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ieee Congress on Evolutionary Computation (ieee CEC)

作者： Tangherloni, Andrea Riva, Simone G. Spolaor, Simone Besozzi, Daniela Nobile, Marco S. Cazzaniga, Paolo Univ Bergamo Dept Human & Social Sci Bergamo Italy Univ Cambridge Dept Haematol Wellcome Trust MRC Cambridge Stem Cell InstWellcome Trust Sanger In Wellcome Trust Genome Campus Cambridge England Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy Bicocca Bioinformat Biostat & Bioimaging Ctr B4 Milan Italy Eindhoven Univ Technol Dept Ind Engn & Innovat Sci Eindhoven Netherlands

ISBN: (纸本)9781728183923

The increasing number of single-cell transcriptomic and single-cell RNA sequencing studies are allowing for a deeper understanding of the molecular processes underlying the normal development of an organism as well as the onset of pathologies. These studies continuously refine the functional roles of known cell populations, and provide their characterization as soon as putatively novel cell populations are detected. In order to isolate the cell populations for further tailored analysis, succinct marker panels-composed of a few cell surface proteins and clusters of differentiation molecules-must be identified. The identification of these marker panels is a challenging computational problem due to its intrinsic combinatorial nature, which makes it an NP-hard problem. Genetic Algorithms (GAs) have been successfully used in bioinformatics and other biomedical applications to tackle combinatorial problems. We present here a GA-based approach to solve the problem of the identification of succinct marker panels. Since the performance of a GA is strictly related to the representation of the candidate solutions, we propose and compare three alternative representations, able to implicitly introduce different constraints on the search space. For each representation, we perform a fine-tuning of the parameter settings to calibrate the GA, and we show that different representations yield different performance, where the most relaxed representations-in which the GA can also evolve the number of genes in the panel-turn out to be the more effective, especially in the case of 0-knowledge problems. Our results also show that the marker panels identified by GAs can outperform manually curated solutions.

关键词： Candidate Solution Representation Genetic Algorithms Single-Cell Transcriptomic Data computational biology bioinformatics

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Convolutional Neural Network With Graphical Lasso to Extract Sparse Topological Features for Brain Disease Classification

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2327-2338页

作者： Ji, Junzhong Yao, Yao Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Artificial Intelligence Inst Beijing Municipal Key Lab Multimedia & Intelligen Beijing 100124 Peoples R China

The functional connectivity provides new insights into the mechanisms of the human brain at network-level, which has been proved to be an effective biomarker for brain disease classification. Recently, machine learning methods have played an important role in functional connectivity classification, among which convolutional neural network (CNN) based methods become a new hot topic since they can extract topological features in the brain network. However, the conventional CNN-based methods haven't taken sparse connectivity patterns (SCPs) of the human brain into consideration, which may lead to redundancy of the topological features, and limit their performance and generalization. To solve it, we propose a novel CNN-based model with graphical Lasso (CNNGLasso) to extract sparse topological features for brain disease classification. First, we develop a novel graphical Lasso model for revealing the SCPs at group-level. Then, the SCPs are used to guide the topological feature extraction. Finally, the obtained sparse topological features are used to classify the patients from normal controls. The experiment results on the ABIDE dataset demonstrate that the CNNGLasso outperforms the others on various performances. Besides, the abnormal brain regions derived from the trained model are consistent with the previous investigations, which further proves the application prospect of the CNNGLasso.

关键词： Feature extraction Diseases Brain modeling Covariance matrices Machine learning Support vector machines Convolutional neural networks Convolutional neural network graphical Lasso sparse topological feature functional magnetic resonance imaging functional connectivity brain disease

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A Memristive Circuit Implementation of Eyes State Detection in Fatigue Driving Based on Biological Long Short-Term Memory Rule

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2218-2229页

作者： Wang, Zilu Hong, Qinghui Wang, Xiaoping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Biological long short-term memory (B-LSTM) can effectively help human process all kinds of received information. In this work, a memristive B-LSTM circuit which mimics a conversion from short-term memory to long-term memory is proposed. That is, the stronger the signal, the more profound the memory and the higher the output. On this basis, an image binarization circuit using adaptive row threshold algorithm is proposed. It can make the image remain a deep impression on the strong pixel information and effectively filter the relatively weak pixel information. In combination with the function of image binarization, a memristive circuit for eyes state detection is proposed by adding corresponding horizontal projection calculation, subtraction calculation and judgement open or closed eyes modules. The proposed circuit can detect whether there is a blink between two adjacent facial images, which uses the characteristics of memristor to detect the difference of horizontal projection between two images. Due to the use of memristor, the proposed circuit can realize in-memory computing, which fundamentally avoids the problem of storage wall and shorten the execution time. Finally, an expectation application in fatigue driving based on the proposed method is demonstrated, which indicates the practicability of the circuit design in this work.

关键词： Memristors Integrated circuit modeling Image processing Fatigue Threshold voltage Software algorithms Computer architecture Memristive circuit design image binarization eyes state detection biological long short-term memory (B-LSTM)

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Investigating the Role of HPC in AI-based Protein-Protein Interaction Analysis

Investigating the Role of HPC in AI-based Protein-Protein In...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Shashikant Patil Jyoti Seth Ananta Ojha Department of uGDX ATLAS SkillTech University Mumbai Maharashtra India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Integrating High-Performance Computing (HPC) with Artificial intelligence (AI) has greatly expanded the area of protein-protein interaction (PPI) analysis, which is essential for comprehending biological processes. In this paper, we studied the importance of HPC in AI-based PPI analysis, providing a unique technique that integrates deep learning, probabilistic modeling, and efficient network analysis. We evaluated the predicted accuracy and computational efficiency of our new technique with six established methods for PPI analysis. We found that the Area Under the ROC Curve (AUC) for the suggested technique was much greater than that for the baseline method. The suggested method's AUC values routinely outperformed those of the gold standard approaches, demonstrating its superiority in predicting protein interactions. Accurate biological insights and applications in drug development and disease understanding rely on this improved prediction capacity. We also evaluated the approaches' efficacy by computing their running times. The suggested technique was shown to be very effective, using much less time for analysis than conventional approaches. This is an enormous benefit, particularly for time-sensitive or extensive PPI study projects. Our study concludes that HPC is crucial for AI-based PPI analysis. The suggested approach shows impressive performance in terms of predicted accuracy and computational economy since it combines deep learning, probabilistic modeling, and HPC-accelerated network analysis. This study advances bioinformatics and systems biology by developing a robust and time-saving resource for PPI analysis. The suggested technique shows promise for additional progress in drug development, illness detection, and functional genomics, all of which rely heavily on a knowledge of protein interactions.

关键词： Proteins Drugs Deep learning Protein engineering Analytical models computational modeling Network analyzers

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Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第1期18卷 216-226页

作者： Yang, Bo Zhang, Yupei Pang, Shanmin Shang, Xuequn Zhao, Xueqing Han, Minghui Xian Polytech Univ State & Local Joint Engn Res Ctr Adv Networking & Sch Comp Sci Shaanxi Key Lab Clothing Intelligence Xian 710048 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China

One type of cancer usually consists of several subtypes with distinct clinical implications, thus the cancer subtype prediction is an important task in disease diagnosis and therapy. Utilizing one type of data from molecular layers in biological system to predict is difficult to bridge the cancer genome to cancer phenotypes, since the genome is neither simple nor independent but rather complicated and dysregulated from multiple molecular mechanisms. Similarity Network Fusion (SNF) has been recently proposed to integrate diverse omics data for improving the understanding of tumorigenesis. SNF adopts Euclidean distance to measure the similarity between patients, which shows some limitations. In this article, we introduce a novel prediction technique as an extension of SNF, namely Deep Subspace Fusion Clustering (DSFC). DSFC utilizes auto-encoder and data self-expressiveness approaches to guide a deep subspace model, which can achieve effective expression of discriminative similarity between patients. As a result, the dissimilarity between inter-duster is delivered and enhanced compactness of intra-cluster is achieved at the same time. The validity of DSFC is examined by extensive simulations over six different cancer through three levels omics data. The survival analysis demonstrates that DSFC delivers comparable or even better results than many state-of-the-art integrative methods.

关键词： Subspace learning cancer subtype data integration omics data

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Evolving Multiobjective Cancer Subtype Diagnosis From Cancer Gene Expression Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2431-2444页

作者： Wang, Yunhe Ma, Zhiqiang Wong, Ka-Chun Li, Xiangtao Northeast Normal Univ Sch Informat Sci & Technol Changchun 130117 Jilin Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Jilin Univ Sch Artificial Intelligence Changchun 130012 Jilin Peoples R China

Detection and diagnosis of cancer are especially essential for early prevention and effective treatments. Many studies have been proposed to tackle the subtype diagnosis problems with those data, which often suffer from low diagnostic ability and bad generalization. This article studies a multiobjective PSO-based hybrid algorithm (MOPSOHA) to optimize four objectives including the number of features, the accuracy, and two entropy-based measures: the relevance and the redundancy simultaneously, diagnosing the cancer data with high classification power and robustness. First, we propose a novel binary encoding strategy to choose informative gene subsets to optimize those objective functions. Second, a mutation operator is designed to enhance the exploration capability of the swarm. Finally, a local search method based on the "best/1" mutation operator of differential evolutionary algorithm (DE) is employed to exploit the neighborhood area with sparse high-quality solutions since the base vector always approaches to some good promising areas. In order to demonstrate the effectiveness of MOPSOHA, it is tested on 41 cancer datasets including thirty-five cancer gene expression datasets and six independent disease datasets. Compared MOPSOHA with other state-of-the-art algorithms, the performance of MOPSOHA is superior to other algorithms in most of the benchmark datasets.

关键词： Cancer Particle swarm optimization Linear programming Gene expression Optimization Encoding Classification cancer subtype diagnosis multiobjective hybrid evolutionary optimization

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A multimodal multi-objective optimisation approach to deal with the phylogenetic inference problem

A multimodal multi-objective optimisation approach to deal w...

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ieee Conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Rivera, Cesar Inostroza-Ponta, Mario Villalobos-Cid, Manuel Univ Santiago Chile Dept Ingn Informat Santiago Chile

ISBN: (纸本)9781728194684

The phylogenetic inference strategies aim to propose hypotheses to explain the evolutionary relationships for different organisms. These resultant evolutionary histories are often represented as phylogenetic trees. In computer science, the phylogenetic inference has been treated as an optimisation problem. The literature has proposed different criteria to select the optimal tree between the possible topologies. In order to reduce the bias associated to the dependency on the selected criterion, different multi-objective optimisation strategies have been proposed during the last decade. These strategies search by solutions using operators and metrics based on the objective space. However, a recent work concluded that the topological features of the trees (decision space) and the objective space in the multi-objective phylogenetic inference context are not related, becoming phylogeny in a multimodal problem. It means that the current multi-objective strategies could discard solutions from different regions of the decision space, limiting the searching process and the resultant topologies. In this work, we propose a new version of the Memetic algorithm based on an NSGA-II scheme for phylogenetic inference, which include a multimodal operator that considers the diversity of the topologies of the trees based on the decision space to rank the solutions. The inclusion of this operator improved the diversity of solutions according to the decision and the objective space, increasing the hypervolume metric compared to the base version of this memetic algorithm.

关键词： multi-objective multimodal optimisation bioinformatics phylogenetic inference

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Design of Turing Systems with Physics-Informed Neural Networks

Design of Turing Systems with Physics-Informed Neural Networ...

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ieee symposium Series on computational intelligence (SSCI)

作者： Jordon Kho Winston Koh Jian Cheng Wong Pao-Hsiung Chiu Chin Chun Ooi School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute of Bioengineering and Bioimaging A*STAR Bioinformatics Institute A*STAR Singapore Singapore Dept of Fluid Dynamics Institute of High Performance Computing Singapore Singapore Dept of Fluid Dynamics Institute of High Performance Computing Centre for Frontier AI Research Singapore Singapore

ISBN: (纸本)9781665487696

Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differential equations containing parameters that determine the rate of constituent diffusion and reaction. Critically, these parameters, such as diffusion coefficient, heavily influence the mode and type of the final pattern, and quantitative characterization and knowledge of these parameters can aid in bio-mimetic design or understanding of real-world systems. However, the use of numerical methods to infer these parameters can be difficult and computationally expensive. Typically, adjoint solvers may be used, but they are frequently unstable for very non-linear systems. Alternatively, massive amounts of iterative forward simulations are used to find the best match, but this is extremely effortful. Recently, physics-informed neural networks have been proposed as a means for data-driven discovery of partial differential equations, and have seen success in various applications. Thus, we investigate the use of physics-informed neural networks as a tool to infer key parameters in reaction─diffusion systems in the steady-state for scientific discovery or design. Our proof-of-concept results show that the method is able to infer parameters for different pattern modes and types with errors of less than 10%. In addition, the stochastic nature of this method can be exploited to provide multiple parameter alternatives to the desired pattern, highlighting the versatility of this method for bio-mimetic design. This work thus demonstrates the utility of physics-informed neural networks for inverse parameter inference of reaction-diffusion systems to enhance scientific discovery and design.

关键词： Self-assembly computational modeling Biological system modeling Partial differential equations Neural networks Stochastic processes Mathematical models

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Identify Consistent Cross-Modality Imaging Genetic Patterns via Discriminant Sparse Canonical Correlation Analysis

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第4期18卷 1549-1561页

作者： Wang, Meiling Shao, Wei Hao, Xiaoke Shen, Li Zhang, Daoqiang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing 211106 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Univ Penn Perelman Sch Med Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA

Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. However, the traditional SCCA algorithm has been designed to seek a linear correlation between the SNP genotype and brain imaging phenotype, ignoring the discriminant similarity information between within-class subjects in brain imaging genetics association analysis. In addition, multi-modality brain imaging phenotypes are extracted from different perspectives and imaging markers from the same region consistently showing up in multimodalities may provide more insights for the mechanistic understanding of diseases. In this paper, a novel multi-modality discriminant SCCA algorithm (MD-SCCA) is proposed to overcome these limitations as well as to improve learning results by incorporating valuable discriminant similarity information into the SCCA algorithm. Specifically, we first extract the discriminant similarity information between within-class subjects by the sparse representation. Second, the discriminant similarity information is enforced within SCCA to construct a discriminant SCCA algorithm (D-SCCA). At last, the MD-SCCA algorithm is adopted to fully explore the relationships among different modalities of different subjects. In experiments, both synthetic dataset and real data from the Alzheimer's Disease Neuroimaging Initiative database are used to test the performance of our algorithm. The empirical results have demonstrated that the proposed algorithm not only produces improved cross-validation performances but also identifies consistent cross-modality imaging genetic biomarkers.

关键词： Imaging Correlation Genetics Brain Diseases Data mining Sparse matrices Imaging genetics sparse canonical correlation analysis multi-modality multi-SNP Alzheimer's disease

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Strategies to Reduce Long-Term Drug Resistance by Considering Effects of Differential Selective Treatments 3rd

Strategies to Reduce Long-Term Drug Resistance by Considerin...

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3rd International symposium on Mathematical and computational Oncology, ISMCO 2021

作者： Ghodsi Asnaashari, Tina Chang, Young Hwan Department of Biomedical Engineering and Computational Biology Program Oregon Health and Science University Portland United States

ISBN: (纸本)9783030912406

Despite great advances in modeling and cancer therapy using optimal control theory, tumor heterogeneity and drug resistance are major obstacles in cancer treatments. Since recent biological studies demonstrated the evidence of tumor heterogeneity and assessed potential biological and clinical implications, tumor heterogeneity should be taken into account in the optimal control problem to improve treatment strategies. Here, first we study the effects of two different treatment strategies (i.e., symmetric and asymmetric) in a minimal two-population model to examine the long-term effects of these treatment methods on the system. Second, by considering tumor adaptation to treatment as a factor of the cost function, the optimal treatment strategy is derived. Numerical examples show that optimal treatment decreases tumor burden for the long-term by decreasing rate of tumor adaptation over time. © 2021, Springer Nature Switzerland AG.

关键词： Tumors

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