Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Cancer is a severe condition of uncontrolled cell division that results in a tumor formation that spreads to other tissues of the body. Therefore, the development of new medication and treatment methods for this is in demand. Classification of microarray data plays a vital role in handling such situations. The relevant gene selection is an important step for the classification of microarray data. This work presents gene encoder, an unsupervised two-stage feature selection technique for the cancer samples’ classification. The first stage aggregates three filter methods, namely principal component analysis, correlation, and spectral-based feature selection techniques. Next, the genetic algorithm is used, which evaluates the chromosome utilizing the autoencoder-based clustering. The resultant feature subset is used for the classification task. Three classifiers, namely support vector machine, k-nearest neighbors, and random forest, are used in this work to avoid the dependency on any one classifier. Six benchmark gene expression datasets are used for the performance evaluation, and a comparison is made with four state-of-the-art related algorithms. Three sets of experiments are carried out to evaluate the proposed method. These experiments are for the evaluation of the selected features based on sample-based clustering, adjusting optimal parameters, and for selecting better performing classifier. The comparison is based on accuracy, recall, false positive rate, precision, F-measure, and entropy. The obtained results suggest better performance of the current proposal

A unified data representation theory for network visualization, ordering and coarse-graining

Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form.Comment: 13 pages, 5 figure

Ellagic Acid Derivatives from Rubus ulmifolius Inhibit Staphylococcus aureus Biofilm Formation and Improve Response to Antibiotics

Biofilms contribute to the pathogenesis of many forms of Staphylococcus aureus infection. Treatment of these infections is complicated by intrinsic resistance to conventional antibiotics, thus creating an urgent need for strategies that can be used for the prevention and treatment of biofilm-associated infections.This study demonstrates that a botanical natural product composition (220D-F2) rich in ellagic acid and its derivatives can limit S. aureus biofilm formation to a degree that can be correlated with increased antibiotic susceptibility. The source of this composition is Rubus ulmifolius Schott. (Rosaceae), a plant used in complementary and alternative medicine in southern Italy for the treatment of skin and soft tissue infections. All S. aureus clonal lineages tested exhibited a reduced capacity to form a biofilm at 220D-F2 concentrations ranging from 50-200 µg/mL, which were well below the concentrations required to limit bacterial growth (530-1040 µg/mL). This limitation was therapeutically relevant in that inclusion of 220D-F2 resulted in enhanced susceptibility to the functionally-distinct antibiotics daptomycin, clindamycin and oxacillin. Testing with kidney and liver cell lines also demonstrated a lack of host cell cytotoxicity at concentrations of 220D-F2 required to achieve these effects.These results demonstrate that extract 220D-F2 from the root of Rubus ulmifolius can be used to inhibit S. aureus biofilm formation to a degree that can be correlated with increased antibiotic susceptibility without toxic effects on normal mammalian cells. Hence, 220D-F2 is a strong candidate for development as a botanical drug for use in the prevention and treatment of S. aureus biofilm-associated infections

Modeling Planarian Regeneration: A Primer for Reverse-Engineering the Worm

A mechanistic understanding of robust self-assembly and repair capabilities of complex systems would have enormous implications for basic evolutionary developmental biology as well as for transformative applications in regenerative biomedicine and the engineering of highly fault-tolerant cybernetic systems. Molecular biologists are working to identify the pathways underlying the remarkable regenerative abilities of model species that perfectly regenerate limbs, brains, and other complex body parts. However, a profound disconnect remains between the deluge of high-resolution genetic and protein data on pathways required for regeneration, and the desired spatial, algorithmic models that show how self-monitoring and growth control arise from the synthesis of cellular activities. This barrier to progress in the understanding of morphogenetic controls may be breached by powerful techniques from the computational sciences—using non-traditional modeling approaches to reverse-engineer systems such as planaria: flatworms with a complex bodyplan and nervous system that are able to regenerate any body part after traumatic injury. Currently, the involvement of experts from outside of molecular genetics is hampered by the specialist literature of molecular developmental biology: impactful collaborations across such different fields require that review literature be available that presents the key functional capabilities of important biological model systems while abstracting away from the often irrelevant and confusing details of specific genes and proteins. To facilitate modeling efforts by computer scientists, physicists, engineers, and mathematicians, we present a different kind of review of planarian regeneration. Focusing on the main patterning properties of this system, we review what is known about the signal exchanges that occur during regenerative repair in planaria and the cellular mechanisms that are thought to underlie them. By establishing an engineering-like style for reviews of the molecular developmental biology of biomedically important model systems, significant fresh insights and quantitative computational models will be developed by new collaborations between biology and the information sciences

Molecular biology of the blood-brain and the blood-cerebrospinal fluid barriers: similarities and differences

Efficient processing of information by the central nervous system (CNS) represents an important evolutionary advantage. Thus, homeostatic mechanisms have developed that provide appropriate circumstances for neuronal signaling, including a highly controlled and stable microenvironment. To provide such a milieu for neurons, extracellular fluids of the CNS are separated from the changeable environment of blood at three major interfaces: at the brain capillaries by the blood-brain barrier (BBB), which is localized at the level of the endothelial cells and separates brain interstitial fluid (ISF) from blood; at the epithelial layer of four choroid plexuses, the blood-cerebrospinal fluid (CSF) barrier (BCSFB), which separates CSF from the CP ISF, and at the arachnoid barrier. The two barriers that represent the largest interface between blood and brain extracellular fluids, the BBB and the BCSFB, prevent the free paracellular diffusion of polar molecules by complex morphological features, including tight junctions (TJs) that interconnect the endothelial and epithelial cells, respectively. The first part of this review focuses on the molecular biology of TJs and adherens junctions in the brain capillary endothelial cells and in the CP epithelial cells. However, normal function of the CNS depends on a constant supply of essential molecules, like glucose and amino acids from the blood, exchange of electrolytes between brain extracellular fluids and blood, as well as on efficient removal of metabolic waste products and excess neurotransmitters from the brain ISF. Therefore, a number of specific transport proteins are expressed in brain capillary endothelial cells and CP epithelial cells that provide transport of nutrients and ions into the CNS and removal of waste products and ions from the CSF. The second part of this review concentrates on the molecular biology of various solute carrier (SLC) transport proteins at those two barriers and underlines differences in their expression between the two barriers. Also, many blood-borne molecules and xenobiotics can diffuse into brain ISF and then into neuronal membranes due to their physicochemical properties. Entry of these compounds could be detrimental for neural transmission and signalling. Thus, BBB and BCSFB express transport proteins that actively restrict entry of lipophilic and amphipathic substances from blood and/or remove those molecules from the brain extracellular fluids. The third part of this review concentrates on the molecular biology of ATP-binding cassette (ABC)-transporters and those SLC transporters that are involved in efflux transport of xenobiotics, their expression at the BBB and BCSFB and differences in expression in the two major blood-brain interfaces. In addition, transport and diffusion of ions by the BBB and CP epithelium are involved in the formation of fluid, the ISF and CSF, respectively, so the last part of this review discusses molecular biology of ion transporters/exchangers and ion channels in the brain endothelial and CP epithelial cells