• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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• pp.72-78
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• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.

• International Journal of Contents
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• v.15 no.1
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• pp.39-51
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• 2019

Spontaneous speech provides rich information defining the linguistic characteristics of individuals. As such, computational analysis of speech would enhance the efficiency involved in evaluating patients' speech. This study aims to provide a method to differentiate the persons with and without aphasia based on language usage. Ten aphasic patients and their counterpart normal controls participated, and they were all tasked to describe a set of given words. Their utterances were linguistically processed and compared to each other. Computational analyses from PCA (Principle Component Analysis) to machine learning were conducted to select the relevant linguistic features, and consequently to classify the two groups based on the features selected. It was found that functional words, not content words, were the main differentiator of the two groups. The most viable discriminators were demonstratives, function words, sentence final endings, and postpositions. The machine learning classification model was found to be quite accurate (90%), and to impressively be stable. This study is noteworthy as it is the first attempt that uses computational analysis to characterize the word usage patterns in Korean aphasic patients, thereby discriminating from the normal group.

• The Plant Pathology Journal
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• v.40 no.2
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• pp.233-233
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• 2024

• The Plant Pathology Journal
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• v.33 no.6
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• pp.529-542
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• 2017

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.5
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• pp.894-899
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• 2011

Traditional Korean Medicine (TKM) has been actively researched through various approaches, including computational methods. This paper aims at providing an overview of domestic studies using the computational techniques in TKM field. A literature search was conducted in Korean publications using OASIS system, and major studies of data mining in TKM were identified. A review was presented in six diagnosis fields, including sasang constitution diagnosis, eight constitution diagnosis, tongue diagnosis, pattern diagnosis for stroke, diagnosis based on ontology, diagnosis for cause of disease. They collect clinical data themselves for experiments and primarily applied a algorithm of decision tree, SVM, neural network, case-based reasoning, ontology reasoning, discriminant analysis. In the future, there needs to identify which algorithm is suitable to diagnosis or other fields of TKM.

• Genomics & Informatics
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• v.20 no.1
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• pp.5.1-5.10
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• 2022

Non-syndromic hearing loss (NSHL) is a common hereditary disorder. Both clinical and genetic heterogeneity has created many obstacles to understanding the causes of NSHL. The present study has attempted to ravel the genetic aetiology in NSHL progression and to screen out potential target genes using computational approaches. The reported NSHL target genes (2009-2020) have been studied by analyzing different biochemical and signaling pathways, interpretation of their functional association network, and discovery of important regulatory interactions with three previously established miRNAs in the human inner ear as well as in NSHL such as miR-183, miR-182, and miR-96. This study has identified SMAD4 and SNAI2 as the most putative target genes of NSHL. But pathogenic and deleterious non-synonymous single nucleotide polymorphisms discovered within SMAD4 is anticipated to have an impact on NSHL progression. Additionally, the identified deleterious variants in the functional domains of SMAD4 added a supportive clue for further study. Thus, the identified deleterious variant i.e., rs377767367 (G491V) in SMAD4 needs further clinical validation. The present outcomes would provide insights into the genetics of NSHL progression.

• Journal of the Optical Society of Korea
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• v.15 no.2
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• pp.111-117
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• 2011

Full-range spectral domain optical coherence tomography (SD-OCT) with a 1-${\mu}m$ band light source is shown here. The phase of the reference beam is continuously stepped while the probing beam scans the sample laterally (B-scan). The two dimensional spectral interferogram obtained is processed by a Fourier transform method to obtain a complex spectrum leading to a full-range OCT image. A detailed mathematical explanation of the complex conjugate resolving method utilized is provided. The system's measurement speed was 7.96 kHz, the measured axial resolution was $9.6{\mu}m$ in air and the maximum sensitivity 99.4 dB. To demonstrate the effect of mirror image elimination, In vivo human eye pathology was measured.

• The Plant Pathology Journal
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• v.40 no.1
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• pp.48-58
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• 2024

The oldest and most extensively cultivated form of millet, known as pearl millet (Pennisetum glaucum (L.) R. Br. Syn. Pennisetum americanum (L.) Leeke), is raised over 312.00 lakh hectares in Asian and African countries. India is regarded as the significant hotspot for pearl millet diversity. In the Indian state of Haryana, where pearl millet is grown, a new and catastrophic bacterial disease known as stem rot of pearl millet spurred by the bacterium Klebsiella aerogenes (formerly Enterobacter) was first observed during fall 2018. The disease appears in form of small to long streaks on leaves, lesions on stem, and slimy rot appearance of stem. The associated bacterium showed close resemblance to Klebsiella aerogenes that was confirmed by a molecular evaluation based on 16S rDNA and gyrA gene nucleotide sequences. The isolates were also identified to be Klebsiella aerogenes based on biochemical assays, where Klebsiella isolates differed in D-trehalose and succinate alkalisation tests. During fall 2021-2023, the disease has spread all the pearl millet-growing districts of the state, extending up to 70% disease incidence in the affected fields. The disease is causing considering grain as well as fodder losses. The proposed scale, consisting of six levels (0-5), is developed where scores 0, 1, 2, 3, 4, and 5 have been categorized as highly resistant, resistant, moderately resistant, moderately susceptible, susceptible, and highly susceptible disease reaction, respectively. The disease cycle, survival of pathogen, and possible losses have also been studied to understand other features of the disease.

• Journal of Physiology & Pathology in Korean Medicine
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• v.30 no.6
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• pp.412-418
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• 2016

The emergence of systems biology in the 21st century is changing the paradigm of biomedical research. Whereas the reductionist approaches focus on components rather than time or contexts, systems biology focus more on interrelationships, dynamics, and contexts. The key ideas of the systems biology shares much with the philosophy of Korean Medicine(KM) and therefore, the paradigm shift is shedding light on understanding the mechanism of action of KM at system level. In this article, I provide a guide to learning systems biology for KM researchers using online learning resources. Thanks to the recent development of MOOC(massive open online courses) and other online learning platforms, learners can access to plenty of high-quality resources from top-tier universities in the world. I expect this guide help researchers to employ systems biology methods into their KM researches, and will lead to the development of future curricula for training "bi-lingual" experts, KM and computational approaches.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.1
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• pp.202-208
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• 2006

The goal of this study is to develop knowledge representation method for the construction and evaluation of ontology for diagnosis in oriental medicine. To develop the expert system for decision making on diagnosis and treatment, the systematic and structural knowledge which can be processible in EMR(Electronic Medical Record) must be precedent, and the Computational Process which control the system as well. This study set up an ontology as a trial model to represent the oriental medical knowledge into the machine processible one. Protege 2.1 has been used to build the ontology, and the serialization format of our ontology is the XML document based on OWL. The components of oriental medical diagnosis was arranged with the combination of symptoms which belong to the certain symptom patterns. Then natural language which expresses the oriental medical diagnosis components were converted into the logical sentence, and individual characteristic symptoms into each values of specific properties. In addition to the study, the diagnosis software for oriental medicine was developed and it used the ontology which we developed. Sequently, we tested the software to confirm the appropriateness of ontology. The result of the test shows that diagnostic questions are automatically formulated according to the diagnosis components of this ontology and that as such diagnostic results are induced. Therefore, the ontology system in this study will be efficient to develop the diagnosis program and useful as a tool for doctors to make decision. But, it is not recommendable to apply the system to the clinical environment until the clear diagnosis standards are introduced, and the more reliable diagnosis program can be developed based on the more appropriate ontology mentioned above.