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检索条件"任意字段=IEEE Computational Systems Bioinformatics Conference"

共 879 条记录，以下是61-70 订阅

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Duck Eggs Hatching Procedure with A Low Cost User Convenient Integrated Device for Developing Countries 9

Duck Eggs Hatching Procedure with A Low Cost User Convenient...

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9th Edition of ieee Region 10 Humanitarian Technology conference, R10-HTC 2021

作者： Hossain, Eftakhar Zeyad, Mohammad Ahmed, S.M. Masum Tasrif Anubhove, Md Sadik Computational Biology and Data Science Laboratory Bangladesh Energy and Technology Research Division Advanced Bioinformatics Chattogram Bangladesh Erasmus+ Joint MSC In Smart Cities and Communities Department of Thermal Engineering Bilbao Spain Mons Belgium

ISBN: (纸本)9781665432405

Poultry agriculture is a form of livestock farming that involves raising chickens, turkeys, ducks, etc, and raises them to provide eggs or meat. However, this work sheds light on the method of hatching duck eggs which maintains a suitable environment around the eggs through an automated and low cost device by controlling temperature (T) and relative humidity (RH). This paper presents a control strategy for automatic temperature and relative humidity control systems for the egg/s hatching procedure for rural areas people or unprivileged communities in the developing countries. One of the best outcomes of this work is the proper hatching procedure steps shown from the beginning to the end. Moreover, for a better understanding and outcomes, the research work has been done under a poultry farm in Charsita village, Lakshmipur, Bangladesh for 6 consecutive months of 2020. The experiment (egg/s hatching) has been conducted under an ice-box. Furthermore, a microcontroller (ATmega328p) is used to monitor the temperature and relative humidity by receiving data from the sensor DHT22 and manipulating the appropriate temperature and humidity using a logic algorithm code. Egg/s need a constant temperature and relative humidity of 35.6 °C to 37.8 °C and 60% to 80%, respectively. Nonetheless, an LCD (16*2) with a matrix keypad (4*4) has been integrated into the unit, allowing farmers to easily set, run, and monitor the appropriate temperature and relative humidity levels within the ice-box. Finally, the results of sensor testing, system device management, and performance rates are recorded to ensure the research's success. © 2021 ieee.

关键词： Microcontrollers

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Towards Aggregation based I/O Optimization for Scaling bioinformatics Applications 44

Towards Aggregation based I/O Optimization for Scaling Bioin...

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44th Annual ieee-Computer-Society International conference on Computers, Software, and Applications (COMPSAC)

作者： Stratton, Jack Albert, Michael Jensen, Quentin Ismailov, Max Jagodzinski, Filip Islam, Tanzima Western Washington Univ Dept Comp Sci Bellingham WA 98225 USA Texas State Univ Dept Comp Sci San Marcos TX USA

ISBN: (纸本)9781728173030

bioinformatics software often integrates multiple off-the-shelf programs into a single compute pipeline. Each standalone program generates output, that is frequently saved into a plain-text file, which is then processed as input by the program that is responsible for the next stage of the computation. Modern advances in genome sequencing and protein structure resolution methods have yielded large amounts of data, that when processed by bioinformatics compute pipelines, results in vast numbers of file reads and writes. Since computation capabilities of large-scale distributed systems grow much faster than their I/O bandwidth, processing such big data using these compute pipelines will not scale as the amount of data grows. For this work, we motivate and demonstrate a dynamic interception-based I/O analysis tool to assess the file read and write characteristics of a protein mutation generation pipeline. We discuss how our analysis tool can be further extended to apply compression and in-site analysis and has the potential to scale UO-intensive bioinformatics applications on high performance computing (HPC) systems.

关键词： computational Biology high performance compute pipeline I/O aggregation

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bioinformatics-inspired non-parametric modelling of pharmacokinetics-pharmacodynamics systems using differential neural networks

Bioinformatics-inspired non-parametric modelling of pharmaco...

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International Joint conference on Neural Networks (IJCNN) held as part of the ieee World Congress on computational Intelligence (ieee WCCI)

作者： Alfaro-Ponce, Mariel Chairez, Isaac Tecnol Monterrey Sch Engn & Sci Monterrey Mexico Inst Politecn Nacl Unidad Profes Interdisciplinaria Biotecnol Mexico City DF Mexico

ISBN: (纸本)9781728169262

Bionformatics and pharmacokinetics-pharmacodynamics (PKPD) systems are two conjugated tools to intensively explore the effect of new drugs on the human body running in-silico analysis. Usually, PKPD models do not consider all the biological reactions that explain the pharmaceutical effect. A complementary non-parametric modeling can be useful to recover the PKPD dynamics despite the uncertainties and external perturbations effect, which can reduce the degree of uncertainties on the drug evaluation. The aim of this study is to get a feasible non-parametric model of PKPD models using a bioinformatics inspired evaluation of antibacterial drug doses. A class of bioinformatics inspired differential neural networks (DNNs) responding to the dose modification provides the non-parametric approximation of the PKPD dynamics. The DNN modeling strategy was applied to approximate the dynamics of PKPD models under four different dosing regimes. The modeling strategy estimated the bacteria survival (measured as the logarithm of the colony forming units per milliliter) after the drug application. The same adjusted DNN-based model confirmed the ability of designing an off-line lab for evaluating diverse dosing strategies of antibacterial pharmaceutical.

关键词： Pharmacokinetics

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Application of Artificial Intelligence in Malignant Tumors: A Science Mapping Analysis

Application of Artificial Intelligence in Malignant Tumors: ...

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ieee Control and System Graduate Research Colloquium (ICSGRC)

作者： Samuel-Soma M. Ajibade Anwar P.P. Abdul Majeed Muhammed Basheer Jasser Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Ganesh Kumar Department of Computing and Information Systems School of Engineering & Technology Sunway University Petaling Jaja Selangor Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkiye Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia

ISBN: (数字)9798350386554

ISBN: (纸本)9798350386561

A Science mapping analysis was performed on Scopus papers spanning from 2003 to 2022 to investigate the utilization of artificial intelligence techniques in malignant tumors research. The analysis unveiled a progressive rise in publications, amounting to a total of 206, with articles accounting for 55.34% and conference papers making up 44.66%. Zuherman Rustam and Universitas Indonesia made substantial contributions. India's exceptional performance might be due to the availability of financial support from prestigious organizations such as Universitas Indonesia, the National Natural Science Foundation of China, and Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico. An investigation of social networks, specifically looking at collaborations between authors, revealed robust international cooperation, which improved the availability of resources and infrastructure. The hotspot research revealed the prominent terms to be genetic algorithms, diseases, and feature extraction. The cluster analysis identified three main areas of focus: Precision Health Analytics, Genomic Cancer Profiling, and Integrated AI Diagnosis. In summary, AIMTR research is actively tackling cancer and oncological concerns through the use of computational approaches, making substantial contributions to both societal and scientific progress.

关键词： Productivity Social networking (online) Genomics Organizations Market research Paper making Artificial intelligence bioinformatics Cancer Genetic algorithms

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The genetic approach application and creation of the project genetic model

The genetic approach application and creation of the project...

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International Computer Sciences and Information Technologies (CSIT)

作者： Sergiy Rudenko Tetiana Kovtun Tetiana Smokova Finohenova Iryna Rector Odesa National Maritime University Odesa Ukraine Department of Logistic Systems and Projects Management Odesa National Maritime University Odesa Ukraine

ISBN: (纸本)9798350334326

The modern trend in the development of project management is the convergence of general management and natural-scientific approaches. Recently, the analogy of the project with a living organism or a biological system has been actively considered. This led to the application of biological and genetic approaches in project management, which is an actual direction of methodological development and is considered a source of non-standard ideas and solutions, allowing to significantly expand the traditional view of the project and improve the process of its management.

关键词： computational modeling Project management Genomics Market research Organisms Information technology bioinformatics

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computational method of database construction for genetic variant calling

Computational method of database construction for genetic va...

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ieee International conference on Big Data

作者： Sunhee Kim Young-Suk Lee Chang-Yong Lee Dept. of Industrial & Systems Engineering Kongju National University Chunan South Korea Dept. of Bio and Brain Engineering KAIST Taejeon South Korea

ISBN: (纸本)9781665480468

In this study, we examined the impact of the variant database in recalibration and developed a database-generation model that gathers potential candidates directly from resequencing genome data. Based on human genome data, we optimize the hyper-parameters in the model and evaluate the performance improvements both in terms of recalibration and variant calling. To test whether our pseudo-database approach is applicable to species other than human, we constructed pseudo-databases for sheep, rice, and chickpea, and compared its performance with dbSNP. Consistently, we find that our pseudo-database provides improved recalibration and error rates. More importantly, the use of pseudo-databases led to the identification of additional genetic variants. Therefore, the reanalysis with our pseudo-databases approach effectively recalibrates the base quality scores and consequently uncovers hidden genetic variations in published resequencing data.

关键词： Databases Error analysis Sociology Genomics Big Data Data models bioinformatics

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MQML: Multi-Omic Quantum Machine Learning Based Cancer Classification, Biomarker Identification in Human Lung Adenocarcinoma

MQML: Multi-Omic Quantum Machine Learning Based Cancer Class...

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Quantum Computing and Engineering (QCE), ieee International conference on

作者： Mandeep Kaur Saggi Sabre Kais Department of Chemistry Purdue University West Lafayette IN USA Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum machine learning (QML) presents novel opportunities for resolving, accelerating, and refining computational biology challenges. In biomedical research and personalized medicine, multi-omics integration offers a comprehensive understanding of complex biological systems, bridging the gap between basic research and clinical applications. Recent advancements in quantum computing could enhance multi-omics integration by leveraging unique quantum principles, potentially revealing intricate patterns in multi-omics datasets and offering new insights into the molecular landscape of lung cancer. This study focuses on the integration, feature selection, diagnostic classification, and biomarker identification of lung cancer types-LUAD primary tumor and solid tissue normal-using QML. Given the heterogeneity and high dimensionality of the data, we aim to assess the performance of QML in feature selection, reduction, encoding, and diagnostic classification in integrated multi-omics cancer datasets. We demonstrate the efficacy of Quantum Neural Networks (QNNs) for classifying multi-omics lung cancer data from The Cancer Genome Atlas, comparing our framework with 16 and 64 dimensions of feature using QML. Our results show that the Multi-omic Quantum Machine Learning (MQML) framework achieves superior classification performance with smaller training datasets, as determined by significant and non-significant p-values. This study provides empirical evidence for the potential application of unconventional computing approaches in biomed-ical sciences, particularly in oncology research, including cancer subtype diagnostic, survival, and stage classification. Key results include the identification of sixteen genes and thirty-two genes with the hybrid quantum-classical neural net-work model, with notable genes such as MIR632, CYP19A1, RP11-108K3.1, C14orf39, THUMPD1, ACSM3, GSC, MIR92a-2, DYRK1B, DNAJA1, IRX1, FMR1, POU2F2, MIR9-2, ZNF274, MIR196a-1, MIR3653, MIR33b, MIR301a, MIR182

关键词： Training Quantum computing Lungs Transcriptomics Lung cancer Machine learning Feature extraction Encoding bioinformatics Tumors

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BroadSurv: A Novel Broad Learning System-based Approach for Survival Analysis

BroadSurv: A Novel Broad Learning System-based Approach for ...

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2021 International conference on Information, Cybernetics, and computational Social systems, ICCSS 2021

作者： Wu, Guangheng Duan, Junwei Wang, Jing Wang, Lu Dong, Cheng Lv, Changwei Jinan University College of Information Science and Technology Guangzhou China Guangdong Polytechnic Normal University School of Computer Science Guangzhou China Jinan University Academic Affairs Office Guangzhou China Jinan University School of Intelligent System Science and Engineering Zhuhai China Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9781665402453

Survival analysis (time-to-event analysis) is a set of statistic methods to analyze time-to-event data and is widely used in many fields such as economics, finance and medicine. One of the fundamental problems in survival analysis is to explore the relationship between the covariates and the survival time. Recently, with the development of deep learning-based techniques, various approaches have been proposed for survival analysis. To better handle the censoring, special cost functions or sophisticated network structures are usually designed for these methods. In this paper, a novel two-stage method is proposed to model the survival data. In the first stage, pseudo conditional probabilities are computed, which can act as the quantitative response variables in regression problems. In the second stage, with these pseudo values, a complicated survival analysis problem is transformed into a regression problem that can be effectively solved by broad learning system. The experimental results show that, with a flexible structure and a simple cost function, our proposed method has a better performance in handling the censored problems. © 2021 ieee.

关键词： bioinformatics

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Intuitionistic Fuzziness and Modality 10

Intuitionistic Fuzziness and Modality

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10th ieee International conference on Intelligent systems, IS 2020

作者： Atanassov, Krassimir Dept. of Bioinformatics and Mathematical Modelling Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences Bl. 105 Acad. G. Bonchev Str. Sofia1113 Bulgaria Intelligent Systems Laboratory Prof. Asen Zlatarov University Burgas8010 Bulgaria

ISBN: (纸本)9781728154565

Ever since the first publication over intuitionistic fuzzinsess, the two standard modal operators "necessity"( ? ) and "possibility"(?) are introduced over intuitionistic fuzzy sets. In the years, these two operators are object of a lot of extensions and modifications. In the present paper, a new direction of tow modifications of the two standard modal operators is discussed. We introduce four new operators and study their properties. © 2020 ieee.

关键词： Fuzzy sets

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A real-time HFO detection framework for localization of SOZ in iEEG recording of patients with epilepsy

A real-time HFO detection framework for localization of SOZ ...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Behrang Fazli Besheli Chandra Prakash Swamy Amir Hossein Ayyoubi Luciano R. F. Branco Gregory A. Worrell Zhiyi Sha Jay R. Gavvala Kai J. Miller Nuri F. Ince Department of Neurologic Surgery Mayo Clinic Rochester MN USA Bioinformatics and Computational Biology Department University of Minnesota Minneapolis MN USA Department of Biomedical Engineering University of Houston Houston TX USA Department of Neurology Mayo Clinic Rochester MN USA Department of Neurology University of Minnesota Minneapolis MN USA Department of Neurology UT Health Houston TX USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Interictal High-frequency Oscillation (HFO) between 80-600 Hz in intracranial EEG (iEEG) is a promising biomarker of the epileptogenic zone in individuals with epilepsy. Numerous studies revealed that the resection of channels with a high rate of HFOs correlates with favorable surgical outcomes. Early feedback to clinicians regarding the distribution of HFOs during the iEEG recording, especially after the implantation of electrodes, would be helpful for clinical decisions. However, iEEG recording can easily get corrupted by various factors mimicking real HFOs. This study presents a real-time HFO detection framework within MATLAB/Simulink that exhibits robustness against such pseudo-HFOs. This detector is responsible for identifying the initial pool of candidate HFOs and transmitting them via user datagram protocol (UDP) to an external application. The external application contains a machine learning tool that is utilized for post-processing and isolating the real-HFOs. It is implemented in a graphical user interface (GUI) that provides visual feedback regarding the distribution and waveforms of HFOs. The entire processing pipeline was validated by randomly selecting 10-minute segments of interictal iEEG recordings from 10 subjects. It successfully identifies the seizure onset zone (SOZ) in these subjects, achieving an average accuracy of 65% using the detected Ripples and 74% using the detected events with both Ripples and Fast Ripples. Importantly, the spatio-temporal distribution of detected HFOs in real-time showed more than 98% spatio-temporal similarity index compared to offline analysis. Our framework proves to be an effective tool for the automatic identification of HFOs in real-time with the ability to promptly stream the HFO analysis results and provide early feedback regarding the probable SOZ regions to clinicians for surgical decision-making.

关键词： Visualization Protocols Pipelines Surgery Epilepsy Hafnium oxide Real-time systems Robustness Recording Graphical user interfaces

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