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ieee Congress on Evolutionary Computation (ieee CEC)

作者： McBride, Ryan Tsang, Herbert H. Trinity Western Univ Appl Res Lab Langley BC Canada

ISBN: (纸本)9781728183923

RNA Design is an essential problem in bioinformatics to tailor RNA Sequences that guide our biology and medicine. While there are many RNA Design solutions in literature, the field has been limited by the high runtime to estimate how a candidate RNA sequence will fold, especially with "long sequences" over 750 "bases";as these folding algorithms are called millions of times per RNA Design problem, this limits RNA Design to shorter sequences despite the prevalence of long sequences in nature;for example, computationally designing COVID's over 20000 bases RNA sequence is not feasible with current RNA Design algorithms. To address this issue, we are the first work to integrate LinearFold, a faster RNA prediction algorithm used by Baidu to analyze COVID with a higher efficiency, into RNA Design. We compare the runtime and solution quality of our Applied Research Lab's Simulated Annealing solution (SIMARD) with and without LinearFold to design sequences thousands of bases long that timeout after weeks of runtime with current algorithms. We also survey challenges in terms of solution quality and adjusting the cooling schedule parameters. This work is thus a first step into RNA Design for longer RNA sequences.

关键词： bioinformatics RNA Design Simulated Annealing Evolutionary Algorithms computational intelligence

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Ensemble Learning Based Gene Regulatory Network Inference 33

Ensemble Learning Based Gene Regulatory Network Inference

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ieee 33rd International Conference on Tools with Artificial intelligence (ICTAI)

作者： Peignier, Sergio Sorin, Baptiste Calevro, Federica Univ Lyon INSA Lyon INRAE BF2IUMR0203 F-69621 Villeurbanne France

ISBN: (纸本)9781665408981

In the machine learning field, the technique known as ensemble learning aims at combining different base learners in order to increase the quality and the robustness of the predictions. Indeed, this approach has widely been applied to tackle, with success, real world problems from different domains, including computational biology. Nevertheless, despite their potential, ensembles combining results from different base learners, have been understudied in the context of gene regulatory network inference. In this paper we applied genetic algorithms and frequent itemset mining, to design small but effective ensembles of gene regulatory network inference methods. These ensembles, were evaluated and compared to well-established single and ensemble methods, on real and synthetic datasets. Results showed that small ensembles, consisting of few but diverse base learners, enhance the exploration of the solution space, and compensate base-learners biases, outperforming state-of-the-art methods. Results advocate for the use of such methods as gene regulatory network inference tools.

关键词： bioinformatics Gene Regulatory Network Inference Ensemble Learning

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Identifying Drug Resistant miRNAs Using Entropy Based Ranking

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第3期18卷 973-984页

作者： Pal, Jayanta Kumar Ray, Shubhra Sankar Pal, Sankar K. Indian Stat Inst Ctr Soft Comp Res Kolkata 700108 India Univ Calcutta Dept Comp Sci & Engn Kolkata 700073 India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

MicroRNAs play an important role in controlling drug sensitivity and resistance in cancer. Identification of responsible miRNAs for drug resistance can enhance the effectiveness of treatment. A new set theoretic entropy measure (SPEM) is defined to determine the relevance and level of confidence of miRNAs in deciding their drug resistant nature. Here, a pattern is represented by a pair of values. One of them implies the degree of its belongingness (fuzzy membership) to a class and the other represents the actual class of origin (crisp membership). A measure, called granular probability, is defined that determines the confidence level of having a particular pair of membership values. The granules used to compute the said probability are formed by a histogram based method where each bin of a histogram is considered as one granule. The width and number of the bins are automatically determined by the algorithm. The set thus defined, comprising a pair of membership values and the confidence level for having them, is used for the computation of SPEM and thereby identifying the drug resistant miRNAs. The efficiency of SPEM is demonstrated extensively on six data sets. While the achieved F-score in classifying sensitive and resistant samples ranges between 0.31 & 0.50 using all the miRNAs by SVM classifier, the same score varies from 0.67 to 0.94 using only the top 1 percent drug resistant miRNAs. Superiority of the proposed method as compared to some existing ones is established in terms of F-score. The significance of the top 1 percent miRNAs in corresponding cancer is also verified by the different articles based on biological investigations. Source code of SPEM is available at http://***/***

关键词： miRNA cancer drug resistance fuzzy set Z(+) number entropy histogram granular probability bioinformatics

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Estimating Functional Connectivity by Integration of Inherent Brain Function Activity Pattern Priors

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2420-2430页

作者： Zhu, Zhiyuan Zhen, Zonglei Wu, Xia Li, Shuo Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Beijing Normal Univ Engn Res Ctr Intelligent Technol & Educ Applicat Minist Educ Beijing 100875 Peoples R China Beijing Normal Univ Fac Psychol Beijing 100875 Peoples R China Western Univ Dept Med Imaging London ON N6A 3K7 Canada Digital Image Grp DIG London ON N6A 3K7 Canada

Brain functional connectivity (FC) has shown great potential in becoming biomarkers of brain status. However, the problem of accurately estimating FC from complex-noisy fMRI time series remains unsolved. Usually, a regularization function is more appropriate in fitting the real inherent properties of the brain function activity pattern, which can further limit noise interference to improve the accuracy of the estimated result. Recently, the neuroscientists widely suggested that the inherent brain function activity pattern indicates sparse, modular and overlapping topology. However, previous studies have never considered this factual characteristic. Thus, we propose a novel method by integration of these inherent brain function activity pattern priors to estimate FC. Extensive experiments on synthetic data demonstrate that our method can more accurately estimate the FC than previous. Then, we applied the estimated FC to predict the symptom severity of depressed patients, the symptom severity is related to subtle abnormal changes in the brain function activity, a more accurate FC can more effectively capture the subtle abnormal brain function activity changes. As results, our method better than others with a higher correlation coefficient of 0.4201. Moreover, the overlapping probability of each brain region can be further explored by the proposed method.

关键词： Brain modeling Functional magnetic resonance imaging Correlation Estimation Diseases Covariance matrices computational modeling Functional connectivity sparse inverse covariance estimation sparse overlapping modularized priors depression

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Evolving Transcriptomic Profiles From Single-Cell RNA-Seq Data Using Nature-Inspired Multiobjective Optimization

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2445-2458页

作者： Li, Xiangtao Zhang, Shixiong Wong, Ka-Chun Jilin Univ Sch Artificial Intelligence Jilin 132000 Jilin Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Transcriptomic profiling plays an important role in post-genomic analysis. Especially, the single-cell RNA-seq technology has advanced our understanding of gene expression from cell population level into individual cell level. Many computational methods have been proposed to decipher transcriptomic profiles from those RNA-seq data. However, most of the related algorithms suffer from realistic restrictions such as high dimensionality and premature convergence. In this paper, we propose and formulate an evolutionary multiobjective blind compressed sensing (EMOBCS) to address those problems for evolving transcriptomic profiles from single-cell RNA-seq data. In the proposed framework, to characterize various gene expression profile models, two objective functions including chi-squared kernel score and euclidean distance of different gene expression profiles are formulated. After that, multiobjective blind compressed sensing based on artificial bee colony is designed to optimize the two objective functions on single-cell RNA-seq data by proposing a rank probability model and two new search strategies into the cooperative convolution framework in an unbiased manner. To demonstrate its effectiveness, extensive experiments have been conducted, comparing the proposed algorithm with 14 algorithms including eight state-of-the-art algorithms and six different EMOBCS algorithms under different search strategies on 10 single-cell RNA-seq datasets and one case study. The experimental results reveal that the proposed algorithm is better than or comparable with those compared algorithms. Furthermore, we also conduct the time complexity analysis, convergence analysis, and parameter analysis to demonstrate various properties of EMOBCS.

关键词： Transcriptomic profiling compressed sensing RNA-seq multi-objective optimization

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m6A-NeuralTool: Convolution Neural Tool for RNA N6-Methyladenosine Site Identification in Different Species

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ieee ACCESS 2021年 9卷 17779-17786页

作者： Rehman, Mobeen Ur Hong, Kim Jee Tayara, Hilal Chong, Kil To Jeonbuk Natl Univ Dept Elect & Informat Engn Jeonju 54896 South Korea Air Univ Dept Avion Engn Islamabad 44000 Pakistan VIS Coll Jeonju Dept New & Renewable Energy Jeonju 55069 South Korea Jeonbuk Natl Univ Sch Int Engn & Sci Jeonju 54896 South Korea Jeonbuk Natl Univ Adv Elect & Informat Res Ctr Jeonju 54896 South Korea

An important role is played by N6-methyladenosine (m6A) in RNA methylation modification. The modification information is crucially required for development in the field of medicine. Biochemical experiments for m6A identification have demonstrated high-quality results. However, this process is not a feasible solution due to its cost and time constraints. Recently, artificial intelligence has played a role to produce effective models which can be utilized for speedy and efficient identification of N6-methyladenosine sites. In consensus, a computational model named as m6A-NeuralTool is proposed in this study. The m6A-NeuralTool makes the final prediction for the identification of m6A sites by applying majority voting on three different sub-architectures. These sub-architectures uses a set of Convolution layers to extract the important features from the one-hot encoded input sequence. Further, one of the sub-architectures uses fully connected layers for the classification while the other two uses Support Vector Machine and Naive Bayes. The proposed m6A-NeuralTool is evaluated on four different species datasets using 10-fold cross-validation, where the proposed tool outperformed the existing state-of-art models for modification identification of m6A sites. When compared with existing models the proposed model increased the accuracy by 14.8%, 8.8%, 6.3%, and 4.9% for the datasets of Saccharomyces cerevisiae (S. cerevisiae), Arabidopsis thaliana (A. thaliana), Mus musculus (M. musculus) and Homo sapiens (H. sapiens) species respectively. The proposed model will allow the researchers from the field of bioinformatics and medicine to accurately identify the modification in m6A sites and use the information in the development of different products which would be beneficial for the community. The m6A-NeuralTool can be accessed freely at: http://***/tools/m6A-NeuralTool/

关键词： RNA Convolution Feature extraction Support vector machines Tools bioinformatics Mathematical model RNA N6-methyladenosine convolution neural network (CNN) support vector machine (SVM) naive bayes (NB) computational biology

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NeDRex-Web: An Interactive Web Tool for Drug Repurposing by Exploring Heterogeneous Molecular Networks

NeDRex-Web: An Interactive Web Tool for Drug Repurposing by ...

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ieee International Conference on bioinformatics and Biomedicine (BIBM)

作者： Andreas Maier Mahdie Rafiei Elisa Anastasi Olga Zolotareva James Skelton Maria L. Elkjaer Ana I. Casas Cristian Nogales Harald H. H. W. Schmidt Tim Kacprowski David B. Blumenthal Anil Wipat Sepideh Sadegh Jan Baumbach Institute for Computational Systems Biology University of Hamburg Hamburg Germany School of Computing Newcastle University Newcastle Upon Tyne UK Department of Neurology Odense University Hospital Odense Denmark Department of Pharmacology and Personalised Medicine Maastricht University Maastricht The Netherlands Braunschweig Integrated Centre of Systems Biology Technical University of Brunswick Brunswick Germany Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Finding new indications for approved drugs is a promising alternative to the often very lengthy and expensive process of de novo drug development. Systems medicine has brought forth several different approaches to tackle this important task. We recently published NeDRex, a network medicine tool for the identification of disease modules and drug repurposing. NeDRex-Web (https://***) brings existing and new features of the NeDRex platform to a user-friendly and research-oriented web application, enabling online exploration of large heterogeneous molecular networks. Focusing mainly on drug repurposing, NeDRex-Web implements customizable disease module identification and drug prioritization workflows to support users of diverse backgrounds in their research. Users are assisted during every step of their analysis, including the definition of relevant input sets, the selection from various algorithms for module identification or drug prioritization, and the prioritization of the results by their statistical significance. A guided connectivity search provides an easy way to identify links between node sets of interest and can be used to create user-specific induced networks.

关键词： Drugs Focusing Knowledge graphs bioinformatics Diseases

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AI-Driven Decision Support System for Heart Failure Diagnosis: INTELHEART Approach Towards Personalized Treatment Strategies

AI-Driven Decision Support System for Heart Failure Diagnosi...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Smiljana Tomasevic Andjela Blagojevic Tijana Geroski Gordana Jovicic Bogdan Milicevic Momcilo Prodanovic Ilija Kamenko Bojana Bajic Stefan Simovic Goran Davidovic Dragana Ignjatovic Ristic Andrej Preveden Lazar Velicki Arsen Ristic Svetlana Apostolovic Edin Dolicanin Nenad Filipovic Faculty of Engineering University of Kragujevac Serbia Institute for Information Technologies Kragujevac Serbia Institute for Artificial Intelligence Research and Development of Serbia Novi Sad Serbia Faculty of Medical Sciences University of Kragujevac Serbia Faculty of Medicine University of Novi Sad Serbia Faculty of Medicine (School of Medicine) University of Belgrade Serbia Faculty of Medicine University of Nis Serbia State University of Novi Pazar Serbia

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure is recognized as a modern epidemic and despite advances in therapy and research, heart failure still carries an ominous prognosis and a significant socioeconomic burden. The main aim of this paper is to demonstrate how novel Decision Support System (DSS) and computational platform like INTELHEART can transform the future of healthcare and early diagnosis of heart failure. The main idea is integration of patient-specific data (i.e. demographic and physical characteristics, medical history, symptoms and signs) and results obtained using existing and novel diagnostic technologies into the cloud environment. Data will be used by different tools for machine learning and computational modelling, developing virtual patient population. Moreover, voice as a biomarker will be collected among participating patients, in order to create a VoiceHeart mobile app. INTELHEART represents a transformative advancement in heart failure care, aiming to make treatment more personalized, and proactive. This initiative centers on precision medicine, using AI-driven analysis and a powerful DSS alongside the cloud-based platform and VoiceHeart mobile app to assist both clinicians and patients. Additionally, it incorporates assessments of psychological resilience and emotional well-being, addressing the oftenoverlooked mental health factors essential to comprehensive heart failure management.

关键词： Decision support systems Cloud computing Precision medicine Transforms Mobile applications Cardiovascular diseases Socioeconomics Prognostics and health management Medical diagnostic imaging Resilience

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MIN: Multi-Channel Interaction Network for Drug-Target Interaction With Protein Distillation

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 1118-1127页

作者： Shuqi Li Shufang Xie Hongda Sun Yuhan Chen Tao Qin Tianjun Ke Rui Yan Gaoling School of Artificial Intelligence Renmin University of China Beijing China Microsoft Research Beijing China

Traditional drug discovery processes are both time-consuming and require extensive professional expertise. With the accumulation of drug-target interaction (DTI) data from experimental studies, leveraging modern machine-learning techniques to discern patterns between drugs and target proteins has become increasingly feasible. In this paper, we introduce the Multi-channel Interaction Network (MIN), a novel framework designed to predict DTIs through two primary components: a representation learning module and a multi-channel interaction module. The representation learning module features a C-Score Predictor-assisted screening mechanism, which selects critical residues to enhance prediction accuracy and reduce noise. The multi-channel interaction module incorporates a structure-agnostic channel, a structure-aware channel, and an extended-mixture channel, facilitating the identification of interaction patterns at various levels for optimal complementarity. Additionally, contrastive learning is utilized to harmonize the representations of diverse data types. Our experimental evaluations on public datasets demonstrate that MIN surpasses other strong DTI prediction methods. Furthermore, the case study reveals a high overlap between the residues selected by the C-Score Predictor and those in actual binding pockets, underscoring MIN’s explainability capability. These findings affirm that MIN is not only a potent tool for DTI prediction but also offers fresh insights into the prediction of protein binding sites.

关键词： Proteins Drugs Diffusion tensor imaging Representation learning Protein sequence Predictive models Compounds Feature extraction Three-dimensional displays Drug discovery

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Implementation of Grey Scale Normalization in Machine Learning & Artificial intelligence for bioinformatics using Convolutional Neural Networks 6

Implementation of Grey Scale Normalization in Machine Learni...

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6th International Conference on Inventive Computation Technologies (ICICT)

作者： Kothari, Divya Patel, Mayank Sharma, Ajay Kumar Geetanjali Inst Tech Studies Udaipur Rajasthan India

ISBN: (纸本)9781728185019

Machine Learning is a trending field nowadays and is very well known as an application of Artificial intelligence (AI). Machine learning makes use of secure arithmetical algorithms to construct computers assignment in a constructive way without being unequivocally programmed. The algorithms acquire freedom of a participated value and estimate output for this by utilizing definite arithmetical methods. The key function of machine learning is to create smart machines that can visualize and work related to human beings. Artificial intelligence has been witnessing a massive improvement in bridging the gap among the capabilities of humans and technologies. Similarly, one of the characteristics of the field were tried to combine the outstanding effects. A Convolution Neural Network (CNN) is a sort of Deep Learning algorithm which can obtain an input image, assign consequence to different aspects in the image and capable to differentiate one from the other. The pre-processing requirement in a CNN is lesser as compared to other classification algorithms. CNN has the capability to find out these filter's characteristics. bioinformatics is a term that is a mixture of two terms bio and informatics. Bio means associated with biology and informatics means in a sequence of information. Thus bioinformatics is an area that deals with handing out and accepting biological statistics using the computational and arithmetical approaches. Machine Learning has added up to of applications in the area of bioinformatics. Machine Learning finds its submission in the subsequent subfields of bioinformatics. The aim of this article is to perform a grayscale normalization of a selected image and thereafter to reduce the effect of illumination differences. Normalization is considered so that CNN works in a faster manner. Different models are available but Keras model is selected to perform this task. Keras supports the style of data preparation for image statistics via the Image Data Generator gro

关键词： Machine Learning Artificial intelligence Convolution Neural Network Application Program Interface

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