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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21-30 订阅

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Transparent AI Models for Meningococcal Meningitis Diagnosis: Evaluating Interpretability and Performance Metrics 12

Transparent AI Models for Meningococcal Meningitis Diagnosis...

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12th ieee International conference on Intelligent systems, IS 2024

作者： Messai, Aya Drif, Ahlem Ouyahia, Amel Guechi, Meriem Rais, Mounira Kaderali, Lars Cherifi, Hocine University of Ferhat Abbas Sétif 1 Lrsd Laboratory Sétif19000 Algeria University of Ferhat Abbas Sétif 1 Department of computer science Sétif19000 Algeria University of Ferhat Abbas Sétif 1 STDs Hiv Aids unit Department of Medicine Sétif19000 Algeria Chu Saadna Abdenour Hospital Sétif Hospital Sétif19000 Algeria University of Ferhat Abbas Sétif 1 Department of Medicine Sétif19000 Algeria Institute of Bioinformatics University Medicine Greifswald Felix-Hausdorf-Str. 8 Greifswald17475 Germany University of Burgundy Dijon Laboratoire Interdisciplinaire Carnot de Bourgogne Icb Umr 6303 Cnrs France

ISBN: (纸本)9798350350982

Meningococcal meningitis, a severe bacterial infection, demands precise diagnosis and immediate intervention. This paper explores the potential of Artificial Intelligence (AI) in improving diagnostic accuracy and clinical decision-making for this condition. We assess interpretability and reliability by analyzing the decision-making process of Machine Learning (ML) models tailored to identify Meningococcal meningitis among different etiologies of meningitis. Initially, we train and test various ML models, including logistic regression, K-nearest neighbors, support vector machine, decision tree, gradient boosting, AdaBoost, random forest, and LightGBM classifier, on a dataset comprising 460 cases of Meningococcal meningitis and 474 cases of other types of meningitis. Among these models, the gradient-boosting model exhibits superior performance metrics, including accuracy (0.88), precision (0.92), recall (0.83), AUROC (0.93), and f1-score (0.87). Subsequently, we employ Explainable AI (XAI) tools (ELI5 and LIME) to elucidate the importance of features in the ML models. Our results highlight key factors contributing to model success, such as Neisseria meningitidis identified through cerebrospinal fluid (CSF) culture and latex agglutination, gram-negative diplococci in CSF smear examination, white cell counts in CSF, and patient age. Local explanations reveal the presence of neutrophils in CSF as a characteristic feature of Meningococcal meningitis. LIME analysis indicates the significance of low lymphocyte percentages and elevated white blood cell counts in predicting this condition. These findings underscore the effectiveness of integrating global and local interpretability techniques, aligning with expert knowledge, and emphasizing the importance of transparent AI models in clinical decision-making processes. © 2024 ieee.

关键词： Cerebrospinal fluid

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Utilizing computational Modeling to Discover the Patterns of DNA Methylation in Acute Myeloid Leukemia Among Various Racial Populations 8

Utilizing Computational Modeling to Discover the Patterns of...

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8th International conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2024

作者： Agustriawan, David Darmawan, Jeremie Theddy Permana, Angga Aditya Overbeck, Marlinda Vasty Yuliani, Sy. Sitorus, Budi Berlinton School of Engineering and Informatics Multimedia Nusantara University Department of Informatics Banten Tangerang Indonesia School of Life Sciences Indonesia International Institute for Life Sciences Department of Bioinformatics East Jakarta Indonesia School of Engineering and Informatics Multimedia Nusantara University Department of Information System Banten Tangerang Indonesia

ISBN: (纸本)9798350368970

Acute myeloid leukemia (AML) is a rapidly occurring disorder that is characterized by the clonal expansion of myeloid progenitors in the bone marrow and the proliferation of immature myeloid cells. Absolute monocyte count (AMC) and absolute lymphocyte count (ALC) have been used in the diagnostic and prognostic predictor of AML. Interestingly, dysregulation of DNA methylation levels is also observed in AML. Data on AML patients were downloaded from the GDC TCGA Acute Myeloid Leukemia (LAML) on *** website. Data exploration andfiltering based on selected genes and racial population groups was performed. DNMT3A, IDH1, IDH2, TET2, FLT3-ITD, NPM1, and WTl are selected as the potential methylation regulation genes in AML with low number of mutation cases. Univariate analysis was done using correlation test while the multivariate analysis was done using a forward step variable selection linear regression model. This study found that the univariate analysis showed a specific methylated gene locations have a role in AML. Moreover, it is also discovered that the highest correlated gene and CpG site for white and non-white populations are different. This study uncovered that monocyte is found to be inversely proportional while the lymphocyte is directly proportional to the methylation level found in certain genes. There may be therapeutic implication from these findings such as the use of demethylating agents. © 2024 ieee.

关键词： Genes

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Using Supervised Machine Learning Algorithms For Drug Target Interaction Prediction

Using Supervised Machine Learning Algorithms For Drug Target...

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2022 Innovations in Intelligent systems and Applications conference, ASYU 2022

作者： Yildiz, Nilay Fatma Ozcan, Alper Akdeniz University Department of Computer Engineering Antalya Turkey

ISBN: (数字)9781665488945

ISBN: (纸本)9781665488945

In conjunction with the advent of bioinformatics, the evolution occurred in the field of drug discovery worldwide. In the literature numerous machine learning (ML), deep learning, and graph theory approaches have been implemented for drug discovery tasks. However, the drug-target interaction (DTI) problem for the existing and new drugs has been anticipated for a long time in drug repurposing and drug discovery areas. The proposed study predicts the drug-target interactions and to achieve this, a computational pipeline has been developed for the heterogeneous network. Firstly, this study obtains the low dimensional vector for the graphical nodes using the node2vec method. Secondly, multiple machine learning methods have been applied to predict drug-target interactions. Lastly, for the evaluation of the proposed models, we calculated the AUROC and AUPRC values and the obtained results indicate that non-linear SVM and Logistic regression performed better than other models with the AUROC, and AUPRC values of 0.8317 and 0.8260 respectively. © 2022 ieee.

关键词： bioinformatics

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Emotion Detection Mastery: EEG Signal Analysis with Balanced vs. Unbalanced Data

Emotion Detection Mastery: EEG Signal Analysis with Balanced...

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2024 International conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Menaka, Jadov Sreelekshmi, G.I. Poulose, Alwin School of Data Science Indian Institute of Science Education and Research Kerala Thiruvananthapuram695551 India University of Kerala Department of Computational Biology and Bioinformatics Kerala Thiruvananthapuram695581 India

ISBN: (纸本)9798331540067

Emotion is crucial in daily life, influencing stress levels, decision-making, relationships, and overall mental health. Recognizing emotions accurately is valuable across sectors like healthcare, education, and beyond. This paper presents a comparative study on emotion recognition, applying various deep learning models with the DEAP dataset, which includes EEG recordings from 32 participants who watched 40 short music video clips. We extracted time-domain, frequency-domain, and wavelet features from the preprocessed EEG data to capture nuanced patterns within the signals. The models assessed in this study include convolutional neural networks (CNN), long short-term memory networks (LSTM), gated recurrent units (GRU), and hybrid architectures, each trained on both balanced and unbalanced datasets. To address class imbalance, data was augmented using the Synthetic Minority Oversampling Technique (SMOTE). Results indicate that the Attention GRU model achieved slightly higher performance by utilizing its attention mechanism to capture extended dependencies within the data. Additionally, model accuracy improved by 5-9% when trained on balanced datasets, underscoring the importance of data balancing in enhancing classification outcomes. © 2024 ieee.

关键词： Convolutional neural networks

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Single-Cell RNA-Seq Data Clustering: Highlighting computational Challenges and Considerations

Single-Cell RNA-Seq Data Clustering: Highlighting Computatio...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Mohamad Nossier Sherin M. Moussa Nagwa Badr Program of Bioinformatics Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Program of Bioinformatics Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Laboratoire Interdisciplinaire de l'Université Française d'Égypte (UFEID Lab) Université Francaise d'Égypte Cairo Egypt Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Recent advancements in single-cell transcriptomics have revolutionized data generation, resulting in the production of vast amounts of single-cell RNA sequencing (scRNA-seq) data. The intricate biology of tissues and organs may be better understood through the analysis of such data, which aims to not only identify known cell types but also uncover novel ones. In this paper, we outline the standard single-cell RNA-seq data analytic process with particular focus on the critical role of unsupervised clustering, since it has a significant influence over subsequent analyses and the resulting biological insights. However, navigating the computational landscape for clustering single-cell transcriptomic data poses significant challenges. Thus, in this article, we provide a simplified exploration of key computational challenges and considerations within the realm of scRNA-seq data clustering, emphasizing the application and relevance of selected unsupervised methods in current research, while acknowledging the potential for further investigation of the biological aspects in this field. Furthermore, we present recommendations on how such challenges may be addressed. This work not only sheds light on the complexities of scRNA-seq data cluster analysis, but also provides insights into how to effectively address and surmount these challenges.

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A Closed-Loop Deep Brain Stimulation Biomedical Model of Parkinson’s Disease

A Closed-Loop Deep Brain Stimulation Biomedical Model of Par...

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ieee Symposium on computational Intelligence and bioinformatics and computational Biology (CIBCB)

作者： Timothe Petitjean Hugo Millet Mariana F. P. Araújo Renan C. Moioli Patricia A. Vargas Caetano M. Ranieri School of Mathematical And Computer Science Heriot-Watt University Edinburgh Scotland Department of Physiological Sciences Health Sciences Center Federal University of Espirito Santo (UFES) Vitoria Brazil Bioinformatics Multidisciplinary Environment Digital Metropolis Institute Federal University of Rio Grande do Norte (UFRN) Natal Brazil Institute of Geosciences and Exact Sciences Sao Paulo State University (UNESP) Rio Claro Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

In this paper, we introduce a new approach to the study of Parkinson’s Disease (PD) through the implementation of a neurorobotics model via the integration of computational neuroscience with the latest robotic technology. Using the iCub as our robot platform, we adapted a computational model of the Basal Ganglia-Thalamo-Cortical (BG-T-C) circuit to investigate the efficiency of Deep Brain Stimulation (DBS) in mitigating PD’s motor *** to our methodology is the use of closed-loop DBS, a technique that adjusts stimulation parameters in real time based on specific kinematic and neuronal biomarkers of PD severity, such as fluctuations in beta band activity and tremor movements. This dynamic approach allows for a more personalized and efficient treatment regimen compared to traditional, static open-loop systems, which cannot adapt to the patient’s changing *** findings of our study corroborate the feasibility of using a neurorobotics model to simulate the motor symptoms of PD and provide evidence that closed-loop DBS can effectively modulate these symptoms. This was achieved by reducing the power spectral density at the beta-band frequency range (8-30 Hz) of the neural activity to below threshold levels and revealing a complex relationship between the severity of the disease and the effectiveness of the treatment.

关键词： Analytical models Adaptation models computational modeling Biological system modeling Biomarkers Brain modeling Real-time systems computational efficiency Robots Diseases

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Prediction of Autism Spectrum Disorder Using AI and Machine Learning 18

Prediction of Autism Spectrum Disorder Using AI and Machine ...

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18th International conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Chauhan, Ritu Mehta, Khushi Eiad, Yafi Zuhairi, Megat Farez Center for Computational Biology and Bioinformatics Amity University Artificial Intelligence and IoT lab UP India Amity Institute of Biotechnology Amity University UP India University of Technology Sydney Faculty of Engineering & Information Technology SydneyNSW Australia Malaysian Institute of Information Technology University Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798350331011

Knowing the likelihood for a generational shift in the scientific exploration of digital data which has extensively outgrown society with tons of data. Moreover, the scientific community has evolved and outgrown themselves with Artificial Intelligence based technology to compute power and detect hidden features and information from outgrown digital databases. These advanced systems have the capability to evaluate enormous amounts of data generated with unprecedent speed, which can detect valuable insights from the data. In addition, the current study of approach focuses on the application of AI with healthcare databases to detect the hidden information from autism spectrum disorder (ASD). In this research we have various algorithmic predictions for Autism Spectrum Disorder (ASD) by combining Machine Learning with the dataset provided. The proposed approach is based on the predictive method where the data preprocessing and data evaluation models are used. The study of approach focuses on ASD with several machine Learning algorithms such as, SVM, KNN, Random Forest, Decision Tree to measure the accuracy and precision among the data and determine the hidden information and patterns. The current research scenario investigated concurrent parameters which are dependable features such as Class\ASD Traits, Sex, Jaundice scores and Q chat- 10 questions by which we have come to a conclusion which corroborated the current analysis with an accuracy Score of 74% for Random Forest, KNN: 71%, SVM: 70.6% and Decision Tree: 69.1% stating whether the individual has the Autism characteristics visible or not using various parametric analysis through data visualization and analyzing results by this approach. © 2024 ieee.

关键词： Decision trees

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SeqCCC: A Sequence-Informed Ligand-Receptor Interaction Prediction Method for Cell-Cell Communication Inference

SeqCCC: A Sequence-Informed Ligand-Receptor Interaction Pred...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Boya Ji Hong Xia Dezun Dong Shaoliang Peng College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Cell-cell communications (CCCs) mediated by ligand-receptor interactions (LRIs) play a pivotal role in coordination and function of biological systems. The two primary steps of CCC inference methods are usually filtering significant LRIs and measuring the intercellular communication strength. Biological language models have demonstrated notable progress in bioinformatics across several biological problems, inspiring by the success of language models in the field of natural language processing. Here, we proposed a computational approach for CCC inference called SeqCCC. First, SeqCCC employed two sophisticated protein language models and the protein sequences of ligands and receptors to predict potential LRIs with the help of existing LRIs. Then, SeqCCC employed permutation test to filter significantly expressed LRIs from the single-cell expression matrix based on these known and predicted LRIs. Finally, SeqCCC calculated communication strength by applying the molecular diffusion and law of mass action in chemistry, and then it improves this by eliminating non-specific CCCs using a permutation test. In the results, SeqCCC yielded an average AUC of 0.93 (mean of the five folds, with a standard deviation of 0.02). The validity and reliability of SeqCCC are further supported by a comparative analysis with common CCC inference methods, which reveals high concordance in inference results. Additionally, SeqCCC provided different visualization methods for CCC results, including Circos Plot, Heatmap Plot, Dot Plot and Heatmap for LRIs. In conclusion, SeqCCC provides a new option for CCC inference by fusing biological language models with sequence information to improve our comprehension of intercellular communications.

关键词： Proteins Heating systems Protein engineering Visualization Biological system modeling computational modeling Predictive models Reliability bioinformatics Standards

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Predicting Cancer Driver Genes: Leveraging Graph Convolutional Networks and Ensemble Learning

Predicting Cancer Driver Genes: Leveraging Graph Convolution...

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ieee Symposium on computational Intelligence and bioinformatics and computational Biology (CIBCB)

作者： Renan Soares de Andrades Mariana Recamonde-Mendoza Institute of Informatics UFRGS Bioinformatics Core HCPA Porto Alegre RS Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Cancer driver genes (CDGs) are crucial in cancer development and are key targets for treatment. This study proposes a novel approach utilizing Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), to predict CDGs by integrating protein-protein interaction (PPI) networks and multi-omics data. We optimized GCN hyperparameters across six different PPI networks from literature, which improved predictive accuracy. A consensus model was created using genes identified across all networks. We used ensemble-based methods, including majority voting and score aggregation, to identify high-probability CDGs. Comparisons showed that ensemble models outperformed individual models. Our approach identified several candidate genes, including some previously unclassified, as potential CDGs in various cancers. Notably, 33 genes were consistently predicted as CDGs across all networks, highlighting their potential importance in cancer development. This study highlights the effectiveness of GNNs for CDG prediction and the value of integrating multi-omics data and diverse PPI networks for advancing cancer genomics research and targeted therapy development.

关键词： Proteins Learning systems Accuracy Graph convolutional networks Medical treatment Predictive models Hyperparameter optimization Ensemble learning bioinformatics Cancer

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Short-term Weather Forecasting for Wind Energy Generation using A Deep Learning Technique 5

Short-term Weather Forecasting for Wind Energy Generation us...

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5th ieee International conference on Smart systems and Technologies, SST 2022

作者： Zim, Abid Hasan Zeyad, Mohammad Ahmed, S.M. Masum Hossain, Eftakhar Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh ABCD Laboratory Bangladesh Chattogram Bangladesh Heriot-Watt University Edinburgh United Kingdom University of Antwerp Faculty of Applied Engineering - Construction Antwerp Belgium

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

Inadequate or erroneous weather predictions have a great impact on wind turbine energy output. Certain weather fluctuations and interruptions affect the efficient operation of wind turbines. Short-term weather predictions that are calibrated and verified make it simple to reduce the threat presented by meteorological uncertainty. Conventional physics-based techniques for forecasting are relatively weaker than data-driven approaches since wind turbine components usually have a multipart nonlinear relationship. Compared to physical methods, non-parametric alternatives like deep learning are thought to be more stable and lead to fewer mistakes in analyst predictions. This research presents the state-of-the-art Temporal Convolutional Network (TCN), one of the deep learning methods, which is introduced as a means of enhancing the precision of short-term forecasting of weather variables. TCN is an architecture that makes use of casual convolutions and dilations in order to be adaptable for sequential data with its temporality and huge receptive fields. Seven significant meteorological parameters from the NASA POWER dataset of Chouchala Sea Beach in Chittagong, Bangladesh, over a period of four months were used in this research. The accuracy was measured using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results obtained from the suggested TCN model demonstrate that the predicted outcome is pretty accurate and consistent. © 2022 ieee.

关键词： Wind turbines

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