Microarray Data Analysis

A large variation in prognosis is observed despite the use of clinical prognostic factors in patients with advanced non-small cell lung cancer (NSCLC). It is likely that this variation is due to the different biological properties of the tumour cells. In this work we aimed to identify gene signature that could predict survival in advanced NSCLC. Total RNA was extracted from five 5 μm-thick sections of the FFPE using the High Pure RNA Paraffin Kit (Roche). RNA amplification was performed using WT-Ovation™ FFPE RNA Amplification System V2 (NuGen). The amplified cDNA was then labelled and hybridised onto Illumina HumanRef-8 v3.0 Expression BeadChips. Microarray data analysis was subsequently performed using Genespring GX version 9.0. Out of 75 FFPE samples, only 32 had sufficient RNA quality and quantity for microarray gene expression analysis. Patients were grouped into long and short survival groups based on the time to cancer-related death. After normalization and filtration, 19,002 genes were selected for differential gene expression analysis. A total of 440 genes differed significantly between the long and short survival groups (ANOVA, p <; 0.05, with Benjamini and Hochberg False Discovery Rate multiple testing correction). Unsupervised Hierarchial Clustering with Pearson correlation and average linkage identified two broad clusters of patients corresponding to the long and short survival. Thirteen genes were selected based on the TTest, 2-fold expression changes, principal components analysis and univariate Cox regression analysis and risk scores were calculated for each patient. These gene signatures were independent predictors of survival. The model was validated with a published microarray data from 130 patients with NSCLC. Using Gene Set Analysis (GSA), we found certain biological processes including metastasis and chemotherapy resistance were up-regulated in the short survival group while TID pathway and MAPKKK cascade were enriched in th- - e long survival group. As the conclusion, there is several distinct gene expression profiles associated with survival of patients with advanced stage NSCLC. Survival outcomes in advanced NSCLC could be predicted based on a 13-gene signature.

Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.

This paper focuses on feature selection for problems dealing with high-dimensional data. We discuss the benefits of adopting a regularized approach with L 1 or L 1–L 2 penalties in two different applications—microarray data analysis in computational biology and object detection in computer vision. We describe general algorithmic aspects as well as architecture issues specific to the two domains. The very promising results obtained show how the proposed approach can be useful in quite different fields of application.

The breakdown of the epidermal barrier and consequent loss of skin hydration is a feature of skin aging and eczematous dermatitis. Few treatments, however, resolve these underlying processes to provide full symptomatic relief. In this study, we evaluated isosorbide di-(linoleate/oleate) (IDL), which was generated by esterifying isosorbide with sunflower fatty acids. Topical effects of IDL in skin were compared with those of ethyl linoleate/oleate, which has previously been shown to improve skin barrier function. Both IDL and ethyl linoleate/oleate downregulated inflammatory gene expression, but IDL more effectively upregulated the expression of genes associated with keratinocyte differentiation (e.g., KRT1, GRHL2, SPRR4). Consistent with this, IDL increased the abundance of epidermal barrier proteins (FLG and involucrin) and prevented cytokinemediated stratum corneum degradation. IDL also downregulated the expression of unhealthy skin signature genes linked to the loss of epidermal homeostasis and uniquely repressed an IFN-inducible coexpression module activated in multiple skin diseases, including psoriasis. In a double-blind, placebo-controlled trial enrolling females with dry skin, 2% IDL lotion applied over 2 weeks significantly improved skin hydration and decreased transepidermal water loss (NCT04253704). These results demonstrate mechanisms by which IDL improves skin hydration and epidermal barrier function, supporting IDL as an effective intervention for the treatment of xerotic pruritic skin.

Rainfall is very important aspect for farmers in day-to-day agriculture and also very important in prospect to Indian economical growth. As now a day we see there is daily climate change in our India as there no stable season any month the rainfall occurs in India which also cause damage to the farming industry as the crop seeds gets damaged. In the proposed study the future rainfall is predicted using Linear Regression Technique as we also compared various model such as SVM, Random Forest Regression, Neural Network model so we obtained better results in Linear Regression so we further consider this model for future prediction of rainfall.

Sequencing projects arising from high-throughput technologies including those of sequencing DNA microarray allowed measuring simultaneously the expression levels of millions of genes of a biological sample as well as to annotate and to identify the role (function) of those genes. Consequently, to better manage and organize this significant amount of information, bioinformatics approaches have been developed. These approaches provide a representation and a more 'relevant' integration of data in order to test and validate the researchers' hypothesis. In this context, this article describes and discusses some techniques used for the functional analysis of gene expression data.

The classification of different types of tumor is of great importance in cancer diagnosis and drug discovery.
Earlier studies on cancer classification have limited diagnostic ability. The recent development of DNA
microarray technology has made monitoring of thousands of gene expression simultaneously. By using this
abundance of gene expression data researchers are exploring the possibilities of cancer classification.
There are number of methods proposed with good results, but lot of issues still need to be addressed. This
paper present an overview of various cancer classification methods and evaluate these proposed methods
based on their classification accuracy, computational time and ability to reveal gene information. We have
also evaluated and introduced various proposed gene selection method. In this paper, several issues
related to cancer classification have also been discussed.

Background: Terminalia chebula (TC) is a deciduous tree of which extracts have demonstrated efficacy for treatment of photodamage, skin aging, and wound healing. However, molecular and cellular mechanisms underlying these benefits remain unclear. Objective: To profile dermal expression responses to a standardized tannin-enriched TC fruit extract (Synastol® TC).

Materials and methods: Microarrays were used to evaluate gene expression in three-dimensional reconstituted human skin cultures.

Results: Genome-wide expression responses to TC were the opposite to those observed in cells exposed to oxidative stress, solar-simulated UV radiation, and wounding, with increased expression of genes associated with water homeostasis, skin barrier establishment, blood vessel development, and circadian rhythms. TC also increased expression of extracellular matrix components, such as collagens (COL1A1, COL1A2) and proteoglycans (PRELP, OGN), and in separate assays, we showed that TC inhibits MMP enzymes (MMP-1, MMP-2, MMP-3, MMP-9, MMP-12) and microbial activity (S. aureus, P. acnes). Unexpectedly, mRNA and protein levels of late keratinocyte (KC) differentiation markers (FLG, LOR) were increased by TC, and expression responses in skin cultures broadly resembled those that occur with KC differentiation. Consistent with these results, TC increased expression of transcription factors regulating KC differentiation (FOS, GHRL3, PPARG) and we noted enrichment of AP-1 binding sites in regions upstream of TC-increased genes.

Conclusion: These results demonstrate that functionally important TC extract responses occur in the epidermis and are therefore not restricted to the dermal layer. Our findings thus suggest mechanisms by which TC may strengthen full-thickness skin architecture for treatment of skin aging and/or chronic wounds.

The key to the keys to immortality and eternal youth lies in the correct answer to the main question: How to naively discover new essential – but still hidden – features required for properly training novel adaptive supervised machine learning algorithms (SMLA)

Hepatocellular carcinoma (HCC) is a major malignancy in the liver and has emerged as one of the main cancers in the world with a high mortality rate. However, the molecular mechanisms of HCC are still poorly understood. Long noncoding RNAs (lncRNAs) have recently come to the forefront as functional non–protein-coding RNAs that are involved in a variety of cellular processes ranging from maintaining the structural integrity of chromosomes to gene expression regulation in a spatiotemporal manner. Many recent studies have reported the involvement of lncRNAs in HCC which has led to a better understanding of the underlying molecular mechanisms operating in HCC. Long noncoding RNAs have been shown to regulate development and progression of HCC, and thus, lncRNAs have both diagnostic and therapeutic potentials. In this review, we present an overview of the lncRNAs involved in different stages of HCC and their potential in clinical applications which have been studied so far.

Microarray technology has become an emerging trend in the domain of genetic research in which many researchers employ to study and investigate the levels of genes&#39; expression in a given organism. Microarray experiments have lots of application areas in the health sector such as diseases prediction and diagnosis, cancer study and soon. The enormous quantity of raw gene expression data usually results in analytical and computational complexities which include feature selection and classification of the datasets into the correct class or group. To achieve satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task. Therefore, this paper presents a comprehensive survey of studies on microarray cancer classification with a focus on feature selection methods. In this paper, the tax...

Galanin (GAL) and GAL receptors (GALR) are overexpressed in limbic brain regions associated
with cognition in Alzheimer disease (AD). The functional consequences of GAL plasticity in
AD are unclear. Because GAL inhibits cholinergic transmission and restricts long-term
potentiation in the hippocampus, GAL overexpression may exacerbate clinical features of AD. In
contrast, GAL expression increases in response to neuronal injury and GAL-mediated
hyperinnervation prevents the decreased expression of phosphatase <<which one?>> mRNA in CBF
neurons in AD. Thus, GAL may also be neuroprotective for AD. Further elucidation of GAL activity
at native GALRs in selectively vulnerable brain regions will help gauge the therapeutic potential
of GALR ligands for the treatment of AD.

Zingerone (Z) is a phenolic alkanone derived from natural sources with anti-inflammatory and antioxidant effects. Acetyl zingerone (AZ) is a recently designed molecule that shares structural features with Z but is expected to have improved stability and antioxidant function. This study utilized microarrays to compare the effects of Z and AZ on gene expression in reconstituted human epidermis. Both Z and AZ increased Notch pathway gene expression (NOTCH1 and MAML3) and decreased expression of genes linked to extracellular matrix disassembly (MMP3 and CTSV) and reactive oxygen species metabolism (PMAIP1 and ARG2). Although Z and AZ each inhibited in vitro matrix metallopeptidase (MMP)-1, MMP-3, and MMP-12 activity, inhibition of MMP-3 and MMP-12 was greater with AZ. Moreover, AZ led to more consistent increases in the expression of genes encoding collagens (COL11A2), proteoglycans (VCAN), and extracellular matrix glycoproteins (SPARC). Finally, AZ opposed gene expression patterns associated with fibroblast senescence, keratinocyte differentiation , and IL-17A stimulation. These effects were AZ-specific and not replicated by Z. These results show that AZ improves extracellular matrix integrity with retinoid-like effects on differentiation and inflammation. Our findings provide a rationale for clinical studies to understand the benefits of AZ in the treatment or prevention of skin aging, or potentially, as a treatment for other human skin diseases.

The term big-data in the context of materials science not only stands for the volume, but also for the heterogeneous nature of the characterization data-sets. This is a common problem in combinatorial searches in materials science, as well as chemistry. However, these data-sets may well be 'small' in terms of limited step-size of the measurement variables. Due to this limitation, application of higher-order statistics is not effective, and the choice of a suitable unsupervised learning method is restricted to those utilizing lower-order statistics. As an interesting case study, we present here variable magnetic-field Piezoresponse Force Microscopy (PFM) study of composite multiferroics, where due to experimental limitations the magnetic field dependence of piezoresponse is registered with a coarse step-size. An efficient extraction of this dependence, which corresponds to the local magnetoelectric effect, forms the central problem of this work. We evaluate the performance of Principal Component Analysis (PCA) as a simple unsupervised learning technique, by pre-labeling possible patterns in the data using Density Based Clustering (DBSCAN). Based on this combinational analysis, we highlight how PCA using non-central second-moment can be useful in such cases for extracting information about the local material response and the corresponding spatial distribution.

We studied genome-wide gene expression from bald and haired scalp of individuals to evaluate pathogenic mechanisms underlying the development and progression of androgenetic alopecia (AGA). Unbiased analyses revealed a “bald pathology” based signature. Ontology enrichment analyses of the differentially expressed genes (DEGs) underscored apoptosis, cell proliferation, perturbed neurological pathways, and WNT signaling as central drivers of the hair loss process. Interactome analysis uncovered several known and novel key transcriptional regulators potentially affecting disease pathogenesis both within and “hidden” from the dataset. One DEG mapped within one of the fourteen identified transcriptionally active “hot spots” across the genome and coincided with a previous AGA-associated gene. The remaining DEGs within the “hot spots” offer an additional set of potential disease linked loci that may help to guide future studies aimed at identifying disease risk genes. Finally, we used in silico analyses to identify five molecular targets for exploration in future AGA therapies.

Determining risky software projects early is a very important factor for project success. In this study it is aimed to choose the most correctly resulting modelling method that will be useful for early prediction of risky software projects to help companies to avoid losing time and money on unsuccessful projects and also facing legal requirements because of not being able to fullfill their responsibilites to their customers While making the research for this subject, it is seen that in previous researches, usually traditional modelling techniques were preferred. But it is observed that these methods were mostly resulted with high misclassification ratio. To overcome this problem, this study proposes a three-layered neural network (NN) architecture with a backpropagation algorithm. NN architecture was trained by using two different data sets which were OMRON data set (collected by OMRON) and 2016-2020 ES.LV data set (collected by the authors) separately. For the made of this study firstly the most relevant classification method (Gaussian Naive Bayes Algorithm) and the most relevant neural network method (Scaled Conjugate Gradient Backpropagation Algorithm) was chosen and both data sets were trained by using each method seperately for the purpose of observing which type of modelling architecture would give better results. Experimental results of this study showed that the neural network approach is useful for predicting whether a project is risky or not risky.

Gene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptional networks. Experiments that apply this technology typically generate overwhelming volumes of data, unprecedented in biological research. Therefore the task of mining meaningful biological knowledge out of the raw data is a major challenge in bioinformatics. Of special need are integrative packages that provide biologist users with advanced but yet easy to use, set of algorithms, together covering the whole range of steps in microarray data analysis. Here we present the EXPANDER 2.0 (EXPression ANalyzer and DisplayER) software package. EXPANDER 2.0 is an integrative package for the analysis of gene expression data, designed as a &#39;one-stop shop&#39; tool that implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering,...

Genes contain blue print of living organism. Malfunctioning occurred in cellular life is indicated by proteins which are responsible for behavior of genes. Fixed set of genes decides behavior and functioning of cells. They guide the cells what to do and when to do. To analyze the insight of biological activities, analysis of gene expressions is necessary. Advanced technology like microarray plays an important role in gene analysis as it captures expressions of thousands of genes under different conditions simultaneously. Out of thousands of genes, very few behave differently which are called as Differentially Expressing Genes (DEGs). Identifying these most significant genes is a crucial task in molecular biology and is a major area of research for bioinformaticians because DEGs are the major source of disease prediction. They help in planning therapeutic strategies for a disease through Gene Regulatory Network (GRN) constructed from them. GRN is a graphical representation containing genes as nodes and regulatory interactions among them as edges. GRN helps to know change over occurred among genes which involved in cause of genetic diseases as well as to analyze their response to different stress conditions through microarray expressions. In this paper we have discussed many methods proposed by researchers for identifying differentially expressing genes based upon changes in their expressions patterns.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes

A multiplex DNA microarray chip was developed for simultaneous identification of nine genetically modified organisms (GMOs), five plant species and three GMO screening elements, i.e. the 35S promoter, the nos terminator and the nptII gene. The chips also include several controls, such as that for the possible presence of CaMV. The on-chip detection was performed directly with PCR amplified products. Particular emphasis was placed on the reduction of the number of PCR reactions required and on the number of primers present per amplification tube. The targets were biotin labelled and the arrays were detected using a colorimetric methodology. Specificity was provided by specific capture probes designed for each GMO and for the common screening elements. The sensitivity of the assay was tested by experiments carried out in five different laboratories. The limit of detection was lower than 0.3% GMO for all tests and in general around 0.1% for most GMOs. The chip detection system complies with the requirements of current EU regulations and other countries where thresholds are established for the labelling of GMO.