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MLRR-ATV: A Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization for scRNA-seq Data Clustering

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ieee/ACM Transactions on computational biology and bioinformatics 2024年 PP卷 1-12页

作者： Wang, Gao-Fei Wang, Juan Yuan, Shasha Zheng, Chun-Hou Liu, Jin-Xing School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing Haidian District China School of Computer Science Qufu Normal University Rizhao Shandong China

Since genomics was proposed, the exploration of genes has been the focus of research. The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to explore gene expression at the single-cell level. Due to the limitations of sequencing technology, the data contains a lot of noise. At the same time, it also has the characteristics of highdimensional and sparse. Clustering is a common method of analyzing scRNA-seq data. This paper proposes a novel singlecell clustering method called Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization (MLRR-ATV). The Adaptive Total-Variation (ATV) regularization is introduced into Low-Rank Representation (LRR) model to reduce the influence of noise through gradient learning. Then, the linear and nonlinear manifold structures in the data are learned through Euclidean distance and cosine similarity, and more valuable information is retained. Because the model is non-convex, we use the Alternating Direction Method of Multipliers (ADMM) to optimize the model. We tested the performance of the MLRRATV model on eight real scRNA-seq datasets and selected nine state-of-the-art methods as comparison methods. The experimental results show that the performance of the MLRRATV model is better than the other nine methods. ieee

关键词： RNA

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MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detection

MedDet: Generative Adversarial Distillation for Efficient Ce...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Zeyu Zhang Nengmin Yi Shengbo Tan Ying Cai Yi Yang Lei Xu Qingtai Li Zhang Yi Daji Ergu Yang Zhao Key Laboratory of Electronic Information Engineering Southwest Minzu University The Australian National University West China Hospital Orthopedic Research Institute Machine Intelligence Laboratory Sichuan University La Trobe University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Cervical disc herniation (CDH) is a prevalent musculoskeletal disorder that significantly impacts health and requires labor-intensive analysis from experts. Despite advancements in automated detection of medical imaging, two significant challenges hinder the real-world application of these methods. First, the computational complexity and resource demands present a significant gap for real-time application. Second, noise in MRI reduces the effectiveness of existing methods by distorting feature extraction. To address these challenges, we propose three key contributions: Firstly, we introduced MedDet, which leverages the multi-teacher single-student knowledge distillation for model compression and efficiency, meanwhile integrating generative adversarial training to enhance performance. Additionally, we customize the second-order nmODE to improve the model’s resistance to noise in MRI. Lastly, we conducted comprehensive experiments on the CDH-1848 dataset, achieving up to a 5% improvement in mAP compared to previous methods. Our approach also delivers over 5 times faster inference speed, with approximately 67.8% reduction in parameters and 36.9% reduction in FLOPs compared to the teacher model. These advancements significantly enhance the performance and efficiency of automated CDH detection, demonstrating promising potential for future application in clinical practice.

关键词： Training Knowledge engineering Musculoskeletal system Magnetic resonance imaging Noise Noise reduction Feature extraction Real-time systems Resilience Immune system

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Parallel interacting multiview learning: An application to prediction of protein sub-nuclear location

Parallel interacting multiview learning: An application to p...

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ieee EMBS International conference on Information Technology Applications in Biomedicine (ITAB)

作者： Olcay Kursun Huseyin Seker Fikret Gürgen Nizamettin Aydin Oleg V. Favorov C. Okan Sakar Department of Computer Engineering Istanbul University 34320 Turkey Department of Computer Engineering Istanbul University Istanbul Turkey Istanbul Universitesi Fatih Istanbul TR Bio-Health Informatics Research Group Centre for Computational Intelligence Department of Informatics Technology De Montfort University Leicester UK Department of Computer Engineering Boğaziçi University Istanbul Turkey Department of Computer Engineering Yildiz Technical University Istanbul Turkey Department of Biomedical Engineering North Carolina State University Chapel Hill NC USA

In some machine learning problems, the dataset has multiple views which may be obtained using different sensors or applying different sampling techniques. These views may have sufficient or partial information about the target concept. In this paper, a method that we called parallel interacting multiview learning (PIML) is proposed in which the views interact during the training process using the predictions of each other together with their original features. This way, the views are expected to strengthen the prediction accuracies of the other views feeding their predictions to the others even during the training process. This technique avoids the way of simply merging features of all views and reaches higher accuracy than its counterparts that do not interact during learning but only combine their predictions after the learning process. PIML is demonstrated on a real bioinformatics dataset for predicting protein sub-nuclear locations.

关键词： Machine learning Merging bioinformatics Informatics Sampling methods Accuracy Pattern recognition Production Protein sequence Speckle

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A Tensorial Approach to Access Cognitive Workload related to Mental Arithmetic from EEG Functional Connectivity Estimates

A Tensorial Approach to Access Cognitive Workload related to...

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Annual International conference of the ieee Engineering in Medicine and biology Society

作者： S. I. Dimitriadis Yu Sun K. Kwok N. A. Laskaris A. Bezerianos Artificial Intelligence and Information Analysis Laboratory Department of Informatics Aristotle

ISBN: (纸本)9781457702150

The association of functional connectivity patterns with particular cognitive tasks has long been a topic of interest in neuroscience, e.g., studies of functional connectivity have demonstrated its potential use for decoding various brain states. However, the high-dimensionality of the pairwise functional connectivity limits its usefulness in some real-time applications. In the present study, the methodology of tensor subspace analysis (TSA) is used to reduce the initial high-dimensionality of the pairwise coupling in the original functional connectivity network to a space of condensed descriptive power, which would significantly decrease the computational cost and facilitate the differentiation of brain states. We assess the feasibility of the proposed method on EEG recordings when the subject was performing mental arithmetic task which differ only in the difficulty level (easy: 1-digit addition v.s. 3-digit additions). Two different cortical connective networks were detected, and by comparing the functional connectivity networks in different work states, it was found that the task-difficulty is best reflected in the connectivity structure of sub-graphs extending over parieto-occipital sites. Incorporating this data-driven information within original TSA methodology, we succeeded in predicting the difficulty level from connectivity patterns in an efficient way that can be implemented so as to work in real-time.

关键词： Difficulty Level Mental arithmetic Tensor Brain Connectivity Cognitive tasks real-time application program Network Workload

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Interpreting Lung Cancer Health Disparity between African American Males and European American Males

Interpreting Lung Cancer Health Disparity between African Am...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Masrur Sobhan Md Mezbahul Islam Ananda Mohan Mondal Knight Foundation School of Computing and Information Sciences Florida International University Miami USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Lung cancer remains a predominant cause of cancer-related deaths, with notable disparities in incidence and outcomes across racial and gender groups. This study addresses these disparities by developing a computational framework leveraging explainable artificial intelligence (XAI) to identify both patient- and cohort-specific biomarker genes in lung cancer. Specifically, we focus on two lung cancer subtypes, Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC), examining distinct racial and sex-specific cohorts: African American males (AAMs) and European American males (EAMs). This study innovatively structures classification tasks based on disease conditions rather than racial labels to avoid race-specific imbalance. We constructed four classification tasks-one three-class problem (LUAD-LUSC-HEALTHY) and three two-class problems (LUAD-LUSC, LUAD-HEALTHY, LUSC-HEALTHY)- to interpret the disease behavior of the patients in terms of genes and pathways. This methodology allows a LUAD or LUSC patient to be analyzed via multiple classifications, yielding robust disparity information for every patient. This preliminary work reports the disparity information for LUAD *** Transcriptome data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects, we processed samples for LUAD, LUSC, and HEALTHY cohorts. We applied machine learning models, including convolutional neural network (CNN), logistic regression (LR), naïve Bayesian classifier (NB), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) for the classification. The SHapley Additive exPlanation (SHAP)-based interpretation of the best performing classification model uncovered cohort-specific genes and pathways related to health disparities between LUAD-AAM and LUAD-EAM cohorts.

关键词： Support vector machines Logistic regression Squamous cell carcinoma Lungs Lung cancer Europe Genomics Convolutional neural networks bioinformatics Random forests

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Probabilistic Framework for Reliability Analysis of Information-Theoretic CAD Systems in Mammography

Probabilistic Framework for Reliability Analysis of Informat...

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

作者： Piotr A. Habas Jacek M. Zurada Adel S. Elmaghraby Georgia D. Tourassi Computational Intelligence Laboratory University of Louisville Louisville KY USA Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Department of Computer Engineering and Computer Science University of Louisville Louisville KY USA Duke Advanced Imaging Laboratories Department of Radiology Duke University Medical Center Durham NC USA

The purpose of this study is to develop and evaluate a probabilistic framework for reliability analysis of information-theoretic computer-assisted detection (IT-CAD) systems in mammography. The study builds upon our previous work on a feature-based reliability analysis technique tailored to traditional CAD systems developed with a supervised learning scheme. The present study proposes a probabilistic framework to facilitate application of the reliability analysis technique for knowledge-based CAD systems that are not feature-based. The study was based on an information-theoretic CAD system developed for detection of masses in screening mammograms from the Digital Database for Screening Mammography (DDSM). The experimental results reveal that the query-specific reliability estimate provided by the proposed probabilistic framework is an accurate predictor of CAD performance for the query case. It can also be successfully applied as a base for stratification of CAD predictions into clinically meaningful reliability groups (i.e., HIGH, MEDIUM, and LOW). Based on a leave-one-out sampling scheme and ROC analysis, the study demonstrated that the diagnostic performance of the IT-CAD is significantly higher for cases with HIGH reliability (Az=0.92plusmn0.03) than for those stratified as MEDIUM (Az=0.84plusmn0.02) or LOW reliability predictions (Az=0.78plusmn0.02)

关键词： Information analysis Mammography Design automation Laboratories Cities and towns USA Councils Supervised learning Application software Spatial databases Delta-sigma modulation

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Assessments of variability in cortical and subcortical measurements and within-network connectivity of the brain using test-retest data

Assessments of variability in cortical and subcortical measu...

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

作者： Hosna Tavakoli Reza Rostami Mohammad-Reza Nazem-Zadeh Computational and Artificial Intelligence Department Institute of Cognitive Science Studies Tehran Iran Department of Psychology University of Tehran Iran Research Center for Molecular and Cellular Imaging Advanced Medical Technologies and Equipment Institute (AMTEI) Tehran University of Medical Sciences Tehran Iran Department of Neuroscience Monash University Melbourne VIC Australia

The evaluation and diagnosis of structural changes in brain caused by disease or treatment over time has become one of the important applications of medical imaging methods, in particular MRI, and it is growing. It is critical to evaluate the reliability of the changes in measurements observed in an individual patient for any clinical decision-making. In this paper, we calculated the repeatability coefficient (RC) as a measure of uncertainty for MRI measurements of subcortical volumes and cortical thickness, and within-network connectivity using test-retest data of 20 healthy subjects. We also evaluated changes in 13 patients who received 20 sessions of transcranial magnetic stimulation as a treatment. The most reliable measure seems to be in the thickness of the left occipital with RC% of 3.5 and the least reliable measure is the brain connectivity within visual network using Yeo’s atlas with RC% of 29.4. The most sensitive measure to the percentage of true changes in treated patients is the connectivity within subcortical network of AAL with 76.9%.Clinical Relevance— The results of this study can be useful for evaluating changes in the gray matter structures or functional connectivity of the brain due to a neurological disease such as Alzheimer's or Parkinson's. Also, the obtained results can be used to evaluate the changes caused by any intervention or treatment that may have any positive or negative effect on the brain.

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A Cloud-unaware Programming Model for Easy Development of Composite Services

A Cloud-unaware Programming Model for Easy Development of Co...

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ieee International conference on Cloud Computing Technology and Science (CloudCom)

作者： Enric Tejedor Jorge Ejarque Francesc Lordan Roger Rafanell Javier Alvarez Daniele Lezzi Raul Sirvent Rosa M. Badia Universitat Poliltècnica de Catalunya Barcelona Spain Barcelona Supercomputing Center Barcelona Spain Artificial Intelligence Research Institute (IIIA) Spanish National Research Council Spain

Cloud computing is inherently service-oriented: cloud applications are delivered to consumers as services via the Internet. Therefore, these applications can potentially benefit from the Service-Oriented Architecture (SOA) principles: they can be programmed as added-value services composed by pre-existing ones, thus favouring code reuse. However, new programming models are required to simplify their development, along with systems that are capable of orchestrating the execution of the resulting SaaS in the Cloud. In that regard, this paper presents Service Super scalar (Servicess), an alternative to existing PaaS which provides a programming model and execution runtime to ease the development and execution of service-based applications in clouds. Servicess is a task-based model: the user is only required to select the tasks, which can be services or regular methods, to be spawned asynchronously. The application, a composite service, is programmed in a totally sequential way and no API call must be included in the code. The runtime is in charge of automatically orchestrating the execution of the tasks in the Cloud, as well as of elastically deploying new virtual resources depending on the load. After describing the main characteristics of the programming model and the runtime, we evaluate the productivity of Servicess and show how it offers a good trade-off between programmability and runtime performance.

关键词： Runtime Programming Java Cloud computing computational modeling bioinformatics Service oriented architecture

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An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions

An Evaluation of Hybrid Deep Learning Models for Classifying...

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

作者： Zilu Wang Ian Daly Junhua Li School of Computer Science and Electronic Engineering University of Essex Colchester UK Laboratory for Brain-Bionic Intelligence and Computational Neuroscience Wuyi University Jiangmen China

Brain-computer Interfaces (BCIs) interpret electroencephalography (EEG) signals and translate them into control commands for operating external devices. The motor imagery (MI) paradigm is popular in this context. Recent research has demonstrated that deep learning models, such as convolutional neural network (CNN) and long short-term memory (LSTM), are successful in a wide range of classification applications. This is because CNN has the property of spatial invariance, and LSTM can capture temporal associations among features. A combination of CNN and LSTM could enhance the classification performance of EEG signals due to the complementation of their strengths. Such a combination has been applied to MI classification based on EEG. However, most studies focused on either the upper limbs or treated both lower limbs as a single class, with only limited research performed on separate lower limbs. We, therefore, explored hybrid models (different combinations of CNN and LSTM) and evaluated them in the case of individual lower limbs. In addition, we classified multiple actions: MI, real movements and movement observations using four typical hybrid models and aimed to identify which model was the most suitable. The comparison results demonstrated that no model was significantly better than the others in terms of classification accuracy, but all of them were better than the chance level. Our study informs the possibility of the use of multiple actions in BCI systems and provides useful information for further research into the classification of separate lower limb actions.

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ieee Access Special Section Editorial: Scalable Deep Learning for Big Data

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ieee ACCESS 2020年 8卷 216617-216622页

作者： Han, Liangxiu Zhang, Daoqiang Rana, Omer Pan, Yi Jabbar, Sohail Yousif, Mazin Aloqaily, Moayad Manchester Metropolitan Univ Dept Comp & Math Manchester M1 5GD Lancs England Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF24 3AA Wales Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA Univ Faisalabad Dept Computat Sci Faisalabad 38000 Pakistan DXC Technol Tysons VA 22102 USA Al Ain Univ Fac Engn Al Ain U Arab Emirates

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