• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2365-2370
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• 2010

The method of cancer classification based on microarray could contribute to being accurate cancer classification by finding differently expressing gene pattern statistically according to a cancer type. Therefore, the process to select a closely related informative gene with a particular cancer classification to classify cancer using present microarray technology with effect is essential. In this paper, the system can detect marker genes to likely express the most differentially explaining the effects of cancer using ovarian cancer microarray data. And it compare and analyze a performance of classification of the proposed system with it of established microarray system using multi-perceptron neural network layer. Microarray data set including marker gene that are selected using ANOVA method represent the highest classification accuracy of 98.61%, which show that it improve classification performance than established microarray system.

• Molecules and Cells
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• v.41 no.10
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• pp.881-888
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• 2018

During the cortical development, cells in the brain acquire somatic mutations that can be implicated in various neurodevelopmental disorders. There is increasing evidence that brain somatic mutations lead to sporadic form of epileptic disorders with previously unknown etiology. In particular, malformation of cortical developments (MCD), ganglioglioma (GG) associated with intractable epilepsy and non-lesional focal epilepsy (NLFE) are known to be attributable to brain somatic mutations in mTOR pathway genes and others. In order to identify such somatic mutations presenting as low-level in epileptic brain tissues, the mutated cells should be enriched and sequenced with high-depth coverage. Nevertheless, there are a lot of technical limitations to accurately detect low-level of somatic mutations. Also, it is important to validate whether identified somatic mutations are truly causative for epileptic seizures or not. Furthermore, it will be necessary to understand the molecular mechanism of how brain somatic mutations disturb neuronal circuitry since epilepsy is a typical example of neural network disorder. In this review, we overview current genetic techniques and experimental tools in neuroscience that can address the existence and significance of brain somatic mutations in epileptic disorders as well as their effect on neuronal circuitry.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.25 no.2
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• pp.45-57
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• 1999

Melanocytes, derived from neural crest, make melanin and protect skin from the hazardous ultraviolet light. Melanocytes with dendritic process has similar morphology with neurogenic cells and share growth factor receptors such as neurotrophin receptors. Melanogenesis can be regulated by ultraviolet light and inflammation of the skin. In addition, several factors such as hormone, cytokines, arachidonic acid can affect the proliferation and melanogenesis of melanocytes. For melanogenesis, melanocytes need expression of various genes including tyrosinase, TRP-1, TRP-2. In addition, melanin need to be transferred from melanocytes to surrounding keratinocytes. The biology of melanocytes is complex and mechanism of melanocytes proliferation and melanogenesis is still under the investigation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• Journal of Plant Biotechnology
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• v.33 no.3
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• pp.195-207
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• 2006

Stem cells are the primordial, initial cells which usually divide asymmetrically giving rise to on the one hand self-renewals and on the other hand progenitor cells with potential for differentiation. Zygote (fertilized egg), with totipotency, deserves the top-ranking stem cell - he totipotent stem cell (TSC). Both the ICM (inner cell mass) taken from the 6 days-old human blastocyst and ESC (embryonic stem cell) derived from the in vitro cultured ICM have slightly less potency for differentiation than the zygote, and are termed pluripotent stem cells. Stem cells in the tissues and organs of fetus, infant, and adult have highly reduced potency and committed to produce only progenitor cells for particular tissues. These tissue-specific stem cells are called multipotent stem cells. These tissue-specific/committed multipotent stem cells, when placed in altered environment other than their original niche, can yield cells characteristic of the altered environment. These findings are certainly of potential interest from the clinical, therapeutic perspective. The controversial terminology 'somatic stem cell plasticity' coined by the stem cell community seems to have been proved true. Followings are some of the recent knowledges related to the stem cell. Just as the tissues of our body have their own multipotent stem cells, cancerous tumor has undifferentiated cells known as cancer stem cell (CSC). Each time CSC cleaves, it makes two daughter cells with different fate. One is endowed with immortality, the remarkable ability to divide indefinitely, while the other progeny cell divides occasionally but lives forever. In the cancer tumor, CSC is minority being as few as 3-5% of the tumor mass but it is the culprit behind the tumor-malignancy, metastasis, and recurrence of cancer. CSC is like a master print. As long as the original exists, copies can be made and the disease can persist. If the CSC is destroyed, cancer tumor can't grow. In the decades-long cancer therapy, efforts were focused on the reducing of the bulk of cancerous growth. How cancer therapy is changing to destroy the origin of tumor, the CSC. The next generation of treatments should be to recognize and target the root cause of cancerous growth, the CSC, rather than the reducing of the bulk of tumor, Now the strategy is to find a way to identify and isolate the stem cells. The surfaces of normal as well as the cancer stem cells are studded with proteins. In leukaemia stem cell, for example, protein CD 34 is identified. In the new treatment of cancer disease it is needed to look for protein unique to the CSC. Blocking the stem cell's source of nutrients might be another effective strategy. The mystery of sternness of stem cells has begun to be deciphered. ESC can replicate indefinitely and yet retains the potential to turn into any kind of differentiated cells. Polycomb group protein such as Suz 12 repress most of the regulatory genes which, activated, are turned to be developmental genes. These protein molecules keep the ESC in an undifferentiated state. Many of the regulator genes silenced by polycomb proteins are also occupied by such ESC transcription factors as Oct 4, Sox 2, and Nanog. Both polycomb and transcription factor proteins seem to cooperate to keep the ESC in an undifferentiated state, pluripotent, and self-renewable. A normal prion protein (PrP) is found throughout the body from blood to the brain. Prion diseases such as mad cow disease (bovine spongiform encephalopathy) are caused when a normal prion protein misfolds to give rise to PrP$^{SC}$ and assault brain tissue. Why has human body kept such a deadly and enigmatic protein? Although our body has preserved the prion protein, prion diseases are of rare occurrence. Deadly prion diseases have been intensively studied, but normal prion problems are not. Very few facts on the benefit of prion proteins have been known so far. It was found that PrP was hugely expressed on the stem cell surface of bone marrow and on the cells of neural progenitor, PrP seems to have some function in cell maturation and facilitate the division of stem cells and their self-renewal. PrP also might help guide the decision of neural progenitor cell to become a neuron.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.397-402
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• 2019

Accurately predicting cancer prognosis to provide appropriate treatment strategies for patients is one of the critical challenges in bioinformatics. Many researches have suggested machine learning models to predict patients' outcomes based on their gene expression data. Gene expression data is high-dimensional numerical data containing about 17,000 genes, so traditional researches used feature selection or dimensionality reduction approaches to elevate the performance of prognostic prediction models. These approaches, however, have an issue of making it difficult for the predictive models to grasp any biological interaction between the selected genes because feature selection and model training stages are performed independently. In this paper, we propose a novel two-dimensional image formatting approach for gene expression data to achieve feature selection and prognostic prediction effectively. Node2Vec is exploited to integrate biological interaction network and gene expression data and a convolutional neural network learns the integrated two-dimensional gene expression image data and predicts cancer prognosis. We evaluated our proposed model through double cross-validation and confirmed superior prognostic prediction accuracy to traditional machine learning models based on raw gene expression data. As our proposed approach is able to improve prediction models without loss of information caused by feature selection steps, we expect this will contribute to development of personalized medicine.

• Journal of Life Science
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• v.31 no.12
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• pp.1120-1127
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• 2021

Depression has a negative impact on social functioning due to its high prevalence and increased suicide rate, and is a disease with a high economic burden. Depression is related to diverse brain-related phenomena, such as neuroinflammation, synaptic dysfunction, and cognitive deficit. As antidepressant drugs used in clinical trials have shown poor therapeutic effects, antidepressant drugs that show rapid efficacy urgently need to be developed. Although studies on various genes, proteins, and signaling pathways related to depression have been conducted, the pathogenesis of depression has not been clearly elucidated. Sirtuin 1 is a nicotinamide-adenine dinucleotide- (NAD+-) dependent histone deacetylase and is involved in cell differentiation, apoptosis, autophagy, and cancer metabolism. Recent genetic studies found that sirtuin 1 is a potential target gene for depression. In addition, preclinical studies reported that sirtuin 1 signaling affects depression-like behavior. In this review, we attempt to present up-to-date knowledge of depression and sirtuin 1. We describe the various roles of sirtuin 1 in the regulation of glial activation, circadian rhythm, neurogenesis, and cognitive function and the effects of its expression on depression. Further, we discuss the effect of sirtuin 1 on the impairment of neural plasticity, one of the key mechanisms of depression, and the associated mechanisms of sirtuin 1.

• Journal of Life Science
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• v.32 no.2
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• pp.108-118
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• 2022

The subventricular zone (SVZ) in the brain contains neural stem cells (NSCs) that generate new neurons throughout one's lifetime. Many extracellular and intracellular factors that affect cell proliferation and neuronal differentiation of NSCs are already well-known. Recently, L-type calcium channels have been reported to regulate neural development and are present in NSCs, differentiating neuroblasts, and mature neurons in the SVZ. Nifedipine, a blocker of L-type calcium channels, has been long used as a therapeutic drug for hypertension. However, studies on the use of nifedipine to inhibit L-type calcium channels of NSCs are lacking. Herein, we treated NSCs cultured from mouse postnatal SVZ with nifedipine during neuronal differentiation. Nifedipine increased the number of Tuj1-positive neurons but did not significantly change the number of Olig2-positive oligodendrocytes. Nifedipine increased cell division during early differentiation, which was detected using the 5-ethynyl-2'-deoxyuridine incorporation assay and immunocytochemistry assessment by staining the cells with phosphorylated histone H3, a mitosis marker. Nifedipine increased the transcription of Dlx2, a neurogenic transcription factor, and the level of Mash1, a marker for early neurogenesis. In addition to nifedipine, verapamil, which is also an L-type calcium channel blocker, showed a slight increase in neurogenesis, but its statistical significance was very low. In contrast, pimozide, a T-type calcium channel blocker, did not affect neurogenesis, although T-type calcium channel genes Cav3.1, Cav3.2, and Cav3.3 were expressed. In summary, nifedipine might promote the neuronal fate of NSCs during early differentiation and calcium signaling through L-type calcium channels might be involved in neuronal differentiation, especially during the early stages of differentiation.

• Korean Journal of Agricultural Science
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• v.38 no.1
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• pp.65-70
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• 2011

Development of mouse fetus brains can be defined morphologically and functionally by three developmental stages, embryo day (ED) 16, postnatal stage one week and eight weeks. These defined stages of brain development may be closely associated with differential gene expression rates due to limited cellular resources such as energy, space, and free water. Complex patterns of expressed genes and proteins during brain development suggests the changes in relative concentrations of proteins rather than the increase in numbers of new gene products. This study was designed to evaluate early protein expression pattern in mouse fetus brain. The mouse brain proteome of fetus at ED 15.5, and 19.5 was obtained using 2-dimensional gel electrophoresis (DE). Analysis of the 2-DE gels in pH 3-10 range revealed the presence of 15 differentially expressed spots, of which 11 spots were identified to be known proteins following MALDI-TOF analysis; 3 spots were up-regulated and 8 spots were down-regulated in the mouse fetus brain at ED 15.5. UP-regulated proteins were identified as MCG18238, isoform M2 of pyruvate kinase isozymes M1/M2, isoform 2 of heterogeneous nuclear ribonucleoprotein K, heterogeneous nuclear ribonucleoprotein H2, creatine kinase B-type, 40S ribosomal protein SA and hemoglobin subunit beta-H1. Down-regulated proteins were putative uncharacterized protein, lactoylglutathione lyase and secreted acidic cysteine rich glycoprotein. Our results revealed composite profiles of mouse fetus brain proteins related to mouse fetus development by 2-DE analysis implying possible roles of these proteins in neural differentiation.

• The Journal of Korean Physical Therapy
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• v.13 no.2
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• pp.433-444
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• 2001

This review highlights the ontogenesis and the differentiation of motor neuron in spinal cord, and introduce the survival motor neuron(SMN) which is associated with growth and survival of motor neurons. The differentiation of floor plate cells and motor neurons in the vertebrate neural tube appears to be induced by signals from the notochord. This signal is Sonic hedgehog(Shh). The early development of motor neurons involves the inductive action of Shh. The SMN gene is essential for embryonic viability. SMN mRNA is also expressed in virtually all cell types in spinal cord, including large motor neurons. The SMN protein is involved in RNA processing and during early embryonic development is necessary fer cell survival. Two SMN genes are present in 5q 13 in humans: the telomeric gene(SMNt), which is the SMA-determining gene, and the centromeric analog gene(SMNc). The majority of transcripts from the SMNt gene are full length but, major transcripts of the SMNc gene have a high degrees of alternative splicing and tend to have little or no exon 7. The SMN is involved in the RNA processing(the biogenesis of snRNPs and pre-mRNA splicing), the anti-apoptotic effects, and regulating gene expression.