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检索条件"任意字段=9th IEEE International Conference on BioInformatics and BioEngineering"

共 229 条记录，以下是1-10 订阅

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2021 ieee 9th international conference on bioinformatics and Computational Biology, ICBCB 2021

2021 IEEE 9th International Conference on Bioinformatics and...

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9th ieee international conference on bioinformatics and Computational Biology, ICBCB 2021

ISBN: (纸本)9780738132020

the proceedings contain 32 papers. the topics discussed include: construction of Chinese freshwater fish information database;identification of potentially therapeutic target genes in ovarian cancer via bioinformatic approach;ITGAX: a potential biomarker of acute myeloid leukemia (AML) through bioinformatic analysis;using machine learning models to study medication adherence in hypertensive patients based on national stroke screening data;investigating spatio-temporal cellular interactions in embryonic morphogenesis by 4D nucleus tracking and systematic comparative analysis — taking nematodes C. elegans and C. briggsae as examples;and drug-target interaction identification via dual-graph regularized robust PCA in heterogeneous networks.

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2021 ieee the 9th international conference on bioinformatics and Computational Biology [Title page]

2021 IEEE the 9th International Conference on Bioinformatics...

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ieee international conference on bioinformatics and Computational Biology (ICBCB)

Presents the title page of the proceedings record.

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2021 ieee the 9th international conference on bioinformatics and Computational Biology

2021 IEEE the 9th International Conference on Bioinformatics...

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ieee international conference on bioinformatics and Computational Biology (ICBCB)

Presents the front cover or splash screen of the proceedings record.

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Low Cost Optical Maskless Photolithography Patterning for Pt/Ti Micro-Electrodes for Microelectromechanical Systems Using a Microlight 3D System 9

Low Cost Optical Maskless Photolithography Patterning for Pt...

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9th international Engineering, Sciences and Technology conference, IESTEC 2024

作者： Villarreal, Daniel Estrada, Victoria De Obaldia, Elida Auciello, Orlando University of Texas at Dallas Department of Material Sciences and Engineering RichardsonTX United States Universidad Tecnologica de Panama Centro de Estudios Multiplinarios en Ciencias Panamá Panama University of Texas at Dallas Materials Science & Engineering and & Bioengineering RichardsonTX United States

the research described in this article focused on developing a process to produce microelectrodes 20 μ ~m long/ 10 μ ~m wide ( 9; 2024 ieee.

ISBN: (纸本)9798350377156

关键词： Microelectrodes

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Application of Machine Learning in the Analysis of Gene Expression in Colorectal Cancer Cells Treated with Chemotherapeutics 24

Application of Machine Learning in the Analysis of Gene Expr...

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24th ieee international conference on bioinformatics and bioengineering, BIBE 2024

作者： Pavić, Jelena Živanović, Marko Pavić, Ognjen Virijević, Katarina Mladenović, Tamara Tanasković, Irena Filipović, Nenad Institute for Information Technologies Bioengineering Laboratory Kragujevac Serbia University of Kragujevac Faculty of Medical Sciences Department of Histology and Embryology Kragujevac Serbia University of Kragujevac Bioengineering Research and Development Center Faculty of Engineering Kragujevac Serbia

ISBN: (纸本)9798331518622

Colorectal cancer is a leading cause of cancerrelated deaths, and understanding its molecular mechanisms is key to improving treatments. this study applies machine learning algorithms to analyze the expression of genes related to redox balance, apoptosis, and cell migration in HCT-116 colorectal cancer cells treated with chemotherapeutics (5fluorouracil, oxaliplatin, irinotecan, and leucovorin). the analyzed genes include those for redox homeostasis (GPX1, GPX2, GPX3, GPX4, TXNRD1, GSTP1, NFE2L2, NFKB1, HIF1A), apoptosis (CASP3, CASP8, CASP9, FAS, BCL-2, BAX), and genes coding cytoskeleton proteins (CDH1, CTNNB1, CDH2). Machine learning models, such as decision trees and random forests were used to analyze gene expression changes based on qPCR data. Notably, GPX4 and BAX were linked to chemoresistance, while CDH1 upregulation suggested an effect on cancer cell migration. this study demonstrates the potential of machine learning in identifying gene expression patterns and aiding in the development of personalized CRC treatments. © 2024 ieee.

关键词： Cancer cells

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Parkinson's Disease Diagnosis from Patients Speech Analysis 9

Parkinson's Disease Diagnosis from Patients Speech Analysis

引用

9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Harshitha Reddy, T. Aryagopal Sai Jagadeesh, Dumpa Venkata Anand, M. Pati, Peeta Basa Bhanu Prakash, K.N. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science And Engineering Bengaluru India A∗star Bioinformatics Institute Singapore

Parkinson9;s disease is a neurological disorder mostly observed in the population over the age of 60 years. Over 8.5 million people world-wide are affected by it. Early diagnosis of the disease helps take preventiv... 详细信息

ISBN: (纸本)9798350394474

Parkinson's disease is a neurological disorder mostly observed in the population over the age of 60 years. Over 8.5 million people world-wide are affected by it. Early diagnosis of the disease helps take preventive and curative measures to enable the affected individuals to lead a normal life. Quality of speech is one of the key indicators for identification of the *** this study, we investigate the effectiveness of multiple machine learning algorithms on various speech related attributes to determine the existence of this disease. Additionally, we identify the crucial features which impact the model's functioning. the study employs Explainable AI (XAI) to investigate the influence of voice features on the research outcomes. the experimental findings showcase a diagnostic accuracy of 95% using Random Forest & XGBoost classifiers. We, using SHAP, identify that 6 features out of a set of 24 provide the best efficiency of diagnosis. © 2024 ieee.

关键词： Support vector machines

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Evaluating the Effectiveness of Transformer Models for Molecular Property Prediction: A Review 9

Evaluating the Effectiveness of Transformer Models for Molec...

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9th international conference on Information Technology and Digital Applications, ICITDA 2024

作者： Imani, Alyssa Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Transformer models, originally successful in natural language processing, are now being applied to chemical and biological studies, excelling in areas such as molecular property prediction, material science, and drug discovery. BERT, a Transformer-based model, has become foundational in cheminformatics, particularly for QSAR (Quantitative Structure-Activity Relationship) modeling and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluations in drug discovery. However, achieving higher accuracy often requires designing more complex models, which can compromise their interpretability. this posing a challenge for researchers who need to understand the reasoning behind the predictions. the trade-off between accuracy and interpretability presents a critical challenge in applying black box models to real-world problems in cheminformatics. this work compares Transformer-based models with traditional machine learning and deep learning approaches, focusing on both interpretability and performance. the goal is to highlight the strengths and limitations of each method, offering insights into their optimal use in drug discovery and material science. © 2024 ieee.

关键词： Prediction models

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λ Paghe Basic Reproductive Number Invasion Analysis using a three Compartment Epidemiological Model 9

λ Paghe Basic Reproductive Number Invasion Analysis using a...

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9th international Engineering, Sciences and Technology conference, IESTEC 2024

作者： Matos, Grimaldo E. Urena Universidad Tecnológica de Panamá Biotechnology Bioinformatics and Systems Biology Group Panamá Panama

ISBN: (纸本)9798350377156

this work investigates a three-compartment model for representing the epidemiological dynamics of temperate phages, using the λ phage as a model organism. Temperate phages can switch between lysogenic and lytic pathways, providing differential survival advantages under varying environmental conditions. the model captures the dynamics of susceptible hosts, lysogenic infected hosts, and free viral particles (SLV). the study identifies critical limitations of the SLV model, such as the absence of an explicit lytic compartment. the study explores the conditions favoring lysogenic or lytic strategies through invasion analysis using the next-generation method theorem to estimate the R0. A critical carrying capacity is used to determine which strategy is advantageous under specific host densities. the findings emphasize the adaptability of temperate phages in switching between strategies based on environmental cues. the study concludes that more sophisticated models are needed to accurately represent the life-history traits of temperate phages, accounting for variable mortality rates, explicit lytic pathways, and the plasticity of temperate strategies. this research provides insights into the ecological and evolutionary contexts influencing the prevalence of temperate strategies in viral populations. © 2024 ieee.

关键词： Anthropogenic

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Machine Learning-Assisted Protein Structure Prediction: An AI Approach for Biochemical Insights 9

Machine Learning-Assisted Protein Structure Prediction: An A...

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9th international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Mahendran, S. Radha Dogra, Akriti Mendhe, Dinesh Babu, S. B. G. Tilak Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology And Advanced Studies Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India Rutgers State University of New Jersey Office of Research Computing New BrunswickNJ United States Aditya Engineering College Department of Ece Surampalem India School of Computer Science Engineering Vellore Institute of Technology Tamil Nadu Vellore India Upums Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350365092

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. this paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. the suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. the study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level. © 2024 ieee.

关键词： Proteins

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Accelerating Pharmaceutical Research and Development through AI-Driven Drug Discovery 9

Accelerating Pharmaceutical Research and Development through...

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9th international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Bhargavi, V. Sesha Dogra, Akriti Bhatt, Alok Ghildiyal, Pallavi Mahendran, S. Radha Kaur, Ranpreet G Narayanamma Institute of Technology & Science Department of Information Technology Shaikpet Hyderabad India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India School of Pharmacy Graphic Era Hill University Uttarakhand Dehradun India Uttaranchal Institute of Pharmaceutical Sciences Uttaranchal University Uttarakhand Dehradun India Pallavaram Department of Bioinformatics Chennai India Bharati Vidyapeeth Deemed To Be University Institute of Management And Entrepreneurship Development Department Management Maharashtra Pune India

ISBN: (纸本)9798350365092

At the intersection of artificial intelligence and the pharmaceutical R&D sector, a new era in drug development has started. In this piece, we provide a thorough analysis of AI-driven drug development and discuss how it might revolutionize the R&D sector in the pharmaceutical business. this research approach, which includes data collection, model construction, and ethical considerations, has shown promising results. Artificial intelligence models have shown very high prediction accuracy, reducing costs and enhancing equity while speeding up the creation of new medications. these findings may improve patient outcomes and increase therapy accessibility. Future priorities will include therapeutic subspecialization, worldwide access initiatives, interpretability, regulatory compliance, data exchange, and multi-omics integration. AI and medications working together might result in a healthcare system that is more accessible to everyone and more effective. © 2024 ieee.

关键词： Data integration

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