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3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Choudhary, Tisya Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Evaluating the Segmentation Performance of Gross Volume Tumor in Cervical Cancer Using MRI Images

Evaluating the Segmentation Performance of Gross Volume Tumo...

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IEEE symposium on computational intelligence and bioinformatics and computational biology (cibcb)

作者： Nazar Zaki Anusuya Krishnan Rafat Damseh Wenjian Qin Ayham Zaitouny Isaias Ghebrehiwet College of IT United Arab Emirates University Al Ain UAE Shenzhen Institute of Advanced Technology Chinese Academy of Science China College of Science United Arab Emirates University Al Ain UAE

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Cervical Cancer (CC) is the most prevalent gynecologic malignancy worldwide. Tumor segmentation in CC is a crucial step for radiotherapy treatment and planning. The clinical practice involves laborious slice-by-slice segmentation of the primary tumor using simultaneous assessments from several image modalities, and ignores spatial ambiguity in tumor delineation. This work evaluates the performance of a state-of-the-art Landing AI for automated 3D medical image segmentation applied to Gross Tumor Volume (GTV) in CC from Magnetic Resonance Imaging (MRI) scans. Our work provides a novel in-house labeled dataset with a systematic assessment of the segmented lesions after network training on various voxel spacing MRI images. The segmentation performance was assessed using the dice coefficient. We demonstrated that training on MRI images to optimize Landing AI achieves an improved dice score of 0.92, outperforming other MRI models.

关键词： Training Image segmentation Three-dimensional displays Systematics Magnetic resonance imaging Radiation therapy Planning Lesions Artificial intelligence Cervical cancer

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Towards Explainability of Dimension Reduction Plots of Unsupervised Learning Model Outcomes

Towards Explainability of Dimension Reduction Plots of Unsup...

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IEEE symposium on computational intelligence and bioinformatics and computational biology (cibcb)

作者： Tony E. Astuhuaman Davila Daniel B. Hier Tayo Obafemi-Ajayi Computer Science Dept. Missouri State University Springfield MO USA Kummer Institute Missouri University of Science & Technology Rolla MO USA Engineering Program Missouri State University Springfield MO USA

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Dimension reduction methods are used to visualize the output of unsupervised learning models when applied to complex data. These techniques improve interpretability by transforming a high-dimension space to a lower-dimension space (usually 2D or 3D). The results are typically viewed as 2D scatter plots, and class centroids may be added to increase interpretability. Although useful, the relationship of these class centroids to the underlying feature space remains opaque. The innovative aspect of this work is to create a strong link between the dimension-reduced space and the underlying high-dimension feature space by adding selected feature centroids to the 2D scatter plots. This approach simultaneously visualizes the centers for the classes and the features on the same 2D scatter plot. Since classes are often imbalanced, we provide a method to balance class sizes. We present an automated framework that performs a grid search to find the optimal dimension reduction parameters, balances the class sizes, uses an ensemble approach to find the most important features, and adds class centroids and selected feature centroids to 2D dimension-reduced plots. This is especially useful when applied to complex, feature-rich biomedical data, as addition of feature centroids to 2D scatter plots serve as landmarks for the previously featureless dimension-reduced space. The utility of this approach is demonstrated by its application to seven classes of neurogenetic diseases with 31 defining phenotypic features.

关键词： Dimensionality reduction Three-dimensional displays Phenotypes Data visualization Data models Ensemble learning bioinformatics Unsupervised learning computational intelligence Diseases

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A Role of Wearable Health Technology in Smart Cities 3rd

A Role of Wearable Health Technology in Smart Cities

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3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Mehta, Khushi Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

In this rapidly changing era of the world, healthcare devices are revolutionizing, which enables the continuous measurement of crucial biological markers for diagnostic purposes, physiological health monitoring, and evaluation. Hence, wearable devices have transformed into accessories, embedded clothing, body adaptors, and body implants. Additionally, the emergence of wearable healthcare devices that facilitates the diagnosis and prognosis by using tiny sensing devices and biomedical devices has greatly enhanced the efficiency as well as the quality of medical care facilities results in the tremendous advancements in semiconductor technology, biomedical technologies, and nano-sized materials over the past few decades. Meanwhile, the devices could provide real-time feedback, allowing users to analyze their health and monitor in a specific period that has been assigned to the smart wearable devices, even though the industries and manufacturers are intensely interested in interpreting the factors that influence the adoption of new smart technologies, which helps to improve the performance and viability of wearable devices to fascinate consumers. Hence, the advancements in technology are regarded as trustworthy equipment for everlasting monitoring of health that are frequently used to monitor and control a diverse of health tracking predictors in the surroundings, including monitoring health and wellness of the body. In recent years, it has been focused on the study of a wide range of parameters closer to advancements in smart technology. As a result, these smart devices are now utilized in a variety of healthcare monitoring applications, one of the most crucial aspects of data collection is the various factors based on bodily activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Artificial Neural Networks for the Rapid Prediction of Possible Ferroptosis Inducers Using the GPx4 Enzyme

Artificial Neural Networks for the Rapid Prediction of Possi...

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IEEE symposium on computational intelligence and bioinformatics and computational biology (cibcb)

作者： Jesus Antonio Alvarado-Huayhuaz Miquéias Amorim Santos Silva Elvis Mauricio Jimenez Peña Joel Claudio Rengifo Maraví Angella M. Cordova-Muñoz Karina dos Santos Machado Facultad de Ciencias Universidad Nacional de Ingeniería Lima Peru Centro de Ciências Computacionais Universidade Federal do Rio Grande Rio Grande Brazil Departamento de Fisicoquímica Universidad Nacional Mayor de San Marcos Lima Peru Departamento de Química Universidad Nacional Agraria La Molina Lima Peru Departamento de Farmacología Universidad Nacional de Trujillo Trujillo Peru

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Cancer affects 20 million people worldwide; therefore, accelerated and urgent pathways are needed for the search for new drugs. The reactivation of cell death induced by the accumulation of iron-dependent lipid peroxides (Ferroptosis) is a potential mechanism for cancer treatment. One pathway to trigger Ferroptosis involves the inactivation of the glutathione-dependent peroxidase (GPx4) protein. Molecules with high affinity for iron are also being studied against cancer through chelation therapy. Therefore, the rapid detection of such molecules in molecular databases is of interest. Catechols exhibit a high iron-chelating power and can be synthesized by microorganisms as a chemical strategy for acquiring Fe 3+ . They can also be found in natural products. Recently, the natural product database in Peru (PeruNPDB) has been published where we found catechols; however, these have not yet been studied in GPx4. To expedite the search for catechols with affinity for the active site of GPx4, in large databases, we have developed a prediction model for scoring based on artificial neural networks. Our initial database consisted of the 83 catechols and 1,024 molecular descriptors. The target is the docking score obtained by ligand-receptor GPx4 coupling with the Autodock Vina software. Emphasizing the application of recursive feature elimination to improve the model’s performance and provides both the model and the procedure to be applied and improved by the scientific community. Our program enables the experimenter to rapidly filter molecules with high affinity specifically for the active site of GPx4, in the search for potential inhibitors.

关键词： Training Databases computational modeling Cancer treatment Artificial neural networks Predictive models Software Compounds Cancer Software development management

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3rd International symposium on Mathematical and computational Oncology, ISMCO 2021

3rd International Symposium on Mathematical and Computationa...

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3rd International symposium on Mathematical and computational Oncology, ISMCO 2021

ISBN: (纸本)9783030912406

The proceedings contain 7 papers. The special focus in this conference is on Mathematical and computational Oncology. The topics include: The Role of Hydrophobicity in Peptide-MHC Binding;Simulating Cytotoxic T-Lymphocyte and Cancer Cells Interactions: An LSTM-Based Approach to Surrogate an Agent-Based Model;strategies to Reduce Long-Term Drug Resistance by Considering Effects of Differential Selective Treatments;Improved Geometric Configuration for the Bladder Cancer BCG-Based Immunotherapy Treatment Model;run for Your Life – An Integrated Virtual Tissue Platform for Incorporating Exercise Oncology into Immunotherapy.

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Strategies to Reduce Long-Term Drug Resistance by Considering Effects of Differential Selective Treatments 3rd

Strategies to Reduce Long-Term Drug Resistance by Considerin...

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3rd International symposium on Mathematical and computational Oncology, ISMCO 2021

作者： Ghodsi Asnaashari, Tina Chang, Young Hwan Department of Biomedical Engineering and Computational Biology Program Oregon Health and Science University Portland United States

ISBN: (纸本)9783030912406

Despite great advances in modeling and cancer therapy using optimal control theory, tumor heterogeneity and drug resistance are major obstacles in cancer treatments. Since recent biological studies demonstrated the evidence of tumor heterogeneity and assessed potential biological and clinical implications, tumor heterogeneity should be taken into account in the optimal control problem to improve treatment strategies. Here, first we study the effects of two different treatment strategies (i.e., symmetric and asymmetric) in a minimal two-population model to examine the long-term effects of these treatment methods on the system. Second, by considering tumor adaptation to treatment as a factor of the cost function, the optimal treatment strategy is derived. Numerical examples show that optimal treatment decreases tumor burden for the long-term by decreasing rate of tumor adaptation over time. © 2021, Springer Nature Switzerland AG.

关键词： Tumors

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Classification of adaptor proteins using recurrent neural networks and PSSM profiles

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BMC GENOMICS 2019年第9期20卷 1-9页

作者： Le, Nguyen Quoc Khanh Nguyen, Quang H. Chen, Xuan Rahardja, Susanto Nguyen, Binh P. Taipei Med Univ Profess Master Program Artificial Intelligence Me Keelung Rd Taipei 106 Taiwan Hanoi Univ Sci & Technol Sch Informat & Commun Technol 1 Dai Co Viet Hanoi 100000 Vietnam Beijing Genom Inst 21 Hongan 3rd St Shenzhen 518083 Guangdong Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China Victoria Univ Wellington Sch Math & Stat Gate 7Kelburn Parade Wellington 6140 New Zealand

Background: Adaptor proteins are carrier proteins that play a crucial role in signal transduction. They commonly consist of several modular domains, each having its own binding activity and operating by forming complexes with other intracellular-signaling molecules. Many studies determined that the adaptor proteins had been implicated in a variety of human diseases. Therefore, creating a precise model to predict the function of adaptor proteins is one of the vital tasks in bioinformatics and computational biology. Few computational biology studies have been conducted to predict the protein functions, and in most of those studies, position specific scoring matrix (PSSM) profiles had been used as the features to be fed into the neural networks. However, the neural networks could not reach the optimal result because the sequential information in PSSMs has been lost. This study proposes an innovative approach by incorporating recurrent neural networks (RNNs) and PSSM profiles to resolve this problem. Results: Compared to other state-of-the-art methods which had been applied successfully in other problems, our method achieves enhancement in all of the common measurement metrics. The area under the receiver operating characteristic curve (AUC) metric in prediction of adaptor proteins in the cross-validation and independent datasets are 0.893 and 0.853, respectively. Conclusions: This study opens a research path that can promote the use of RNNs and PSSM profiles in bioinformatics and computational biology. Our approach is reproducible by scientists that aim to improve the performance results of different protein function prediction problems. Our source code and datasets are available at https://***/ngphubinh/adaptors.

关键词： Adaptor proteins Prediction Classification Deep learning RNN GRU PSSM

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Transcriptome profiling analysis reveals that CXCL2 is involved in anlotinib resistance in human lung cancer cells

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BMC MEDICAL GENOMICS 2019年第2期12卷 15-22页

作者： Lu, Jun Xu, Wei Qian, Jie Wang, Shuyuan Zhang, Bo Zhang, Lele Qiao, Rong Hu, Minjuan Zhao, Yiming Zhao, Xiaodong Han, Baohui Shanghai Jiao Tong Univ Shanghai Ctr Syst Biomed 800 Dong Chuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Chest Hosp Dept Pulm Med 241 Huaihai West Rd Shanghai 200030 Peoples R China

BackgroundAnlotinib has been demonstrated its anti-tumor efficacy on non-small cell lung cancer (NSCLC) in clinical trials at 3rd line. However, anlotinib resistance occurs during its administration, and the underlying mechanism is still *** resistant lung cancer cell line NCI-H1975 was established in vitro. Toxicologic effects undergoing anlotinib stress were observed upon NCI-H1975 cells and anlotinib resistant NCI-H1975 cells, respectively. Transcriptome profiling was performed to screen anlotinib resistance-associated genes between NCI-H1975 cells and anlotinib resistant NCI-H1975 cells. Functional assays were performed to examine the correlations between CXCL2 gene expression and anlotinib *** found anlotinib inhibits cell proliferation and cell viability in NCI-1975 cells, whereas it attenuates these activities in anlotinib resistant NCI-H1975 cells. Transcriptome profiling analysis identified 769 anlotinib-responsive genes enriched in the biological processes of microtubule-based process, cytoskeleton organization, and wound healing. Furthermore, we found 127 genes are associated with anlotinib resistance. In particular, we demonstrated that CXCL2 contributes to anlotinib resistance in NCI-H1975 *** study suggested that CXCL2 is involved in anlotinib resistance in NCI-H1975 cells and provided an insight for understanding the resistant mechanism of anlotinib.

关键词： Anlotinib Drug resistance CXCL2 Non-Small Cell Lung Cancer Transcriptome

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm's Arthropod Community Modelling 3

Ecological Show Cave and Wild Cave: Negative Binomial Gllvm'...

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3rd International Conference on Computer Science and computational intelligence, ICCSCI 2018

作者： Caraka, Rezzy Eko Shohaimi, Shamarina Kurniawan, Isma Dwi Herliansyah, Riki Budiarto, Arif Sari, Shinta Purnama Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Indonesia Kalimantan Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Ecology is a branch of biology that studies the interaction and relationship between organisms and their environment. Abundance, distribution of organisms and patterns of biodiversity are great interests for many ecologists. One of interesting ecosystems to be studied is a cave. A cave has a typical environment character with a vulnerable ecosystem. Many caves in Indonesia, particularly in Gunungsewu karst area have been developed into tourist objects (show caves) and managed imprudently. Such cave management has potential to harm the environment and leads to ecosystem destruction. Arthropods are the most abundance fauna in cave that play critical roles in maintaining cave ecosystems equilibrium. In the heart of statistical ecology, we need to analyze the differences on Arthropods community and abiotic (climatic-edaphic) parameters among show caves and wild caves. Statistical techniques are needed for the extraction of such information. GLLVM is one method that is able to explain spatial-based information and is particularly suitable for ecology. In this paper, we use negative binomial models to see the differences on spatial patterns of predator and decomposer Arthropods, also characteristic of edaphic and climatic in each cave. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Caves

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