view to relieve the users from choosing an appropriate kernel for the problem at hand without any domain specific knowledge. Secondly, for the first time, we modify GP training by using a multi-objective optimizer to tune the hyperparameters and weights of multiple kernels and extract an approximation of the complete trade-off front between data-fit and model complexity. We then propose to use a novel solution selection strategy based on mean standardized log loss (MSLL) to select a solution from the estimated trade-off front and finalise training of a GP model. The results on three data sets and comparison with the standard approach clearly show the potential benefit of the proposed approach of using multi-objective optimization with multiple kernels.Natural Environment Research Council (NERC

Revealed Preference Dimension via Matrix Sign Rank

Given a data-set of consumer behaviour, the Revealed Preference Graph succinctly encodes inferred relative preferences between observed outcomes as a directed graph. Not all graphs can be constructed as revealed preference graphs when the market dimension is fixed. This paper solves the open problem of determining exactly which graphs are attainable as revealed preference graphs in $d$-dimensional markets. This is achieved via an exact characterization which closely ties the feasibility of the graph to the Matrix Sign Rank of its signed adjacency matrix. The paper also shows that when the preference relations form a partially ordered set with order-dimension $k$, the graph is attainable as a revealed preference graph in a $k$-dimensional market.Comment: Submitted to WINE `1

Histone arginine methylation regulates pluripotency in the early mouse embryo

It has been generally accepted that the mammalian embryo starts its development with all cells identical, and only when inside and outside cells form do differences between cells first emerge. However, recent findings show that cells in the mouse embryo can differ in their developmental fate and potency as early as the four-cell stage1,2,3,4. These differences depend on the orientation and order of the cleavage divisions that generated them2,5. Because epigenetic marks are suggested to be involved in sustaining pluripotency6,7, we considered that such developmental properties might be achieved through epigenetic mechanisms. Here we show that modification of histone H3, through the methylation of specific arginine residues, is correlated with cell fate and potency. Levels of H3 methylation at specific arginine residues are maximal in four-cell blastomeres that will contribute to the inner cell mass (ICM) and polar trophectoderm and undertake full development when combined together in chimaeras. Arginine methylation of H3 is minimal in cells whose progeny contributes more to the mural trophectoderm and that show compromised development when combined in chimaeras. This suggests that higher levels of H3 arginine methylation predispose blastomeres to contribute to the pluripotent cells of the ICM. We confirm this prediction by overexpressing the H3-specific arginine methyltransferase CARM1 in individual blastomeres and show that this directs their progeny to the ICM and results in a dramatic upregulation of Nanog and Sox2. Thus, our results identify specific histone modifications as the earliest known epigenetic marker contributing to development of ICM and show that manipulation of epigenetic information influences cell fate determination

Effective Stimuli for Constructing Reliable Neuron Models

The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for selecting specific stimuli has never been rigorously justified. The present experimental and theoretical study proposes a novel framework, inspired by learning theory, for objectively selecting the stimuli that best unravel the neuron's dynamics. The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron's dynamics as attested by their ability to generalize well to the neuron's response to novel experimental stimuli. We used this framework to evaluate a variety of stimuli in different types of cortical neurons, ages and animals. Despite their simplicity, a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons. The general framework that we propose paves a new way for defining, evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose

An efficient application of goal programming to tackle multiobjective problems with recurring fitness landscapes

Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many objective problems is often a difficult task even for modern multiobjective algorithms. In some cases, multiple instances of the problem scenario present similarities in their fitness landscapes. That is, there are recurring features in the fitness landscapes when searching for solutions to different problem instances. We propose a methodology to exploit this characteristic by solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We use three goal-based objective functions and show that on benchmark instances of the multiobjective vehicle routing problem with time windows, the methodology is able to produce good results in short computation time. The methodology allows to combine the effectiveness of state-of-the-art multiobjective algorithms with the efficiency of goal programming to find good compromise solutions in problem scenarios where instances have similar fitness landscapes

An Intron-Retaining Splice Variant of Human Cyclin A2, Expressed in Adult Differentiated Tissues, Induces a G1/S Cell Cycle Arrest In Vitro

BACKGROUND: Human cyclin A2 is a key regulator of S phase progression and entry into mitosis. Alternative splice variants of the G1 and mitotic cyclins have been shown to interfere with full-length cyclin functions to modulate cell cycle progression and are therefore likely to play a role in differentiation or oncogenesis. The alternative splicing of human cyclin A2 has not yet been studied. METHODOLOGY/PRINCIPAL FINDINGS: Sequence-specific primers were designed to amplify various exon-intron regions of cyclin A2 mRNA in cell lines and human tissues. Intron retaining PCR products were cloned and sequenced and then overexpressed in HeLa cells. The subcellular localization of the splice variants was studied using confocal and time-lapse microscopy, and their impact on the cell cycle by flow cytometry, immunoblotting and histone H1 kinase activity. We found a splice variant of cyclin A2 mRNA called A2V6 that partly retains Intron 6. The gene expression pattern of A2V6 mRNA in human tissues was noticeably different from that of wild-type cyclin A2 (A2WT) mRNA. It was lower in proliferating fetal tissues and stronger in some differentiated adult tissues, especially, heart. In transfected HeLa cells, A2V6 localized exclusively in the cytoplasm whereas A2WT accumulated in the nucleus. We show that A2V6 induced a clear G1/S cell cycle arrest associated with a p21 and p27 upregulation and an inhibition of retinoblastoma protein phosphorylation. Like A2WT, A2V6 bound CDK2, but the A2V6/CDK2 complex did not phosphorylate histone H1. CONCLUSION/SIGNIFICANCE: This study has revealed that some highly differentiated human tissues express an intron-retaining cyclin A2 mRNA that induced a G1/S block in vitro. Contrary to full-length cyclin A2, which regulates cell proliferation, the A2V6 splice variant might play a role in regulating nondividing cell states such as terminal differentiation or senescence

A genetic algorithm for the one-dimensional cutting stock problem with setups

This paper investigates the one-dimensional cutting stock problem considering two conflicting objective functions: minimization of both the number of objects and the number of different cutting patterns used. A new heuristic method based on the concepts of genetic algorithms is proposed to solve the problem. This heuristic is empirically analyzed by solving randomly generated instances and also practical instances from a chemical-fiber company. The computational results show that the method is efficient and obtains positive results when compared to other methods from the literature. © 2014 Brazilian Operations Research Society

miR-337-3p and Its Targets STAT3 and RAP1A Modulate Taxane Sensitivity in Non-Small Cell Lung Cancers

NSCLC (non-small cell lung cancer) often exhibits resistance to paclitaxel treatment. Identifying the elements regulating paclitaxel response will advance efforts to overcome such resistance in NSCLC therapy. Using in vitro approaches, we demonstrated that over-expression of the microRNA miR-337-3p sensitizes NCI-H1155 cells to paclitaxel, and that miR-337-3p mimic has a general effect on paclitaxel response in NSCLC cell lines, which may provide a novel adjuvant strategy to paclitaxel in the treatment of lung cancer. By combining in vitro and in silico approaches, we identified STAT3 and RAP1A as direct targets that mediate the effect of miR-337-3p on paclitaxel sensitivity. Further investigation showed that miR-337-3p mimic also sensitizes cells to docetaxel, another member of the taxane family, and that STAT3 levels are significantly correlated with taxane resistance in lung cancer cell lines, suggesting that endogenous STAT3 expression is a determinant of intrinsic taxane resistance in lung cancer. The identification of a miR-337-3p as a modulator of cellular response to taxanes, and STAT3 and RAP1A as regulatory targets which mediate that response, defines a novel regulatory pathway modulating paclitaxel sensitivity in lung cancer cells, which may provide novel adjuvant strategies along with paclitaxel in the treatment of lung cancer and may also provide biomarkers for predicting paclitaxel response in NSCLC