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7th ieee International conference on bioinformatics and Bioengineering, BIBE

作者： Dunker, A. Keith Oldfield, Christopher J. Meng, Jingwei Romero, Pedro Yang, Jack Y. Obradovic, Zoran Uversky, Vladimir N. Center for Computational Biology and Bioinformatics Indiana University Schools of Medicine and Informatics Indianapolis IN 46202 United States Center for Computational Biology and Bioinformatics Indiana University School of Informatics Indianapolis IN 46202 United States Department of Biochemistry and Molecular Biology Indiana University School of Medicine Indianapolis IN 46202 United States Department of Radiation Oncology Harvard Medical School Massachusetts General Hospital Boston MA 02114 United States Center for Information Science and Technology Temple University Philadelphia PA 19122 United States Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN 46202 United States Institute for Biological Instrumentation Russian Academy of Sciences 142290 Puschino Russia

ISBN: (纸本)1424415098

Just over 10 years ago, in June, 1997, in the Proceedings of the ieee International conference on Neural Networks, we published our first predictor of intrinsically disordered protein [1]. Since then, we have substantially improved our predictors, and more than 20 other laboratory groups have joined in efforts to improve the prediction of protein disorder. At the algorithmic level, prediction of protein intrinsic disorder is similar to the prediction of secondary structure, but, at the structural level, secondary structure and intrinsic disorder are entirely different. The secondary structure class called random coil or irregular differs from intrinsic disorder due to very different dynamic properties, with the secondary structure class being much less mobile than the region of disorder. At the biological level, unlike the prediction of secondary structure, the prediction of intrinsic disorder has been revolutionary. That is, for many years, experimentalists have provided evidence that some proteins lack fixed structure or are disordered (or unfolded) under physiological conditions. Experimentalists further are showing that, for some proteins, functions depended on the unstructured rather than structured state. However, these examples have been mostly ignored. To our knowledge, not one disordered protein or disorder-associated function is discussed in any biochemistry textbook, even though such examples began to be discovered more than 50 years ago. Disorder prediction has been important for showing that the few experimentally characterized examples represent a very large cohort that is found all across all three domains of life. We now know that many significant biological functions depend directly on, or are importantly associated with, the unfolded or partially folded state. In this paper, we will briefly review some of the key discoveries that have occurred in the last decade, and, furthermore, will make a few highly speculative projections. ©2007 ieee.

关键词： Forecasting

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A Parallel Differential Evolution Algorithm for Parameter Estimation in Dynamic Models of Biological Systems 8

A Parallel Differential Evolution Algorithm for Parameter Es...

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8th International conference on Practical Applications of computational biology & bioinformatics (PACBB)

作者： Penas, D. R. Banga, Julio R. Gonzalez, P. Doallo, R. IIM CSIC BioProc Engn Grp Vigo Spain Univ A Coruna Comp Architecture Grp La Coruna Spain

ISBN: (纸本)9783319075815;9783319075808

Metaheuristics are gaining increased attention as efficient solvers for hard global optimization problems arising in bioinformatics and computational systems biology. Differential Evolution (DE) is one of the most popular algorithms in that class. However, the original algorithm requires many evaluations of the objective function, so its application to realistic computational systems biology problems, like those considering parameter estimation in dynamic models, results in excessive computation times. In this work we present a modified DE method which has been extended to exploit the structure of parameter estimation problems and which is able to run efficiently in parallel machines. In particular, we describe an asynchronous parallel implementation of DE which also incorporates three new search heuristics which exploit the structure of parameter estimation problems. The efficiency and robustness of the resulting method is illustrated with two types of benchmarks problems (i) black-box global optimization problems and (ii) calibration of systems biology dynamic models. The results show that the proposed algorithm achieves excellent results, not only in terms of quality of the solution, but also regarding speedup and scalability.

关键词： computational Systems biology Parallel Metaheuristics Differential Evolution

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Scalable Control of Asynchronous Boolean Networks 17th

Scalable Control of Asynchronous Boolean Networks

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17th International conference on computational Methods in Systems biology (CMSB)

作者： Su, Cui Paul, Soumya Pang, Jun Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Esch Sur Alzette Luxembourg Univ Luxembourg Fac Sci Technol & Commun Esch Sur Alzette Luxembourg

ISBN: (纸本)9783030313043;9783030313036

We summarise our recent research results on developing efficient and scalable control methods for gene regulatory networks modelled as asynchronous Boolean networks. Our methods compute a minimal subset of nodes of a given Boolean network, such that (different types of) perturbations of these nodes, in one step or a sequence of steps, can drive the network (from an initial state) to a target steady state.

关键词： Artificial intelligence

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A computational Environment for Long-Term Multi-Feature and Multi-Algorithm Seizure Prediction

A Computational Environment for Long-Term Multi-Feature and ...

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32nd Annual International conference of the ieee Engineering-in-Medicine-and-biology-Society (EMBC 10)

作者： Teixeira, C. A. Direito, B. Costa, R. P. Valderrama, M. Feldwisch-Drentrup, H. Nikolopoulos, S. Le Van Quyen, M. Schelter, B. Dourado, A. Univ Coimbra Ctr Informat & Syst CISUC P-3000 Coimbra Portugal Univ Edinburgh Neuroinformat Doctoral Training Ctr Inst Adapt & Neural Computat Sch Informat Edinburgh Midlothian EH8 9AB Scotland CNRS INSERM ICM Res Ctr UMRS975 Paris France Albert Ludwigs Univ Bernstein Ctr Computat Neurosci Freiburg Germany

ISBN: (纸本)9781424441242

The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors one should consider long-term recordings and several features and algorithms integrated in one software tool. A computational environment, based on Matlab (R), is presented, aiming to be an innovative tool for seizure prediction. It results from the need of a powerful and flexible tool for long-term EEG/ECG analysis by multiple features and algorithms. After being extracted, features can be subjected to several reduction and selection methods, and then used for prediction. The predictions can be conducted based on optimized thresholds or by applying computational intelligence methods. One important aspect is the integrated evaluation of the seizure prediction characteristic of the developed predictors.

关键词： ECG analysis EEG analysis Matlab computational environment computational intelligence methods electrocardiography electroencephalography epilepsy patient daily life feature extraction long-term multi-feature prediction long-term recordings medical computing medical disorders multi-algorithm seizure prediction seizure occurrence

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An efficient algorithm for scheduling problem with batching and common due window

An efficient algorithm for scheduling problem with batching ...

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2007 International conference on computational intelligence and Security Workshops, CIS 2007

作者： Hongluan, Zhao Guojun, Li Jiqiang, Wang Guoyong, Han School of Computer Science and Technology Shandong Jianzhu Univ. Jinan China School of Mathematics and System Science Shandong Univ. Jinan China Computational Systems and Biology Lab. Department of Biochemistry and Molecular Biology Univ. of Georgia United States School of Statistics and Mathematics Shandong Finance Inst. Jinan China

ISBN: (纸本)0769530737

Jobs share a common due window and belong to distinct families with sequence-independent setup tasks on a single machine. The window location and size are decision variables with linear penalties. Our objective is to find a schedule minimizing the sum of the weighted number of early and tardy jobs, together with the determination cost of the due window. Following several properties of an optimal schedule, a polynomial algorithm is firstly proposed and range it to P-problem. © 2007 ieee.

关键词： Scheduling algorithms

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computational methods in systems biology: 12th international conference, CMSB 2014 Manchester, UK, November 17-19, 2014 Proceedings 12

Computational methods in systems biology: 12th international...

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12th International conference on computational Methods in Systems biology, CMSB 2014

作者： Mendes, Pedro Dada, Joseph O. Smallbone, Kieran The University of Manchester Manchester Institute of Biotechnology 131 Princess Street Manchester United Kingdom

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SAGCN: Using Graph Convolutional Network With Subgraph-Aware for circRNA-Drug Sensitivity Identification

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 1765-1774页

作者： Sun, Weicheng Ren, Chengjuan Xu, Jinsheng Zhang, Ping Huazhong Agr Univ Coll informat Wuhan 430070 Peoples R China Sichuan Int Studies Univ Coll Language Intelligence Chongqing 400031 Peoples R China

Circular RNAs (circRNAs) play a significant role in cancer development and therapy resistance. There is substantial evidence indicating that the expression of circRNAs affects the sensitivity of cells to drugs. Identifying circRNAs-drug sensitivity association (CDA) is helpful for disease treatment and drug discovery. However, the identification of CDA through conventional biological experiments is both time-consuming and costly. Therefore, it is urgent to develop computational methods to predict CDA. In this study, we propose a new computational method, the subgraph-aware graph convolutional network (SAGCN), for predicting CDA. SAGCN first constructs a heterogeneous network composed of circRNA similarity network, drug similarity network, and circRNA-drug bipartite network. Then, a subgraph extractor is proposed to learn the latent subgraph structure of the heterogeneous network using a graph convolutional network. The extractor can capture 1-hop and 2-hop information and then a fusing attention mechanism is designed to integrate them adaptively. Simultaneously, a novel subgraph-aware attention mechanism is proposed to detect intrinsic subgraph structure. The final node feature representation is obtained to make the CDA prediction. Experimental results demonstrate that SAGCN obtained an average AUC of 0.9120 and AUPR of 0.8693, exceeding the performance of the most advanced models under 10-fold cross-validation. Case studies have demonstrated the potential of SAGCN in identifying associations between circRNA and drug sensitivity.

关键词： subgraph extractor subgraph perceptron CircRNAs-drug sensitivity association cancer treatment cancer treatment subgraph perceptron cancer treatment graph convolu- tional network

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Towards interactive exploration of images, meta-data, and analytic results in the open microscopy environment

Towards interactive exploration of images, meta-data, and an...

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2005 ieee computational Systems bioinformatics conference, Workshops and Poster Abstracts

作者： Hochheiser, Harry Goldberg, Ilya G. Laboratory of Genetics Image Informatics and Computational Biology Unit National Institute on Aging 333 Cassell Drive Baltimore MD 21224 United States

ISBN: (纸本)0769524427

The collection, management, and analysis of microscopy data present numerous informatics challenges. Appropriate user tools are needed to support acquisition, creation, analysis, annotation and interpretation of microscopy data. The Open Microscopy Environment (OME) is a database-driven system for the storage, analysis, management, and interpretation of microscopy data, An examination of these tasks in the context of regularly occurring structures in OME's data model motivates a framework for constructing these tools. Initial implementations of several components, linked together in a common environment, provide a partial realization of this framework. © 2005 ieee.

关键词： Microscopic examination

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AI in bioinformatics and Genomics

AI in Bioinformatics and Genomics

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2024 International conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Mukherjee, Sumita Thapliyal, Kavita Maurya, Alka Chaudhary, Naina Sudeshna, Sushree Amity University AIBS Noida India SIIB Pune India Amity University Tashkent Uzbekistan

ISBN: (纸本)9798350390049

Artificial intelligence (AI) has revolutionized various fields, including bioinformatics and genomics, by offering powerful tools and techniques to analyse and interpret complex biological data. This research paper explores the significant role of AI in bioinformatics and genomics, highlighting its applications, advancements, challenges, and future directions. Through a comprehensive review of relevant literature, case studies, and examples, and aims the power of AI transformation to provide insights on the study of biological systems, accelerating advancements in personalized medicine and biotechnology and enhancing our understanding of genomics. These areas have risen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research. Artificial intelligence (AI) has increasingly gained attention in bioinformatics research and computational molecular biology has become common for the researchers to apply the off-shelf systems with the availability of different types of AI algorithms. At present researchers are facing difficulties in choosing the best method that could be applied to a specific data set, with various intelligent methods available in the literature. The researchers need to apply machine learning and AI with different tools and techniques which present the data in an annotated with context, estimates of accuracy, proper predictions and explanation. comprehensible fashion. This article aims to review the use of AI in the areas of bioinformatics and computational molecular biology (DNA sequencing). The AI in functional genomics, and its application in this paper defines the importance of applicability and interpretability including ethical, legal and economic issues. © 2024 ieee.

关键词： accelerating advancements AI bioinformatics biotechnology complex biological data Genomics

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Searching for a single mathematical function to address the nonlinear retention time shifts problem in nanoLC-MS data: A fuzzy-evolutionary computational proteomics approach

Searching for a single mathematical function to address the ...

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2010 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

作者： Barton, Alan J. National Research Council Canada's Institute for Information Technology Ottawa ON Canada

ISBN: (纸本)9781424467662

Proteomics involves collecting and analyzing information about proteins within one or more complex samples in order to address a biological problem. One methodology is the use of high performance liquid chromatography coupled mass spectrometry (nanoLC-MS). In such a case, the accurate determination of non-linear peptide retention times between runs is expected to increase the number of identified peptides and hence, proteins. There are many approaches when using a computer for such a problem;including very interactive to completely non-interactive algorithms for finding global and local functions that may be either explicit or implicit. This paper extends previous work and explores finding an explicit global function for which two stages are involved: i) computation of a set of candidate functions (results) by the algorithm, and ii) searching within the set for patterns of interest. For the first stage, three classes of approximating global functions are considered: Class 1 functions that have a completely unknown structure, Class 2 functions that have a tiny amount of domain knowledge incorporated, and Class 3 functions that have a small amount of domain knowledge incorporated. For the second stage, some issues with current similarity measures for mathematical expressions are discussed and a new measure is proposed. Preliminary experimental results with an Evolutionary Computation algorithm called Gene Expression Programming (a variant of Genetic Programming) when used with a fuzzy membership within the fitness function are reported. © ieee.

关键词： Peptides

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