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21st Annual International conference on Research in computational Molecular biology (RECOMB)

作者： Guo, Yuchun Tian, Kevin Zeng, Haoyang Gifford, David K. MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

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Modeling Gene Regulatory Network Based on Non-homogeneous Dynamic Bayesian Network 11

Modeling Gene Regulatory Network Based on Non-homogeneous Dy...

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11th International conference on bioinformatics and computational biology (ICBCB)

作者： Hu Chunling Zhang Qianqian Zhang Jiayao Hefei Uni Coll Artificial Intelligence & Big Data Hefei Peoples R China

ISBN: (纸本)9798350397871

Among methods for reconstructing gene regulatory networks, non-homogeneous dynamic Bayesian networks have received much attention because of their advantages of expressing both the regulatory relationships among genes and the strengths of relationships among genes. However, such models sample the parent node set randomly, and the selection of the candidate parent node set does not take into account the correlation between nodes, resulting in a low convergence speed of network reconstruction. This paper has proposed a non-homogeneous dynamic Bayesian network model based on parent node filtering (PF-NH-DBN). Firstly, PF-NH-DBN uses mutual information and time-series conditional mutual information to screen the initial set of candidate parent nodes. Thereby reducing the search space and further removing redundant edges, making network reconstruction more accurate. Secondly, the mutual information based on the Gaussian mutation strategy is proposed to avoid falling into the local optimum. Finally, the experimental results on synthetic and on real biological network data show that the new model yields better network reconstruction accuracies than the original model.

关键词： gene regulatory network non-homogeneous dynamic Bayesian network mutual information network structure inference

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Truncated profile hidden Markov models

Truncated profile hidden Markov models

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2005 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB '05

作者： Smith, Scott F. IEEE United States Department of Electrical and Computer Engineering Boise State University Boise ID 83725-2075 United States

ISBN: (纸本)0780393872

The profile hidden Markov model (HMM) is a powerful method for remote homolog database search. However, evaluating the score of each database sequence against a profile HMM is computationally demanding. The computation time required for score evaluation is proportional to the number of states in the profile HMM. This paper examines whether the number of states can be truncated without reducing the ability of the HMM to find proteins containing members of a protein domain family. A genetic algorithm (GA) is presented which finds a good truncation of the HMM states. The results of using truncation on searches of the yeast, E. coli, and pig genomes for several different protein domain families is shown. © 2005 ieee.

关键词： Markov processes

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Enhancing the Performance of a Microarray Gridding Algorithm via GPU Computing Techniques

Enhancing the Performance of a Microarray Gridding Algorithm...

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ieee 13th International conference on bioinformatics and Bioengineering (BIBE)

作者： Katsigiannis, Stamos Zacharia, Eleni Maroulis, Dimitris Univ Athens Real Time Syst & Image Anal Lab Dept Informat & Telecommun Athens 15703 Greece

ISBN: (纸本)9781479931637

cDNA microarrays are a useful tool for studying the expression levels of genes. Nevertheless, microarray image gridding remains a challenging and complex task. Most of the microarray image analysis tools require human intervention, leading to variations of the gene expression results. Automatic methods have also been proposed, but present high computational complexity. In this work, the performance enhancement via GPU computing techniques of a fully automatic gridding method, previously proposed by the authors' research group, is presented. The NVIDIA CUDA architecture was utilized in order to achieve parallel computation of complex steps of the algorithm. Experimental results showed that the proposed approach provides enhanced performance in terms of computational time, while achieving higher utilization of the available computational resources.

关键词： DNA computational complexity graphics processing units medical image processing parallel algorithms parallel architectures

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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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High sensitivity technique for translation initiation site detection

High sensitivity technique for translation initiation site d...

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Proceedings of the 2004 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB'04

作者： Ho, Loi Sy Rajapakse, Jagath C. Bioinformatics Research Center School of Computer Engineering Nanyang Technological University Singapore 639798

ISBN: (纸本)0780387287

Low-order Markov models are insufficient to represent hidden and complex features surrounding translation initiation sites (TISs). We present a neural network approach for detecting TISs of eukaryotes that combines lower-order models carrying biological knowledge, with non-linearity, to capture higher-order nucleotide correlations at TISs and in the surrounding coding and noncoding regions. The model consists of Markov chain models of low-order, a protein encoding model, neural networks, and a ribosome scanning model. The Markov models capture the local interaction of coding and noncoding regions while the protein encoding model utilizes evolutionary information of proteins to encode the downstream coding, sequence of the ITS. With the incorporation of ribosome scanning model, the present method allows for incorporating the biological contextual information of potential TIS into the neural network based prediction system, thereby increasing number of correct recognitions considerably. A 3-fold cross-validation evaluation on the Pedersen and Nielsen dataset yielded an average 93.8% of sensitivity and 96.9% of specificity, indicating superior accuracy of the present system to the previous highest measures of 88.5% of sensitivity and 96.3% of specificity. ©2004 ieee.

关键词： Neural networks

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Guest Editorial for Selected Papers From BIOKDD 2019

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第3期18卷 809-810页

作者： Yan, Da Thankachan, Sharma Chen, Jake Y. Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Univ Alabama Birmingham UAB Dept Genet UAB Informat Inst Genet Comp Sci & Informat Sect Birmingham AL 35294 USA

The papers in this special section were presented at the 18th International Workshop on Data Mining in bioinformatics (BIOKDD), held in conjunction with the ACM SIGKDD International conference on Knowledge Discovery a... 详细信息

关键词： Special Issues And Sections bioinformatics Biological System Modeling Data Mining Big Data Meetings

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RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery 9

RL-MD: A Novel Reinforcement Learning Approach for DNA Motif...

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9th ieee International conference on Data Science and Advanced Analytics (DSAA)

作者： Wang, Wen Wang, Jianzong Si, Shijing Huang, Zhangcheng Xiao, Jing Ping An Technol Shenzhen Co Ltd Shenzhen Peoples R China Shanghai Int Studies Univ Sch Econ & Finance Shanghai Peoples R China

ISBN: (纸本)9781665473309

The extraction of sequence patterns from a collection of functionally linked unlabeled DNA sequences is known as DNA motif discovery, and it is a key task in computational biology. Several deep learning-based techniques have recently been introduced to address this issue. However, these algorithms can not be used in real-world situations because of the need for labeled data. Here, we presented RL-MD, a novel reinforcement learning based approach for DNA motif discovery task. RL-MD takes unlabelled data as input, employs a relative informationbased method to evaluate each proposed motif, and utilizes these continuous evaluation results as the reward. The experiments show that RL-MD can identify high-quality motifs in real-world data.

关键词： Biomedical AI Reinforcement Learning computational biology bioinformatics DNA Motif Discovery

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Clustering Algorithm of Novel Distribution Function for Dimensionality Reduction Using Big Data of OMICS: Health, clinical and biology Research Information

Clustering Algorithm of Novel Distribution Function for Dime...

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7th ieee International conference on computational intelligence and Computing Research (ICCIC)

作者： Srikanth, Panigrahi VNRVJIET Dept IT Hyderabad 500090 Andhra Pradesh India

ISBN: (纸本)9781509006120

This paper defines the problem and design of the appropriate similarity with distribution function of the omics data is a critical objective. Data mining integrate methodical section at the large explosion of huge amount data that can be obtained to utilize and innovative knowledge. Researchers present and future the omics technologies permit to imitate as highly dimensional of omics data. This paper main objective to distance measure is using to concern as clustering algorithms. In order to particular tasks based on reduced high-dimensional omics data of dimensional reduction applying proposed distance measure is designed with distribution function based on PDF and CDF using designed average function and distance measure. It is using training and testing reduce data based on clusters. Reduced data using with class and without class of the OMICS data with accurate results.

关键词： Proteomics Genomics Transcriptomics Metabolisms Big data OMICS data Distribution Function Clustering algorithms

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A computational Model to Analyse E/I (Excitation/Inhibition) Dynamics for Neural Network Integrated with Astrocyte

A Computational Model to Analyse E/I (Excitation/Inhibition)...

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2020 ieee conference on computational intelligence in bioinformatics and computational biology, CIBCB 2020

作者： Bandyopadhyay, Anirban Sharma, Gaurav Roy Chowdhury, Shubhajit Biomedical Systems Laboratory Multimedia Analytics Networks and Systems Group School of Basic Science United States Biomedical Systems Laboratory Multimedia Analytics Networks and Systems Group School of Computing and Electrical Engineering Indian Institute of Technology Mandi India

ISBN: (纸本)9781728194684

Excitatory glutamate and inhibitory GABA dynamics homeostasis, which is often designated as E/I ratio, is regulated by different types of neural inhibitory circuit system. Gliotransmitter released from astrocyte coupled with two main types of the neural network system, feedback and feedforward inhibitory network can render alteration in E/I dynamics. The recent study provides a new model to study the E/I dynamics by integrating the neural circuit with astrocyte, aiming to understand the effect of input synaptic current ranging 20 -40μA/cm2, the effect of two different types of neural inhibition networks on the E/I dynamics and the effect of information transmission from astrocyte to the neural network with different coupling parameters on the E/I dynamics. The simulated result reveals that the frequency of E/I peak and the maximum peak height is increased with increasing the input current. The maximum E/I peak is diminished in the case of feedback inhibition system than the feedforward system with the constant input current. Another important outcome of the model is that the E/I ratio is dependent on the astrocytic glutamate with a limiting coupling parameter value. © 2020 ieee.

关键词： Dynamics

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