Hassen Dhrif, PhD

Axonal injury results in regenerative success or failure, depending on whether the axon lies in the peripheral or the CNS, respectively. The present study addresses whether epigenetic signatures in dorsal root ganglia discriminate between regenerative and non-regenerative axonal injury. Chromatin immunoprecipitation for the histone 3 (H3) post-translational modifications H3K9ac, H3K27ac and H3K27me3; an assay for transposase-accessible chromatin; and RNA sequencing were performed in dorsal root… Show more

Axonal injury results in regenerative success or failure, depending on whether the axon lies in the peripheral or the CNS, respectively. The present study addresses whether epigenetic signatures in dorsal root ganglia discriminate between regenerative and non-regenerative axonal injury. Chromatin immunoprecipitation for the histone 3 (H3) post-translational modifications H3K9ac, H3K27ac and H3K27me3; an assay for transposase-accessible chromatin; and RNA sequencing were performed in dorsal root ganglia after sciatic nerve or dorsal column axotomy. Distinct histone acetylation and chromatin accessibility signatures correlated with gene expression after peripheral, but not central, axonal injury. DNA-footprinting analyses revealed new transcriptional regulators associated with regenerative ability. Machine-learning algorithms inferred the direction of most of the gene expression changes. Neuronal conditional deletion of the chromatin remodeler CCCTC-binding factor impaired nerve regeneration, implicating chromatin organization in the regenerative competence. Altogether, the present study offers the first epigenomic map providing insight into the transcriptional response to injury and the differential regenerative ability of sensory neurons. Show less

Feature Subset Selection (FSS) is a non-convex, non-linear, multi-modal, high-dimensional, Multi-Objective Optimization problem, where finding the optimum solution is a fairly difficult task due to its ultra-large search space and its very large number of local minima. Existing FSS methods suffer from problems like stagnation in local optima and high computational cost. Evolutionary Computation (EC) techniques are well known global search algorithms. Particle Swarm Optimization (PSO) is an EC… Show more

Feature Subset Selection (FSS) is a non-convex, non-linear, multi-modal, high-dimensional, Multi-Objective Optimization problem, where finding the optimum solution is a fairly difficult task due to its ultra-large search space and its very large number of local minima. Existing FSS methods suffer from problems like stagnation in local optima and high computational cost. Evolutionary Computation (EC) techniques are well known global search algorithms. Particle Swarm Optimization (PSO) is an EC technique that is simple to implement and that converges faster than other methods such as Genetic Algorithms (GA), Differential Evolution (DE) or Ant Colony Optimization (ACO). PSO has been successfully applied to areas such as engineering, biology, image processing, job scheduling, robotics and neural networks, but its potential for FSS has not been fully investigated. The overall goal of this thesis is to investigate and improve the capability of PSO for FSS aiming to select the smallest, most compact subset of features with the best classification performance in the least amount of time. This thesis investigates the use of PSO for wrapper, filter, embedded and hybrid approaches and it implements PSO on both single and multi-objective FSS. FSS problems are characterized by an increasing number of features and instances, massively challenging classical methods. Representing efficient global search methods, Swarm Intelligence (SI) based Evolutionary Computation (EC) techniques have increasingly been applied to solve such FSS problems. Despite their popularity, PSO and other swarm-based algorithms still suffer from a variety of problems and short comings as a consequence of high-dimensional data. We start this dissertation with a comprehensive survey, we formulate the challenges of SI algorithms that tackle FSS problems, discuss algorithmic solutions and present an overview of metrics that allow the systematic and comparable assessment of algorithms performance. ... Show less

The determination of a small set of biomarkers to make a diagnostic call can be formulated as a feature subset selection (FSS) problem to find a small set of genes with high relevance for the underlying classification task and low mutual redundancy. However, repeated application of a heuristic, evolutionary FSS technique usually fails to produce consistent results. Here, we introduce COMB-PSO-LS, a novel hybrid (wrapper-filter) FSS algorithm based on Particle Swarm Optimization (PSO) that… Show more

The determination of a small set of biomarkers to make a diagnostic call can be formulated as a feature subset selection (FSS) problem to find a small set of genes with high relevance for the underlying classification task and low mutual redundancy. However, repeated application of a heuristic, evolutionary FSS technique usually fails to produce consistent results. Here, we introduce COMB-PSO-LS, a novel hybrid (wrapper-filter) FSS algorithm based on Particle Swarm Optimization (PSO) that features a local search strategy to select the least dependent and most relevant feature subsets. In particular, we employ a Randomized Dependence Coefficient (RDC)-based filter technique to guide the search process of the particle swarm, allowing the selection of highly relevant and consistent features. Classifying cancer samples through patient gene expression profiles, we found that COMB-PSO-LS provides highly stable and non-redundant gene subsets that are relevant for the classification process, outperforming standard PSO methods. Show less

Evolutionary computation (EC) algorithms, such as discrete and multi-objective versions of particle swarm optimization (PSO), have been applied to solve the Feature selection (FS) problem, tackling the combinatorial explosion of search spaces that are peppered with local minima. Furthermore, high-dimensional FS problems such as finding a small set of biomarkers to make a diagnostic call add an additional challenge as such methods ability to pick out the most important features must remain… Show more

Evolutionary computation (EC) algorithms, such as discrete and multi-objective versions of particle swarm optimization (PSO), have been applied to solve the Feature selection (FS) problem, tackling the combinatorial explosion of search spaces that are peppered with local minima. Furthermore, high-dimensional FS problems such as finding a small set of biomarkers to make a diagnostic call add an additional challenge as such methods ability to pick out the most important features must remain unchanged in decision spaces of increasing dimensions and presence of irrelevant features. We developed a combinatorial PSO algorithm, called COMB-PSO, that scales up to high-dimensional gene expression data while still selecting the smallest subsets of genes that allow reliable classification of samples. In particular, COMB-PSO enhances the encoding, speed of convergence, control of divergence and diversity of the conventional PSO algorithm, balancing exploration and exploitation of the search space. Applying our approach on real gene expression data of different cancers, COMB-PSO finds gene sets of smallest size that allow a reliable classification of the underlying disease classes. Show less

This work proposes estimating sky radiance distribution curves between 350-2500nm from images captured with a hemispherical digital camera. A novel hardware system simultaneously captured spectral, spatial, and temporal information to acquire accurate physical measurements of the solar/skydome radiance variation. To achieve this goal, we use a custom-built spectral radiance measurement scanner to measure the directional spectral radiance, a pyranometer to measure the irradiance of the entire… Show more

This work proposes estimating sky radiance distribution curves between 350-2500nm from images captured with a hemispherical digital camera. A novel hardware system simultaneously captured spectral, spatial, and temporal information to acquire accurate physical measurements of the solar/skydome radiance variation. To achieve this goal, we use a custom-built spectral radiance measurement scanner to measure the directional spectral radiance, a pyranometer to measure the irradiance of the entire hemisphere, and a commercial digital camera to capture high-dynamic range (HDR) hemispherical imagery of the sky. We use the measurements obtained from a commercially available digital camera and correlating spectroradiometer measurements to train machine learning (ML) models to estimate whole sky full-spectrum radiance distributions (VIS, VNIR, and SWIR) from a low dimensional RGB input. We train clear, cloudy, and mixed sky models, and cross-validate the estimated radiance distributions with ground-truth data. We highlight important measured and engineered ML features, and we present useful feature engineering techniques employed to minimize model estimation error. Additional contributions of this work include the code for all ML models and experiments, a dataset of all-sky HDR captures with correlating spectroradiometer measurements captured 453 times over 16 days, and an open-source, crossplatform, interactive viewer used to visualize photometric and radiometric data side by side. Show less