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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第3期19卷 1255-1256页

作者： Kwok-Wing Tsui, Stephen Chinese Univ Hong Kong Sch Biomed Sci Shatin Hong Kong Peoples R China

This special section of ieee/ACM Transactions on computational biology and bioinformatics (TCBB) is a collection of papers presented at the 18th Asia Pacific bioinformatics conference (APBC2020), which was a virtual c... 详细信息

关键词： Special Issues And Sections bioinformatics Meetings

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Volumetric Representation of Biomolecules Using Cell Decomposition and Robotics 16

Volumetric Representation of Biomolecules Using Cell Decompo...

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16th ieee International conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Chakraborty, Arindam Gohil, Mahendra Kumar Savitha, G. Dasgupta, Bhaskar Indian Inst Technol Kanpur Ctr Mechatron Mech Engn Kanpur 208016 Uttar Pradesh India Banasthali Vidyapith Fac Biophys & Mol Dynam Vanasthali 304022 Rajasthan India Indian Inst Technol Kanpur Ctr Mechatron Fac Mech Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781728114620

Biological macro-molecules come in a large variety of complexes. Representation and modelling their structures are essential to provide a computational analogue of experimentally determined structures. In this work, the authors propose a representational scheme for biomolecules with the aid of approximate cell decomposition and a nano-robotic schema. The kino-chemical information of a molecule is programmatically retrieved from reliable repositories (RLooM, Protein data bank etc.) of biochemical structures and a Nano-manipulator schema is obtained from the D-H representation of the molecular chain under consideration. The molecule is enclosed in a suitable bounding box, and using approximate cell decomposition in conjunction with bonding information, a volumetric steric clash description of the chain is proposed which serves as a model for infringement function estimation and subsequent structural prediction for proteins and nucleic acids. From the robotic representation, the variable kinematic description due to rotating atoms is always at hand to generate the new corresponding infringement description within the bounding *** of appropriately indexed cells give a reasonably accurate estimate of infringement in a particular configuration and the estimates are found to be comparable to experimental results. The proposed rendering have been extensively compared with a number of professional software renderings from NMR and crystallographic data.

关键词： Biological system modeling Molecular biophysics Kinematics computational modeling Proteins Manipulators

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Evolving Graph Compression using Similarity Measures for bioinformatics Applications 13

Evolving Graph Compression using Similarity Measures for Bio...

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13th ieee Annual conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Brown, Joseph Alexander Houghten, Sheridan Collins, Tyler Kennedy Qu, Qiang Innopolis Univ Inst Informat Syst Innopolis Russia Brock Univ Dept Comp Sci St Catharines ON Canada

ISBN: (纸本)9781467394727

Many real-world graphs, including those storing various forms of biological data, are of such large size that storing and processing their information has too high a cost. As a result, one possible solution is to compress the graphs by merging nodes into supernodes. This study introduces a genetic algorithm for graph compression that is based on the similarity of nodes, where two nodes are considered similar if a high proportion of their neighbours are in common. The methodology was applied to three real-world graphs storing widely varying data, as well as the gene regulatory network of E. coli. This study used a fixed compression rate of 25 % as a target for the graphs. Results for a parameter study of variation operators exhibit a strong preference for crossover, in comparison to mutation which was found to be disruptive to graph structure.

关键词： Escherichia coli

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Accelerating Protein Tertiary Structure Analysis Based on FPGA

Accelerating Protein Tertiary Structure Analysis Based on FP...

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10th Annual ieee Symposium on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Shen Quanhua Xia Fei Zhu Qianghua Naval Univ Engn Elect Engn Coll Wuhan 430033 Peoples R China

ISBN: (纸本)9781467358750

In the era of tertiary protein structure prediction, the most successful protein structure prediction methods involve sampling protein conformations which is a computational problem due to the increasing scale of the protein database. Recently FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design. In this paper, we propose a complete fine-grained parallel hardware implementation on FPGA XC5VLX330 chip to accelerate the BackboneDBN program for sampling realistic protein conformations using local structural bias. The experimental results show a speedup factor of more than 20x over software version running on a PC platform with Intel E7400 dual-core. However, the FPGA's power consumption is only about 30% of that of current general-purpose CPUs.

关键词： bioinformatics biological techniques field programmable gate arrays lab-on-a-chip molecular configurations parallel algorithms proteins

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Predicting and Characterizing Legal Claims of Hospitals with computational intelligence: the Legal and Ethical Implications

Predicting and Characterizing Legal Claims of Hospitals with...

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

作者： Gallese, Chiara Fuchs, Caro Riva, Simone G. Foglia, Emanuela Schettini, Fabrizio Ferrario, Lucrezia Falletti, Elena Nobile, Marco S. LIUC Carlo Cattaneo Univ Sch Ind Engn Castellanza VA Italy Eindhoven Univ Technol Jheronimus Acad Data Sci Shertogenbosch Netherlands Univ Oxford Weatherall Inst Mol Med Radcliffe Dept Med Oxford England LIUC Carlo Cattaneo Univ Ctr Hlth Econ Social & Healthcare Management Castellanza VA Italy LIUC Carlo Cattaneo Univ Sch Law Castellanza VA Italy Ca Foscari Univ Venice Dept Environm Sci Informat & Stat Venice Italy

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

In this paper we propose a fuzzy logic-based approach to analyze UK National Health Service (NHS) public administrative data related to pre- and post-pandemic claims filed by patients, analyzing the legal and ethical issues connected to the use of Artificial intelligence systems, including our own, to take critical decisions having a significant impact on patients, such as employing computational intelligence to justify the management choices related to Intensive Care Unit (ICU) bed allocation. Differently from previous papers, in this work we follow an unsupervised approach and, specifically, we perform an analysis of UK hospitals by means of a computational intelligence algorithm integrating Fuzzy C- Means and swarm intelligence. The dataset that we analyse allows us to compare pre- and post-pandemic data, to analyze the ethical and legal challenges of the use of computational intelligence for critical decision-making in the health care field.

关键词： computational intelligence AI and Law AI Ethics Health Care Covid-19 Fuzzy Logic

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Special Section on Swarm-Based Algorithms and Applications in computational biology and bioinformatics

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第6期15卷 1863-1864页

作者： Tan, Ying Shi, Yuhui Peking Univ Sch EECS Dept Machine Intelligence Key Lab Machine PerceptMOE Beijing 100871 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

The seven papers in this special section were presented at the ICSI 2016 conference. These articles are primarily dealing with either novel bioinspired swarm intelligence algorithms and their improvements aswell as some practical applications inmulti-objective optimization, network community detection, curve fitting, and swarm robotics, etc.

关键词： Special Issues And Sections Particle Swarm Optimization Bio Inspired Computing Evolutionary Computation Meetings

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AI Deployment in Medical Devices- Ethical and Regulaltory Reflections, Beyond Data Protection and Bias - EU perspective 20

AI Deployment in Medical Devices- Ethical and Regulaltory Re...

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20th ieee conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Braun, Tomasz Harasimiuk, Dominika Ewa Lazarski Univ Fac Law & Adm Warsaw Poland Univ Warsaw Fac Polit Sci & Int Studies Warsaw Poland

ISBN: (纸本)9798350310177

Legal regulations do not eliminate existing ethical problems related to the use of modern technology in medical devices for disease diagnosis and treatment. Among these problems, some are already well analyzed in the literature - they concern the protection of personal data and the occurrence of bias and discrimination. In addition to these, however, there are several other, no less important issues involving the application of artificial intelligence technologies to medical devices. They concern how the technologies used in these devices for the purpose of diagnosis and therapy may replace human intervention and what evaluation criteria they should therefore be subject to, as well as what risks they entail.

关键词： Artificial intelligence New technologies Medical devices Ethical dilemmas Accuracy of diagnosis Reliability of AI Consistency

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An Automated Approach to Translate A Biological Process from ODEs into Graphical Hybrid Functional Petri Nets

An Automated Approach to Translate A Biological Process from...

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

作者： Mecheter, Imene Hadjidj, Rachid Foufou, Sebti Qatar Univ Comp Sci & Engn Dept Doha Qatar

ISBN: (纸本)9781467389884

The study of biological systems is growing rapidly, and can be considered as an intrinsic task in biological research, and a prerequisite for diagnosing diseases and drug development. The integration of biological studies with computer technologies led to noticeable developments in biology with the appearance of many powerful modeling and simulation techniques and tools. The help of computers in biology resulted in deeper knowledge about complex biological systems and biopathways behaviors. Among modeling tools, the Petri Net formalism plays an important role. Petri Net is a powerful computerized and graphical modeling technique originally developed by Carl Adam Petri in 1960 to model discrete event systems. With its various extensions, Petri Nets find applications in many other fields including biology. The extension known under the name Hybrid Functional Petri Net (HFPN) was developed specifically to model biological systems. Traditionally, biological processes are captured as systems of ordinary differential equations (ODEs). However, HFPNs offer a much more elegant and versatile approach to represent these processes more accurately. In fact, HFPNs allow to capture phenomena which are impossible to capture with ODES, while being more intuitive and easy to understand and model with. In this work we propose an approach to translate a system of ODEs representing a biological process into a HFPN. The resulting HFPN, not only preserves the semantics of the original model, but is also more humanly readable thanks to the use of a novel technique to connect its components in a smart way. To validate our approach, we implemented it as an extension to the tool Real Time Studio (an integrated environment for modeling, simulation and automatic verification of real-time systems), and compared our simulation results with those obtained by simulating systems of ODEs using MATLAB.

关键词： Petri nets Biological system modeling computational modeling Mathematical model Biological systems Differential equations

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computational intelligence for Metabolic Pathway Design: Application to the Pentose Phosphate Pathway 13

Computational Intelligence for Metabolic Pathway Design: App...

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13th ieee Annual conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Skelton, D. J. Hallinan, J. S. Park, S. Wipat, A. Newcastle Univ Sch Comp Sci Newcastle Upon Tyne Tyne & Wear England Macquarie Univ Dept Biol Sci Sydney NSW Australia Newcastle Univ Ctr Bacterial Cell Biol Sch Comp Sci Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Ctr Hlth & Bioinformat Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781467394727

Metabolic engineering is increasingly being used for the production of industrial products such as pharmaceuticals and enzymes. These chemicals have traditionally been chemically synthesized, but the application of synthetic biology techniques to microbes facilitates faster, cheaper production. Modelling and the integration of existing data can help inform the design of synthetic pathways. We applied an evolutionary algorithm to a flux balance model of metabolism in the industrially important bacterium Bacillus subtilis. Our target metabolites are sedoheptulose- 7-phosphate and riboflavin, components of the pentose phosphate pathway. The algorithm combines the results of the flux balance analysis with phylogenetic information derived from data warehouses, to predict several potential interventions to the metabolic network, mostly involving knockouts of genes related to the pathway.

关键词： Evolutionary computation data integration metabolic engineering genomics synthetic biology

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Incorporating Prior Expert Knowledge In Learning Bayesian Networks From Genetic Epidemiological Data

Incorporating Prior Expert Knowledge In Learning Bayesian Ne...

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ieee conference on computational intelligence in bioinformatics and computational biology

作者： Su, Chengwei Borsuk, Mark E. Andrew, Angeline Karagas, Margaret Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Community & Family Med Sect Biostatist & Epidemiol Geisel Sch Med Lebanon NH USA

ISBN: (纸本)9781479945351

We consider the applicability of Bayesian networks (BNs) for discovering relations between genes, environment, and disease. Most state-of-the-art BN structure learning algorithms are not capable of learning structures from data containing missing values, which is a norm in genetic epidemiological data. In addition, there exists a wealth of existing prior knowledge which could be incorporated to improve computational efficiency in BN structure learning. To address these challenges, we applied a Markov chain Monte Carlo based BN structure learning algorithm to data from a population-based study of bladder cancer in New Hampshire, USA. A large improvement in computational efficiency is achieved under this approach.

关键词： Bayesian network genetic epidemiology Markov chain Monte Carlo complex traits bioinformatics

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