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43rd Annual International conference of the ieee-Engineering-in-Medicine-and-biology-Society (ieee EMBC)

作者： Mallol-Ragolta, Adria Liu, Shuo Schuller, Bjoern W. Univ Augsburg Chair Embedded Intelligence Hlth Care & Wellbeing Augsburg Germany Imperial Coll London Grp Language Audio & Mus London England

ISBN: (纸本)9781728111797

Face masks alter the speakers' voice, as their intrinsic properties provide them with acoustic absorption capabilities. Hence, face masks act as filters to the human voice. This work focuses on the automatic detection of face masks from speech signals, emphasising on a previous work claiming that face masks attenuate frequencies above 1 kHz. We compare a paralinguistics-based and a spectrograms-based approach for the task at hand. While the former extracts paralinguistic features from filtered versions of the original speech samples, the latter exploits the spectrogram representations of the speech samples containing specific ranges of frequencies. The machine learning techniques investigated for the paralinguistics-based approach include Support Vector Machines (SVM), and a Multi-Layer Perceptron (MLP). For the spectrograms-based approach, we use a Convolutional Neural Network (CNN). Our experiments are conducted on the Mask Augsburg Speech Corpus (MASC), released for the Interspeech 2020 computational Paralinguistics Challenge (COMPARE). The best performances on the test set from the paralinguistic analysis are obtained using the high-pass filtered versions of the original speech samples. Nonetheless, the highest Unweighted Average Recall (UAR) on the test set is obtained when exploiting the spectrograms with frequency content below 1 kHz.

关键词： Support vector machines Training Absorption Transfer learning Surgery Feature extraction Convolutional neural networks

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DeepCRC: Precision Colorectal Cancer Diagnosis Using Deep Learning and Metagenomic Biomarkers

DeepCRC: Precision Colorectal Cancer Diagnosis Using Deep Le...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Han Wang Yunhe Li Yannuo Feng Kexin Shen Chang Lu Zhongshi Xie School of Information Science and Technology Institute of Computational Biology Northeast Normal University Changchun China China-Japan Union Hospital of Jilin University Changchun China School of Psychology Northeast Normal University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths globally, underscoring the critical need for early detection. Microbial biomarker detection offers significant advantages in revealing the physiological and pathological mechanisms of CRC, as well as in early screening. It provides not only deep insights into disease progression but also enables the possibility of non-invasive early diagnosis. The utilization of metagenomic shotgun sequencing (MGS) technology and the bioBakery workflow to analyze gut microbiome samples from six independent cohorts (n = 1291), including 645 CRC patients and 646 healthy controls (HC). Metagenomic analysis revealed a significant reduction in microbial diversity and identified 117 microbial species with altered abundance in CRC patients (p < 0.05). The DeepCRC model, incorporating key biomarkers and clinical metadata, achieved 97.70% accuracy and an AUC of 0.982 on an independent test set. SHAP analysis indicated that microbial species such as Ruminococcaceae unclassified SGB15260 and Clostridium leptum impact CRC significantly. These bacteria are associated with CRC through mechanisms involving inflammation and gut barrier dysfunction. The superior performance of the DeepCRC diagnostic model is expected to advance the development of non-invasive CRC screening methods. Source code is available at https://***/NENUBioCompute/DeepCRC.

关键词： Deep learning Metagenomics Analytical models Sequential analysis Accuracy Biological system modeling Source coding Precision medicine Colorectal cancer Physiology

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Multimodal Medical Image Feature Representation and Fusion for AD Early Diagnosis

Multimodal Medical Image Feature Representation and Fusion f...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Jing Qu Ying Qu Xueliang Bai Lanxin Xu Han Wang Guixia Liu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Science North China University of Science and Technology Tangshan China School of Information Science and Technology Institute of Computational Biology Northeast Normal University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Alzheimer's disease (AD) is progressive and gets worse with time. Early detection is an effective treatment for the disease, which can timely implement effective interventions. The multi-modal medical image contains more comprehensive and abundant information about the disease, which can reflect the different aspects of AD and reduce the potential bias in single-modal diagnosis. However, an effective and reliable solution is still lacking, which can integrate multimodal neuroimaging effectively for the precision diagnosis and treatment of AD. In this work, we proposed a feature representation and fusion model of MRI and PET scans based on a multiscale convolution network and cross-attention for AD early diagnosis, named camAD. Compared with the existing models, camAD performs better with fewer pre-processing steps. Using the Alzheimer’s disease neuroimaging initiative (ADNI) datasets, we demonstrated that integrating multi-modality data outperforms single-modality models in accuracy, specificity, sensitivity, AUC, and F1 scores. Our models exhibited significantly good accuracy and generality in four classification tasks, which provided a promising way to understand the underlying mechanisms of the disorder's progression.

关键词： Neuroimaging Accuracy Sensitivity Convolution Magnetic resonance imaging Feature extraction Data models Reliability Alzheimer's disease Medical diagnostic imaging

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Modeling UGT2B7 and NR1I3 genes through multilayer network to highlight hidden link with taxane neurotoxicity

Modeling UGT2B7 and NR1I3 genes through multilayer network t...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Giuseppe Agapito Marianna Milano Francesca Scionti Nicoletta Staropoli Pierfrancesco Tassone Pierosandro Tagliaferri Mario Cannataro Mariamena Arbitrio Dept. of Law Economics and Social Sciences University Magna Græcia Catanzaro Italy Dept. of Experimental and Clinical Medicine University Magna Græcia Catanzaro Italy Dept. of Medical and Surgical Sciences University Magna Græcia Catanzaro Italy Institute for Biomedical Research and Innovation National Research Council Catanzaro Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Breast cancer (BC) remains a leading global malignancy, with taxane-based treatment (TBT), including paclitaxel and docetaxel, significantly enhancing outcomes across early-stage, locally advanced, and metastatic BC. Despite its efficacy, TBT can cause severe taxane-related peripheral neurotoxicity (TrPN), which limits dosing and affects patient quality of life. TrPN is cumulative, primarily sensory, and unpredictable, with genetic predisposition playing a key role in its development. Previous pharmacogenomic (PGx) study identified five single nucleotide polymorphisms (SNPs) in the NR1I3 and UGT2B7 genes, linked to protection against severe TrPN in BC patients. ROC analysis validated these findings in an independent BC dataset. Neuroprotective effects were in patients homozygous for allele variants 2 with ultrametabolizer phenotype, which accelerates taxane inactivation and reduces treatment efficacy, potentially worsening prognosis. NR1I3 and UGT2B7 genes could serve as predictive biomarkers for TrPN and taxane bioavailability. Further network and pathway enrichment analyses (PEA) provided insights into the molecular pathways involved in TrPN and BC progression. Network analysis, a powerful tool in computational biology, integrates genomic and proteomic data to uncover drug-response mechanisms. This approach advances personalized medicine by identifying key genes and biomarkers for predicting treatment response and adverse effects, offering new therapeutic opportunities.

关键词： Correlation Phenotypes Precision medicine Proteomics Biomarkers Optical fiber networks Metabolism bioinformatics Protection Prognostics and health management

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Counterfactual Inference-based Data Augmentation for Drug-side effect Associations Prediction

Counterfactual Inference-based Data Augmentation for Drug-si...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Wenjie Yao Weizhong Zhao Xiaowei Xu Xingpeng Jiang Xianjun Shen Tingting He Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan China School of Computer Central China Normal University Wuhan China National Language Resources Monitoring and Research Center for Network Media Central China Normal University Wuhan China Department of Information Science University of Arkansas at Little Rock Little Rock AR USA

Detecting drug side effects is crucial in development of drugs. As publicly available biomedical data expands, researchers have devised numerous computational methods for predicting drug-side effect associations (DSAs). Among these, network-based approaches have gained significant attention in the biomedical field. However, the challenge of data scarcity poses a significant hurdle for existing DSAs prediction models. While various data augmentation methods have been created to solve the proble, most rely on random alterations to the original networks, neglecting the causality of DSAs’ existence, thus impacting the predictive performance negatively. In this paper, we introduce a counterfactual inference-based data augmentation method to enhance performance. First,a heterogeneous information network (HIN) is construct by integrating multiple biomedical data sources. We employ community detection on the HIN to preform a counterfactual inference-based method, deriving augmented links and an augmented HIN. Subsequently, we apply a meta-path-based graph neural network to obtain high-quality representations of drugs and side effects, enabling the prediction of DSAs. Our comprehensive experiments confirm the effectiveness of this counterfactual inference-based data augmentation for DSAs prediction.

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Techno Economic Feasibility Analysis of Grid-Connected Microgrid by Using Solar PV for Residential Usage 9

Techno Economic Feasibility Analysis of Grid-Connected Micro...

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

作者： Mahmud, Dewan Mahnaaz Hasan, Sayeed Ahmed, S.M. Masum Zeyad, Mohammad Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh Energy and Technology Research Division Chattogram Bangladesh Erasmus+ Joint MSC In Smart Cities and Communities Faculty of Engineering Mons Belgium Erasmus+ Joint MSC In Smart Cities and Communities Department of Thermal Engineering Bilbao Spain

ISBN: (纸本)9781665432405

Renewable energy systems (RES) are becoming more prominent because of the depletion of natural resources. Optimum sizing of the hybrid RES is necessary to ensure that renewable energy sources penetrate. Improper sizing may result in higher initiating costs or an inability to meet load demand under various weather conditions. By analyzing with Hybrid Optimization Multiple Energy Resources (HOMER pro), this study shows the ideal hybrid energy system that provides the lowest cost of energy production, emissions, and so on. This study provides a grid-connected hybrid power system model based on metrological data collected from a residential building in Mohammadpur, Dhaka, Bangladesh. Solar energy is used as a sustainable energy source thus the cost of the system was decreased, which is also environmentally friendly. This renewable energy also helped to minimize the levelized cost of energy (COE). The conclusion estimates operational costs, production costs, net present cost, and energy cost for the two combinations evaluated for the proposed microgrid system. According to the simulation results, delivering power to the load via a microgrid would be a viable and cost-effective solution. © 2021 ieee.

关键词： Battery storage

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Out-of-Equilibrium Fluctuations Drive Correlations Between Enzyme and Metabolic Product Levels

Out-of-Equilibrium Fluctuations Drive Correlations Between E...

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ieee conference on Decision and Control

作者： A. Borri P. Palumbo A. Singh Istituto di Analisi dei Sistemi e Informatica “A. Ruberti” Italian National Research Council (IASI-CNR) Roma Italy Department of Biotechnologies and Biosciences University of Milano-Bicocca Milan Italy Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA

Enzyme-driven catalysis of a substrate into a product forms the fundamental backbone of cellular metabolic pathways. In the deterministic formulation of such a reaction scheme, the equilibrium level of the metabolic product is independent of the steady-state enzyme, so that any perturbation in enzyme levels causes a transient change in metabolic product levels that perfectly adapts to the original enzyme-independent steady state. In this work, we consider a stochastic formulation of the problem, where enzyme levels constantly fluctuate due to the inherently noisy gene expression process as well as to the extrinsic noise in substrate availability. Our results show that such out-of-equilibrium fluctuations can result in positive (or negative) enzyme-product and substrate-product correlations, whose behavior qualitatively and quantitatively changes in different scenarios characterized by perturbations of nominal parameters and variable noise levels.

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Artificial intelligence in Biological Activity Prediction 13th

Artificial Intelligence in Biological Activity Prediction

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13th International conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Correia, Joao Resende, Tiago Baptista, Delora Rocha, Miguel Univ Minho CEB Ctr Biol Engn Campus Gualtar Braga Portugal

ISBN: (纸本)9783030238735;9783030238728

Artificial intelligence has become an indispensable resource in chemoinformatics. Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. These approaches enable the automation of compound discovery to find biologically active molecules with important properties. Here, we present a review of some of the main machine learning studies in biological activity prediction of compounds, in particular for sweetness prediction. We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks.

关键词： Machine learning Deep learning Biological activity prediction Sweetness prediction Compound featurization

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ieee BIBM 2019 Special Issue

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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ieee TRANSACTIONS ON NANOBIOSCIENCE 2020年第3期19卷 504-505页

作者： Hu, Xiaohua Nortel Res Ctr New York NY USA Verizon Lab Waltham MA USA DMW Software Silicon Valley CA USA Drexel Univ Philadelphia PA 19104 USA Drexel Univ Data Min & Bioinformat Lab Coll Comp & Informat Philadelphia PA 19104 USA

bioinformatics and biomedicine research is fundamental to our understanding of complex biological systems, impacting the science and technology of fields ranging from agricultural and environmental sciences to pharmaceutical and medical sciences. This type of research requires close collaboration among multidisciplinary teams of researchers in computer science, statistics, physics, engineering, life sciences and medical sciences, and their interfaces. The ieee International conference on bioinformatics and Biomedicine aims to provide an open and interactive forum to catalyze the cross-fertilization of ideas from these disciplines and to bridge our knowledge gaps.

关键词： Special issues and sections Meetings bioinformatics Biological systems Genomics computational systems biology Diseases Proteins Vaccines

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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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