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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 ieee.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Orienting Conflicted Graph Edges Using Genetic Algorithms to Discover Pathways in Protein-Protein Interaction Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1970-1985页

作者： Iqbal, Shahid Halim, Zahid Monash Univ Fac Informat Technol Data Sci & AI Grp Melbourne Vic 3800 Australia Ghulam Ishaq Khan Inst Engn Sci & Technol Machine Intelligence Res Grp MInG Fac Comp Sci & Engn Topi 23460 Khyber Pakhtunk Pakistan

Advanced computational techniques of the current era help to identify proteins from the complex biological network that interact with each other and with the cell's environment. Biological pathways are a chain of molecular actions that leads to a new molecular product creation or alters the cellular state. These pathways are helpful in the predication of many real-world issues. Rebuilding these pathways is a challenging task due to the fact that protein interactions are undirected, whereas pathways are directed. To discover these pathways in protein-protein interaction data from specified source and target, it is essential to orient protein interactions. Unfortunately, the edge orientation problem is NP-hard, which makes it challenging to develop effective algorithms. This work rebuilds biologically important pathways in a weighted network of protein interactions of yeast species. The proposed algorithm, pseudo-guided multi-objective genetic algorithm (PGMOGA) rebuilds pathways by assigning orientation to the edges of the weighted network. Extending the past research, mathematical modeling of single-objective and multi-objective functions is performed. The PGMOGA is compared with four state-of-the-art approaches, namely, random orientation plus local search (ROLS), single-objective genetic algorithm (SOGA), multi-objective genetic algorithm (MOGA), and multi random search (MRS). The comparison is based on three general and four path specific metrics. Results show that the current proposal performs better.

关键词： Proteins bioinformatics biology Genetic algorithms Task analysis Organisms Genomics Evolutionary algorithm gene network protein pathway identification

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Spiking Approximations of the MaxPooling Operation in Deep SNNs

Spiking Approximations of the MaxPooling Operation in Deep S...

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ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on computational intelligence (ieee WCCI) / International Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Gaurav, Ramashish Tripp, Bryan Narayan, Apurva Univ Waterloo Syst Design Engn Waterloo ON Canada Univ British Columbia Dept Comp Sci Vancouver BC Canada

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Spiking Neural Networks (SNNs) are an emerging domain of biologically inspired neural networks that have shown promise for low-power AI. A number of methods exist for building deep SNNs, with Artificial Neural Network (ANN)-to-SNN conversion being highly successful. MaxPooling layers in Convolutional Neural Networks (CNNs) are an integral component to downsample the intermediate feature maps and introduce translational invariance, but the absence of their hardwarefriendly spiking equivalents limits such CNNs' conversion to deep SNNs. In this paper, we present two hardware-friendly methods to implement Max-Pooling in deep SNNs, thus facilitating easy conversion of CNNs with MaxPooling layers to SNNs. In a first, we also execute SNNs with spiking-MaxPooling layers on Intel's Loihi neuromorphic hardware (with MNIST, FMNIST, & CIFAR10 dataset);thus, showing the feasibility of our approach.

关键词： Energy consumption Neuromorphics Buildings Hardware biology Convolutional neural networks Artificial intelligence

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A fast and accurate learning-based decoding algorithm for the classification of cardiovascular and respiratory challenges using intraneural electrodes in the pig vagus nerve 44

A fast and accurate learning-based decoding algorithm for th...

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44th Annual International conference of the ieee-Engineering-in-Medicine-and-biology-Society (EMBC)

作者： Pollina, Leonardo Vallone, Fabio Ottaviani, Matteo M. Strauss, Ivo Recchia, Fabio A. Moccia, Sara Micera, Silvestro Scuola Super Sant Anna BioRobot Inst Pisa Italy Scuola Super Sant Anna Dept Excellence Robot & Artificial Intelligence Pisa Italy Scuola Super Sant Anna Inst Life Sci Pisa Italy Fdn Toscana Gabriele Monasterio Pisa Italy Temple Univ Cardiovasc Res Ctr Lewis Katz Sch Med Philadelphia PA USA Ecole Polytech Fed Lausanne Ctr Neuroprosthet Translat Neural Engn Lausanne Switzerland Ecole Polytech Fed Lausanne Inst Bioengn Lausanne Switzerland

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Bioelectronic medicine is a new approach for developing closed-loop neuromodulation protocols on the peripheral nervous system (PNS) to treat a wide range of disorders currently treated with pharmacological approaches. Algorithms need to have low computational cost in order to acquire, process and model data for the modulation of the PNS in real time. Here, we present a fast learning-based decoding algorithm for the classification of cardiovascular and respiratory functional alterations (i.e., challenges) by using neural signals recorded from intraneural electrodes implanted in the vagus nerve of 5 pigs. Our algorithm relies on 9 handcrafted features, extracted following signal temporal windowing, and a multi-layer perceptron (MLP) for feature classification. We achieved fast and accurate classification of the challenges, with a computational time for feature extraction and prediction lower than 1.5 ms. The MLP achieved a balanced accuracy higher than 80% for all recordings. Our algorithm could represent a step towards the development of a closed-loop system based on a single intraneural interface with both the potential of real time classification and selective modulation of the PNS.

关键词： Peripheral nervous system Electrodes Biological system modeling Modulation Feature extraction Prediction algorithms Real-time systems

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Aberrant Static and Dynamic Functional Brain Network in Depression Based on EEG Source Localization

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第3期20卷 1876-1889页

作者： Lin, Xiangbin Kong, Weizhuang Li, Jianxiu Shao, Xuexiao Jiang, Changting Yu, Ruilan Li, Xiaowei Hu, Bin Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China Shandong Acad Intelligent Comp Technol Qingdao 266590 Shandong Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci CAS Ctr Excellence Brain Sci & Intelligence Techno Shanghai 200234 Peoples R China Lanzhou Univ Joint Res Ctr Cognit Neurosensor Technol Lanzhou 730000 Gansu Peoples R China Chinese Acad Sci Inst Semicond Lanzhou 730000 Gansu Peoples R China Lanzhou Univ Minist Educ Engn Res Ctr Open Source Software & Real Time Syst Lanzhou 730000 Gansu Peoples R China

Objective. Depression is accompanied by abnormalities in large-scale functional brain networks. This paper combined static and dynamic methods to analyze the abnormal topology and changes of functional connectivity network (FCN) of depression. Methods. We collected resting-state EEG recordings from 27 depressed subjects and 28 normal subjects, then obtained 68 regions of interests (ROIs) by source localization. We took ROIs as the nodes and correlations as the edges to build FCNs and analyzed static network based on graph theory. We used a sliding window method followed by k-means clustering, states analyses and trend analysis of network metrics over time to study dynamic connectivity. Results. The clustering coefficient (CC) and local efficiency in depression were increased, the characteristic path length and global efficiency were decreased, and local metrics had different manifestations in different resting state networks (RSNs);Depression had reduced connectivity in most RSNs, but increased connectivity in the default mode network, and there was a decoupling phenomenon between different RSNs;Depressed patients spent more time in sparsely connected states, their FCN's flexibility was less than normal subjects;The trend of CC over time was opposite between two groups. Most metrics in normal showed a relatively stronger correlation with time. Significance. Our research may provide a deeper understanding of neurophysiological mechanisms of depression and new biomarkers for clinical diagnosis of depression.

关键词： Functional connectivity graph theory K-means clustering source localization sliding window method

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EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and computational intelligence Approaches and Their Applications

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1645-1666页

作者： Gu, Xiaotong Cao, Zehong Jolfaei, Alireza Xu, Peng Wu, Dongrui Jung, Tzyy-Ping Lin, Chin-Teng Univ Tasmania Hobart Tas 7005 Australia Macquarie Univ N Ryde NSW 2109 Australia Univ Elect Sci & Technol China Chengdu 610054 Peoples R China Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China Univ Calif San Diego La Jolla CA 92093 USA Univ Technol Sydney Sydney NSW 2007 Australia

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.

关键词： Electroencephalography Brain Deep learning Sensors Monitoring Functional magnetic resonance imaging Entertainment industry

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Feature-Sensitive Deep Convolutional Neural Network for Multi-Instance Breast Cancer Detection

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第4期19卷 2241-2251页

作者： Wang, Yan Zhang, Lei Shu, Xin Feng, Yangqin Yi, Zhang Lv, Qing Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu 610065 Sichuan Peoples R China ASTAR Inst High Performance Comp Singapore 138632 Singapore Sichuan Univ West China Hosp Dept Galactophore Surg Chengdu 610041 Sichuan Peoples R China

To obtain a well-performed computer-aided detection model for detecting breast cancer, it is usually needed to design an effective and efficient algorithm and a well-labeled dataset to train it. In this paper, first, a multi-instance mammography clinic dataset was constructed. Each case in the dataset includes a different number of instances captured from different views, it is labeled according to the pathological report, and all the instances of one case share one label. Nevertheless, the instances captured from different views may have various levels of contributions to conclude the category of the target case. Motivated by this observation, a feature-sensitive deep convolutional neural network with an end-to-end training manner is proposed to detect breast cancer. The proposed method first uses a pre-train model with some custom layers to extract image features. Then, it adopts a feature fusion module to learn to compute the weight of each feature vector. It makes the different instances of each case have different sensibility on the classifier. Lastly, a classifier module is used to classify the fused features. The experimental results on both our constructed clinic dataset and two public datasets have demonstrated the effectiveness of the proposed method.

关键词： Feature extraction Mammography Breast cancer Image analysis Deep learning Medical diagnostic imaging Training Mammography breast cancer detection multi-instance classification deep convolutional neural network

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Complexity and Enumeration in Models of Genome Rearrangement 29th

Complexity and Enumeration in Models of Genome Rearrangem...

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29th International Computing and Combinatorics conference, COCOON 2023

作者： Bailey, Lora Blake, Heather Smith Cochran, Garner Fox, Nathan Levet, Michael Mahmoud, Reem Matson, Elizabeth Bailey Singgih, Inne Stadnyk, Grace Wang, Xinyi Wiedemann, Alexander Department of Mathematics Grand Valley State University AllendaleMI United States Department of Mathematics and Computer Science Davidson College DavidsonNC United States Department of Mathematics and Computer Science Berry College Mount BerryGA United States Department of Quantitative Sciences Canisius University BuffaloNY United States Department of Computer Science College of Charleston CharlestonSC United States Department of Computer Science Virginia Commonwealth University RichmondVA United States The Division of Mathematics and Computer Science Alfred University AlfredNY United States Department of Mathematical Sciences University of Cincinnati CincinnatiOH United States Department of Mathematics Furman University GreenvilleSC United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Department of Mathematics Randolph–Macon College AshlandVA United States

ISBN: (纸本)9783031491894

In this paper, we examine the computational complexity of enumeration in certain genome rearrangement models. We first show that the Pairwise Rearrangement problem in the Single Cut-and-Join model (Bergeron, Medvedev, & Stoye, J. Comput. Biol. 2010) is # P -complete under polynomial-time Turing reductions. Next, we show that in the Single Cut or Join model (Feijao & Meidanis, ieee ACM Trans. Comp. Biol. Bioinf. 2011), the problem of enumerating all medians (#MEDIAN ) is logspace-computable (FL ), improving upon the previous polynomial-time (FP ) bound of Miklós & Smith (RECOMB 2015). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Phylogenetic

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DualGOFiller: A Dual-Channel Graph Neural Network with Contrastive Learning for Enhancing Function Prediction in Partially Annotated Proteins 29th

DualGOFiller: A Dual-Channel Graph Neural Network with Cont...

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29th International conference on Research in computational Molecular biology, RECOMB 2025

作者： Wang, Shaojun Liu, Hancheng Zhai, Weiqi Zhu, Shanfeng Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Ministry of Education Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing and Shanghai Institute of Artificial Intelligence Algorithm Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China

ISBN: (纸本)9783031902512

Protein function prediction remains a critical and challenging task in the field of bioinformatics. Despite various existing computational methods, most focus on predicting functions for novel proteins, often overlooking those with incomplete annotations. Predicting missing Gene Ontology (GO) terms for these partially annotated proteins is also important for gaining a comprehensive understanding of their biological roles. To address this gap, we introduce DualGOFiller, a dual-channel graph neural network (GNN) model, which is a novel method specifically focused on predicting missing GO terms for partially annotated proteins. DualGOFiller integrates heterogeneous (protein-GO bipartite graph) and homogeneous (protein-protein interaction network and GO directed acyclic graph) network information, enriched with attribute data from protein sequences and GO term descriptions. It further employs graph contrastive learning (GCL) to strengthen the alignment across different views. Experiments demonstrate the superiority of DualGOFiller over state-of-the-art methods, including traditional recommendation system approaches and several protein function prediction methods. The ablation study further confirms the significant contributions of key component in DualGOFiller for boosting predictive performance. The source code is available at https://***/ZhuLab-Fudan/DualGOFiller. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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ieee/ACM Transactions on computational biology and bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 ieee.

关键词： Tumors

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