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ieee symposium Series on computational intelligence (SSCI)

作者： Benjamin Poole Minwoo Lee The Department of Computer Science University of North Carolina at Charlotte Charlotte NC USA

ISBN: (纸本)9781665487696

Brain-Computer Interfaces (BCI) have allowed for direct communication from the brain to external applications for the automatic detection of cognitive processes such as error recognition. Error-related potentials (ErrPs) are a particular brain signal elicited when one commits or observes an erroneous event. However, due to the noisy properties of the brain and recording devices, ErrPs vary from instance to instance as they are combined with an assortment of other brain signals, biological noise, and external noise, making the classification of ErrPs a non-trivial problem. Recent works have revealed particular cognitive processes such as awareness, embodiment, and predictability that contribute to ErrP variations. In this paper, we explore the performance of classifier transferability when trained on different ErrP variation datasets generated by varying the levels of awareness and embodiment for a given task. In particular, we look at transference between observational and interactive ErrP categories when elicited by similar and differing tasks. Our empirical results provide an exploratory analysis into the ErrP transferability problem from a data perspective.

关键词： Cognitive processes Brain-computer interfaces biology Recording Noise measurement Task analysis computational intelligence

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New Distance-Based approach for Genome-Wide Association Studies

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第5期19卷 2938-2949页

作者： Irigoien, Itziar Cormand, Bru Soler-Artigas, Maria Sanchez-Mora, Cristina Ramos-Quiroga, Josep-Antoni Arenas, Concepcion Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Leioa 48940 Biscay Spain Univ Barcelona Fac Biol Dept Genet Microbiol & Estadist Barcelona 08007 Catalonia Spain Inst Salud Carlos III Ctr Invest Biomed Red Enfermedades Raras CIBERER Madrid 28220 Spain Inst Biomed Univ Barcelona IBUB Barcelona 08028 Catalonia Spain Inst Recerca St Joan de Diu Barcelona 08950 Catalonia Spain Univ Autonoma Barcelona Vall dHebron Res Inst Psychiat Genet Unit Grp Psychiat Mental Hlth & Addict Barcelona 08193 Spain Hosp Univ Vall dHebron Dept Psychiat Barcelona 08035 Spain Inst Salud Carlos III Biomed Network Res Ctr Mental Hlth CIBERSAM Madrid 28220 Spain Univ Barcelona Fac Biol Dept Genet Microbiol & Stat Barcelona 08007 Catalonia Spain Univ Autonoma Barcelona Dept Psychiat & Legal Med Barcelona 08193 Spain Univ Barcelona Dept Genet Microbiol & Stat Stat Sect Barcelona 08028 Spain

With the rise of genome-wide association studies (GWAS), the analysis of typical GWAS data sets with thousands of single-nucleotide polymorphisms (SNPs) has become crucial in biomedicine research. Here, we propose a new method to identify SNPs related to disease in case-control studies. The method, based on genetic distances between individuals, takes into account the possible population substructure, and avoids the issues of multiple testing. The method provides two ordered lists of SNPs;one with SNPs which minor alleles can be considered risk alleles for the disease, and another one with SNPs which minor alleles can be considered as protective. These two lists provide a useful tool to help the researcher to decide where to focus attention in a first stage.

关键词： Diseases Genomics bioinformatics Psychiatry Tools Sociology Mental health Distances NN-nearest neighbours DB-discriminant genome-wide association studies ADHD

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Agent-based model using adjacent possible space for generating waves of novelty

Agent-based model using adjacent possible space for generati...

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ieee symposium Series on computational intelligence (SSCI)

作者： Mikihiro Suda Takumi Saito Mizuki Oka Grad. School of Science and Technology University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781665487696

The continuous emergence of novelty is key to the creation of open-endedness. The phenomenon of the waves of popularity of novelties which generates novelties, and leads to their attracting attention and evolving, has been observed in groups of people. The exploration of this mechanism can provide clues to understanding the nature of open-ended evolution. It has been demonstrated that the behavior of people within this context can be successfully captured by an agent-based model that incorporates the adjacent possible space. In this study, we propose an extended model on this basis to create waves of novelty. The proposed model introduces a bias in that it increases the selection probability of agents who interact with influential agents. The introduction of this bias leads to the novelty gaining attention over preferential attachment. The proposed model fits better to real data than existing models, and captures the dynamics and structure of real social networks more effectively. The construction of speciations and niches is necessary for natural biological species to evolve. Similarly, the results of this study suggest that the provision of an advantageous structure in a particular local environment is important for the growth of novelty.

关键词： Correlation Social networking (online) Evolution (biology) Biological system modeling computational modeling Semantics Data models

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IHA-YOLO: Inter-Head Attention for Real-Time Cell Detection

IHA-YOLO: Inter-Head Attention for Real-Time Cell Detection

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ieee International symposium on Biomedical Imaging

作者： Tooba Tehreem Sheikh Komal Kumar Muhammad Awais Noa Novershtern Rao Muhammad Anwer Hisham Cholakkal Mohamed bin Zayed University of Artificial Intelligence UAE Queen Mary University of London UK Georgia Institute of Technology USA Weizmann Institute of Science Israel

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Multiclass cell detection is a crucial task in numerous biomedical applications, particularly in cell biology. The development of YOLO object detection models has advanced the field of real-time detection, but it is still struggling with challenges in medical imaging due to limited data availability, overlapping tiny objects, diverse cell types, and class imbalances. In this paper, we introduce Inter-Head Attention (IHA)-YOLO, a novel model that proposes an inter-head self-attention block to enhance global representation learning, thereby improving the contextual understanding across feature maps and performing effective detection of small cells and sub-cell structures in medical images. Through extensive experiments on five publicly available datasets, IHA-YOLO outperforms the state-of-the-art methods, achieving an average absolute mAP50 improvement of 2.03% and a 13% faster inference rate. In addition to cell detection, we adapt IHA-YOLO for cell counting to demonstrate its effectiveness. Our source code and models are available at: https://***/toobatehreem/IHA-YOLO.

关键词： YOLO Representation learning Adaptation models Head Biological system modeling computational modeling Source coding Refining Real-time systems Biomedical imaging

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PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2841-2847页

作者： Nguyen, Thin Lee, Samuel C. Quinn, Thomas P. Truong, Buu Li, Xiaomei Tran, Truyen Venkatesh, Svetha Le, Thuc Duy Deakin Univ Appl Artificial Intelligence Inst Burwood Vic 3125 Australia Univ South Australia Sch Informat Technol & Math Sci Adelaide SA 5001 Australia

The classification of clinical samples based on gene expression data is an important part of precision medicine. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graph-based methods to improve classifier performance. Existing approaches to personalized gene networks have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced. Here, we propose a novel method, called Personalized Annotation-based Networks (PAN), that avoids this limitation by using curated annotation databases to transform gene expression data into a graph. Unlike competing methods, PANs are calculated for each sample independent of the population, making it a more efficient way to obtain single-sample networks. Using three breast cancer datasets as a case study, we show that PAN classifiers not only predict cancer relapse better than gene features alone, but also outperform PPI (protein-protein interactions) and population-level graph-based classifiers. This work demonstrates the practical advantages of graph-based classification for high-dimensional genomic data, while offering a new approach to making sample-specific networks. Supplementary information: PAN and the baselines are implemented in Python. Source code and data are available at https://***/thinng/PAN.

关键词： Annotations Gene expression Databases Breast cancer Ontologies Genomics Sociology Personalized medicine annotation-based networks gene expression breast cancer

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Molecular Connectivity Index-Based Data Augmentation for Molecular Property Prediction

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 1234-1247页

作者： Zeyu Wang Tianyi Jiang Jinhuan Wang Jiafei Shao Bin Wei Qi Xuan Hong Wang Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou China Binjiang Institute of Artificial Intelligence Zhejiang University of Technology Hangzhou China College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals Zhejiang University of Technology Hangzhou China

Recent years have seen a rapid growth of machine learning in cheminformatics problems. In order to tackle the problem of insufficient training data in reality, more and more researchers pay attention to data augmentation technology. However, few researchers pay attention to the problem of construction rules and domain information of data, which will directly impact the quality of augmented data and the augmentation performance. While in graph-based molecular research, the molecular connectivity index, as a critical topological index, can directly or indirectly reflect the topology-based physicochemical properties and biological activities. In this paper, we propose a novel data augmentation technique that modifies the topology of the molecular graph to generate augmented data with the same molecular connectivity index as the original data. The molecular connectivity index combined with data augmentation technology helps to retain more topology-based molecular properties information and generate more reliable data. Furthermore, we adopt five benchmark datasets to test our proposed models, and the results indicate that the augmented data generated based on important molecular topology features can effectively improve the prediction accuracy of molecular properties, which also provides a new perspective on data augmentation in cheminformatics studies.

关键词： Indexes Data augmentation Data models Training Data mining Graph neural networks Chemicals Topology Reliability Predictive models

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Fast Hybrid Data Structure for a Large Alphabet K-Mers Indexing for Whole Genome Alignment

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ieee ACCESS 2021年 9卷 161890-161897页

作者： Hrivnak, Rostislav Gajdos, Petr Snasel, Vaclav VSB Tech Univ Ostrava Dept Comp Sci FEECS Ostrava 70800 Czech Republic

The most common index data structures used by whole genome aligners (WGA) are based on suffix trees (ST), suffix arrays, and FM-indexes. These data structures show good performance results as WGA works with sequences of letters over small alphabets;for example, four letters a, c, t, g for DNA alignment. A novel whole genome aligner, which we are developing, will work with distances between the label sites on DNA samples, which are represented as a sequence of positive integers. The size of alphabet sigma is theoretically unlimited. This has prompted us to investigate if there is a better structure that would improve search performance on large alphabets compared to the commonly used suffix-based structures. This paper presents the implementation of a highly optimized hybrid index data structure based on a ternary search tree (TST) and hash tables, which perform much better when working with strings on large alphabets compared to the ST. Single core parallelism was achieved using advanced vector extensions (AVX) single instruction multiple data (SIMD) instruction set. When searching for short k-mers over an alphabet of 25, 695 letters, our index search performance was up to 29 times better than the search performance of the reference ST. When the alphabet was compressed to approximately 1;300 letters, our index search performance was still up to 2.6 times better than the ST. The source code is available free on http://***/Resources/Upload/Home/public/software/*** under the MIT license.

关键词： Engineering management engineering in medicine and biology bioinformatics computational and artificial intelligence computer architecture data structures tree data structures parallel processing parallel algorithms

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A Hierarchical Discriminative Sparse Representation Classifier for EEG Signal Detection

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1679-1687页

作者： Gu, Xiaoqing Zhang, Cong Ni, Tongguang Changzhou Univ Sch Informat Sci & Engn Changzhou 213164 Jiangsu Peoples R China Changzhou Univ Sch Comp & Artificial Intelligence Changzhou 213164 Jiangsu Peoples R China

Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. Recently sparse representation-based classification (SRC) methods have achieved the good performance in EEG signal automatic detection, by which the EEG signals are sparsely represented using a few active coefficients in the dictionary and classified according to the reconstruction criteria. However, most of SRC learn a linear dictionary for encoding, and cannot extract enough information and nonlinear relationship of data for classification. To solve this problem, a hierarchical discriminative sparse representation classification model (called HD-SRC) for EEG signal detection is proposed. Based on the framework of neural network, HD-SRC learns the hierarchical nonlinear transformation and maps the signal data into the nonlinear transformed space. Through incorporating this idea into label consistent K singular value decomposition (LC-KSVD) at the top layer of neural network, HD-SRC seeks discriminative representation together with dictionary, while minimizing errors of classification, reconstruction and discriminative sparse-code for pattern classification. By learning the hierarchical feature mapping and discriminative dictionary simultaneously, more discriminative information of data can be exploited. In the experiment the proposed model is evaluated on the Bonn EEG database, and the results show it obtains satisfactory classification performance in multiple EEG signal detection tasks.

关键词： Electroencephalography Feature extraction Dictionaries Epilepsy Classification algorithms Signal detection Brain modeling Electroencephalogram sparse representation-based classification deep neural network discriminative dictionary learning

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Predicting the stage of non-small cell lung cancer with divergence neural network using pre-treatment computed tomography 9

Predicting the stage of non-small cell lung cancer with dive...

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9th ieee International Conference on bioinformatics and computational biology, ICBCB 2021

作者： Jieun, Choi Hwan-Ho, Cho Hyunjin, Park Sungkunkwan University Dept of Artificial Intelligence Suwon Korea Republic of Sungkunkwan University Dept of Electronic and Computer Engineering Suwon Korea Republic of School of Electronic and Electrical Engineering Sungkunkwan University Suwon Korea Republic of

ISBN: (纸本)9780738132020

Determining the stage of non-small cell lung cancer (NSCLC) is important for treatment and prognosis. Staging includes a professional interpretation of imaging, thus we aimed to build an automatic process with deep learning (DL). We proposed an end-to-end DL method that uses pre-treatment computer tomography images to classify the early- and advanced-stage of NSCLC. DL models were developed and tested to classify the early- and advanced-stage using training (n = 58), validation (n = 7), and testing (n = 17) cohorts obtained from public domains. The network consists of three parts of encoder, decoder, and classification layer. Encoder and decoder layers are trained to reconstruct original images. Classification layers are trained to classify early- and advanced-stage NSCLC patients with a dense layer. Other machine learning-based approaches were compared. Our model achieved accuracy of 0.8824, sensitivity of 1.0, specificity of 0.6, and area under the curve (AUC) of 0.7333 compared with other approaches (AUC 0.5500 ─ 0.7167) in the test cohort for classifying between early- and advanced-stages. Our DL model to classify NSCLC patients into early-stage and advanced-stage showed promising results and could be useful in future NSCLC research. © 2021 ieee.

关键词： Medical imaging

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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 1248-1262页

作者： Dongdong Zhao Qiben Xu Fan Li Yiheng Mao Jianwen Xiang Huanhuan Li Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence and Chongqing Research Institute Wuhan University of Technology Wuhan China School of Computer Science School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

With the development of genome sequencing technology, genomic data have been widely collected and used in real-world scenarios, such as genomic medicine and similar patient query (SPQ) cases, based on similarity comparisons of genomic sequences. However, genomic data are unique for every person and contain a large amount of sensitive information involving personal privacy. Therefore, determining how to protect privacy while utilizing genomic data has become a key issue. In this paper, we mainly investigate the privacy protection of SPQs, and we propose five algorithms (NDB-ED, NDB-Band, NDB-Block, NDB-SIS and NDB-SDS) based on a promising technique called negative representation of information (NRI). The proposed algorithms use five kinds of similarity comparison approaches widely applied in SPQs and convert all genomic sequences into negative databases ( NDBs, among the most important NRI forms) for privacy protection. When performing an SPQ, NDB-ED approximates the edit distance between the sketches (the statistics of NDBs) of two genomic sequences to evaluate the dissimilarity. Banded edit distance and block-wise edit distance are two effective approximate edit distance algorithms, which can greatly reduce the time complexity. NDB-Band and NDB-Block are used to estimate the banded edit distance and the block-wise edit distance between the sketches of NDB pairs, respectively, to further improve query efficiency and reduce communication overhead. Besides, private genome set intersection size (SIS) and set difference size (SDS) can also be used instead of edit distance to evaluate the dissimilarity between genomic pairs during SPQs. NDB-SIS and NDB-SDS estimate the SIS and SDS between the sketches, for similarity comparison. The experimental results demonstrate that the proposed algorithms can achieve promising results in terms of accuracy and efficiency (Our best algorithm improves accuracy by at least 10% and query time is at least 700 times faster than existing a

关键词： bioinformatics Genomics Protection Databases Protocols Privacy Data privacy Accuracy Time complexity Hamming distances

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