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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Mingzhou Liu Zheyu Wang Feng Liu School of Education Science and Technology Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Artificial Intelligence Nanjing University of Science and Technology ZiJin College Nanjing China Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Depression is a severe mental health disorder that constitutes a significant global health challenge, necessitating efficient and accurate automated diagnostic tools. Traditional approaches to automated depression assessment using multi-modal data often face three significant difficulties: small sample sizes in available datasets, high computational cost, and difficulties in effectively integrating different modalities. In this paper, we first mitigate the problem of dataset quality by utilizing an estimated imputation strategy for missing values based on a known data augmentation method. Additionally, we introduce a multi-modal depression detection model named MDDMamba, designed to address the latter two challenges by integrating features from pre-trained emotion models with the memory-efficient selective state space model named Mamba. The MDDMamba model combines features from pre-trained emotion models for both text and audio, enhancing the extraction of emotional information. The core of the MDDMamba model is the CrossMamba module, which replaces the traditional Transformer’s quadratic computational complexity with a linear complexity alternative, facilitating efficient multi-modal fusion. Experimental results on CMDC and EATD-Corpus datasets show that the MDDMamba model achieves better classification and regression performance and stronger generalization capabilities in depression detection. Notably, the MDDMamba model achieves an F1 score of 0.94 and a Pearson correlation coefficient of 0.86 on the CMDC dataset while maintaining lower memory cost, outperforming most advanced multi-modal depression detection methods across various experimental scenarios.

关键词： Emotion recognition computational modeling Biological system modeling Depression Brain modeling Feature extraction Data models Imputation Data mining computational complexity

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Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 2045-2056页

作者： Akmal, Muhammad Aizaz Hussain, Waqar Rasool, Nouman Khan, Yaser Daanial Khan, Sher Afzal Chou, Kuo-Chen Univ Management & Technol Dept Comp Sci Lahore 54000 Punjab Pakistan Univ Punjab Coll Informat Technol Natl Ctr Artificial Intelligence Lahore 54000 Punjab Pakistan Univ Karachi Int Ctr Chem & Biol Sci Dr Panjwani Ctr Mol Med & Drug Res Karachi 75270 Sindh Pakistan King Abdulaziz Univ Fac Comp & Informat Technol Rabigh Jeddah 21589 Saudi Arabia Abdul Wali Khan Univ Dept Comp Sci Mardan 23200 Khyber Pakhtunk Pakistan Gordon Life Sci Inst Boston MA USA

Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.

关键词： Proteins Predictive models Amino acids Tools bioinformatics Benchmark testing Prediction algorithms Serine Glycosylation GalNAc O-linked PseAAC

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Identifying Molecular Biomarkers for Diseases With Machine Learning Based on Integrative Omics

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2514-2525页

作者： Shi, Kai Lin, Wei Zhao, Xing-Ming Fudan Univ Sch Math Sci Shanghai 200433 Peoples R China Guilin Univ Technol Coll Sci Guilin 541004 Guangxi Peoples R China Fudan Univ Sch Math Sci Res Inst Intelligent Complex Syst MOE Frontiers Ctr Brain SciSCMSSCAM Shanghai 200433 Peoples R China Fudan Univ Ctr Computat Syst Biol ISTBI Shanghai 200433 Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence IS Shanghai 200433 Peoples R China Minist Educ Key Lab Computat Neurosci & Brain Inspired Intell Shanghai Peoples R China

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.

关键词： Feature extraction Diseases Databases bioinformatics Machine learning Genomics Molecular biomarker machine learning precision medicine disease diagnosis gene prioritization

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Asymptotic Synchronization Analysis in Drive-Response Markovian Jump Boolean Networks

IEEE Transactions on Computational Biology and Bioinformatic...

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ieee Transactions on computational biology and bioinformatics 2025年第3期22卷 1107-1117页

作者： Yong Wang Chi Huang Jie Zhong Qinyao Pan Ning Li Yaqi Wang School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China School of Mathematics Science Zhejiang Normal University Jinhua China Engineering Research Center of Intelligent Finance Ministry of Education Southwestern University of Finance and Economics Chengdu China College of Mathematics and Information Science Henan University of Economics and Law Zhengzhou China School of Engineering Qufu Normal University Rizhao China

This paper explores the asymptotic synchronization of drive-response Markovian jump Boolean networks (MJBNs) using an algebraic state space representation. For two switching signals, an augmented variable is introduced, recasting the synchronization problem into an asymptotic set stability challenge. Initially, a criterion is developed to identify all nonnegative solutions of a related equation via stochastic processes. Then, an alternative criterion based on invariant subsets and the largest singular value is proposed, where the equivalence of the proposed two criteria is demonstrated. Contrasting with deterministic BNs, where synchronization methods depend on finite power of state transition matrices, the criteria for MJBNs involve a more intricate approach. Numerical examples are included to validate the effectiveness of the proposed theoretical concepts.

关键词： Synchronization Switches Vectors Circuit stability Artificial intelligence Stability criteria Economics Asymptotic stability Training Symbols

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A Tiny Efficient U-Net with Gated Linear Attention for Medical Image Segmentation

A Tiny Efficient U-Net with Gated Linear Attention for Medic...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Sibo Ju Zhaozhen Chen Xiangwen Liao Yiqing Shen Junjun He Yanzhou Su College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science Johns Hopkins University Baltimore USA Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical image segmentation is crucial for diagnosis and treatment planning. While recent advancements in deep learning, particularly UNet variants, have improved segmentation performance, they often result in increased model complexity, which limits their real-time applicability on resource-constrained devices in clinical settings. To address this challenge, we present the Tiny Efficient U-Net (TE-UNet), a novel lightweight model balancing efficiency and accuracy. TE-UNet uses a U-shaped encoder-decoder framework with a gated linear attention mechanism to process low-level and high-level features, preserving details and reducing complexity. It employs depth-wise separable convolutions for higher-level processing, enhancing efficiency without losing performance. Additionally, skip connections improve multi-scale feature extraction and information flow. Experiments on two public datasets across different modalities demonstrate that TE-UNet outperforms ten state-of-the-art methods, maintaining a parameter size under 40KB and low computational cost. TE-UNet makes real-time segmentation more accessible for various clinical applications.

关键词： Performance evaluation Image segmentation Attention mechanisms Accuracy Logic gates Real-time systems computational efficiency Complexity theory Planning Medical diagnostic imaging

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Evaluation of the Performance of Different Numerical Methods in Cardiac Electrophysiology Simulations

Evaluation of the Performance of Different Numerical Methods...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yiwen Ding Yacong Li Luyao Chen Yaping Li Weidong Zhao Henggui Zhang Beijing Academy of Artificial Intelligence Beijing China Shandong University Jinan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

This paper presents a comprehensive evaluation of the performance of different numerical methods in cardiac electrophysiological simulations, including the Explicit Euler (EE) Method, Implicit Euler (IE) Method, Trapezoidal Rule (TR), and the Fourth-Order Runge-Kutta (RK4) Method within the framework of one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) models of the human ventricles. Simulation accuracy was investigated for varying the time step (h) for different tissue scales and complexities. It was shown that, as the dimension of the model increased, the upper limit of h for acceptable simulation accuracy (h ul ) gradually decreased. However, the difference in h ul among the methods was small. As the h or the tissue dimension increased, the EE method showed greater computational error compared to other methods. The experiment results also showed that the amplitude of the external stimulation pulse current, the size of the spatial range of tissue receiving stimuli, the spatial dimension of the tissue models, as well as the diffusion coefficient of the model, also affected simulation accuracy. The present study discusses the influence of numerical methods on the accuracy of cardiac simulations, providing insights for choosing optimal numerical methods for cardiac simulations.

关键词： Heart Analytical models Solid modeling Accuracy computational modeling Biological system modeling External stimuli Mathematical models Numerical models Complexity theory

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MilGNet: A Multi-instance Learning-based Heterogeneous Graph Network for Drug repositioning

MilGNet: A Multi-instance Learning-based Heterogeneous Graph...

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2022 ieee International conference on bioinformatics and Biomedicine, BIBM 2022

作者： Gu, Yaowen Zheng, Si Zhang, Bowen Kang, Hongyu Li, Jiao Institute of Medical Information Chinese Academy of Medical Sciences Peking Union Medical College Beijing China Tsinghua University Institute for Artificial Intelligence Department of Computer Science and Technology BNRist Beijing China Computational Medicine Beijing Co. Ltd Beijing China Beijing Institute of Technology School of Life Science Department of Biomedical Engineering Beijing China

ISBN: (纸本)9781665468190

The traditional wet-experiment-guided drug discovery is a labor-consuming and time-consuming process. Quite a few computational drug repositioning approaches have been proposed to predict potential drug-disease associations for the discovery of new indications for drugs and new therapies for diseases. Among them, heterogeneous graph neural network-based approaches can learn drug/disease topological representations on heterogeneous graphs and then give precise inferences for unconfirmed drug-disease associations. However, the existing approaches ignored the meta-paths in the drug-disease networks which could enhance the model performance and interpretability. In this study, we first proposed a multi-instance learning-based heterogeneous graph network approach for drug-disease association prediction, which is called MilGNet. Fusing with heterogeneous graph convolutional layer, the MilGNet learns meta-path-level representations for given drug-disease pairs by a novel pseudo meta-path instance generator and a bidirectional translating embedding projector. Then, an attention-based multi-scale interpretable joint predictor is assembled for precise and rational drug-disease association prediction. Comprehensive experiments have demonstrated the effectiveness of MilGNet compared to 6 advanced approaches. Meanwhile, the case study also shows the model interpretability of MilGNet by identifying high confident meta-paths. Our adopted benchmark dataset and source code are available at https://***/gu-yaowen/MilGNet.. © 2022 ieee.

关键词： Forecasting

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A minimal dynamical system and analog circuit for non-associative learning

A minimal dynamical system and analog circuit for non-associ...

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ieee conference on Decision and Control

作者： Matthew Smart Stanislav Y. Shvartsman Martin Mönnigmann Center for Computational Biology Flatiron Institute Department of Molecular Biology and the Lewis-Sigler Institute of Integrative Genomics Princeton University Ruhr University Bochum

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Learning in living organisms is typically associated with networks of neurons. The use of large numbers of adjustable units has also been a crucial factor in the continued success of artificial neural networks. In light of the complexity of both living and artificial neural networks, it is surprising to see that very simple organisms - even unicellular organisms that do not possess a nervous system - are capable of certain forms of learning. Since in these cases learning may be implemented with much simpler structures than neural networks, it is natural to ask how simple the building blocks required for basic forms of learning may be. The purpose of this study is to discuss the simplest dynamical systems that model a fundamental form of non-associative learning, habituation, and to elucidate technical implementations of such systems, which may be used to implement non-associative learning in neuromorphic computing and related applications.

关键词： Neuromorphic engineering computational modeling Neurons Artificial neural networks Learning (artificial intelligence) Organisms Complexity theory Dynamical systems Biological neural networks Periodic structures

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GenoHoption: Bridging Gene Network Graphs and Single-Cell Foundation Models

GenoHoption: Bridging Gene Network Graphs and Single-Cell Fo...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Jiabei Cheng Jiachen Li Kaiyuan Yang Hongbin Shen Ye Yuan Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiaotong University Shanghai China Key Laboratory of Biopharmaceutical Preparation and Delivery Chinese Academy of Sciences Beijing China State Key Laboratory of Biochemical Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The remarkable success of foundation models has sparked growing interest in their application to single-cell biology. Models like Geneformer and scGPT promise to serve as versatile tools in this specialized field. However, representing a cell as a sequence of genes remains an open question since the order of genes is interchangeable. Injecting the gene network graph offers gene relative positions and compact data representation but poses a dilemma: limited receptive fields without in-layer message passing or parameter explosion with message passing in each layer. To pave the way forward, we propose GenoHoption, a new computational framework for single-cell sequencing data that effortlessly combines the strengths of these foundation models with explicit relationships in gene networks. We also introduce a constraint that lightens the model by focusing on learning the predefined graph structure while ensuring further hops are deducted to expand the receptive field. Empirical studies show that our model improves by an average of 1.27% on cell-type annotation and 3.86% on perturbation prediction. Furthermore, our method significantly decreases computational overhead. Overall, GenoHoption can function as an efficient and expressive bridge, connecting existing single-cell foundation models to gene network graphs. 1

关键词： Bridges Sequential analysis Foundation models Biological system modeling Message passing computational modeling Perturbation methods Predictive models Genetics Periodic structures

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Pre-miRNA sequence prediction using convolutional neural network 6

Pre-miRNA sequence prediction using convolutional neural net...

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6th International conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022

作者： Nasr, Saifeddine Ben Messaoudi, Imen Oueslati, Afef Elloumi Lachiri, Zied University of Tunis El Manar Siti Laboratory National School of Engineers of Tunis BP 37 le Belvédère Tunis1002 Tunisia University of Carthage Higher Institute of Information Technologies and Communications Industrial Computing Department Tunisia National School of Engineers of Carthage University of Carthage Electrical Engineering Department Tunisia

ISBN: (纸本)9781665451161

MicroRNAs (miRNAs) are shown to be involved in the normal functioning of eukaryotic cells by regulating gene *** small endogenous non-coding RNAs can be extracted by very expensive manipulations. This results in a broad area of bioinformatics interest. In the field of computational biology, statistical approaches are provided to identify these structures. Several pieces of research target pre-miRNAs instead of targeting miRNAs thanks to their stem-loop structures which facilitate their detection with accuracy. Like these works, this paper presents a new technique to tackle this bioinformatics problem. We present here, a binary classification problem of pre-miRNA from non-pre-miRNA sequences. Based on a convolutional neural network (CNN), our method takes the RNA sequences of Cænorhabditis elegans as input, and identifies them as positive (pre-miRNA) or negative (non-pre-miRNA). However, data is transformed into RGB color images before being classified. For this, we propose using the EIIP method to encode the genomic sequences into 1D signals. The latter data is expressed on the time-frequency domain using the continuous wavelet transform and the complex Morlet as mother wavelet. Our classification model is simple, rapid and efficient since it reaches 100% of accuracy within few minutes when images are resized and transformed in the HSV colorimetric plan. Our method surpasses the approaches HHMMiR and Triplet SVM whose accuracy rate does not exceed 90.9%. © 2022 ieee.

关键词： bioinformatics

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