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ieee TRANSACTIONS ON NANOBIOSCIENCE 2016年第4期15卷 306-308页

作者： Hu, Xiaohua Huang, Yufei Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA

bioinformatics and biomedicine research is fundamental to our understanding of complex biological systems, impacting the science and technology of fields ranging from agricultural and environmental sciences to pharmaceutical and medical sciences. This type of research requires close collaboration among multidisciplinary teams of researchers in computer science, statistics, physics, engineering, life sciences and medical sciences, and their interfaces. The ieee International conference on bioinformatics and Biomedicine (BIBM) aims to provide an open and interactive forum to catalyze the cross-fertilization of ideas from these disciplines and to bridge our knowledge gaps.

关键词： Special issues and sections Meetings bioinformatics Genomics computational systems biology Biomedical informatics

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Emergent technology technical committee

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ieee computational intelligence Magazine 2007年第4期2卷 5-8页

作者： Sendhoff, Bernhard Honda Research Institute Europe GmbH Germany

The role of the Emergent Technology Technical Committee (ETTC) within the computational intelligence Society of ieee is to identify early and promote new directions in research within the scope of the CIS to ensure that ieee CIS can provide the best service in these areas as promptly as possible. The TFs are regarded as incubators for new technology technical committees within ieee CIS. The majority of new research areas cannot be clearly classified into one of the existing traditional scientific disciplines. Modern technologies are inherently hybrid and cross-disciplinary. computational Systems biology uses computer simulations to model biological systems in a holistic way, thereby taking the networked nonlinear interactions between a large number of heterogeneous components into account. Social Computing transfers the thinking in social interactions, contexts and conventions, as well as group dynamics and collaborative developments into computer systems.

关键词： bioinformatics

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Managing Memory and Reducing I/O Cost for Correlation Matrix Calculation in bioinformatics

Managing Memory and Reducing I/O Cost for Correlation Matrix...

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

作者： Krishnajith, Anaththa P. D. Kelly, Wayne Hayward, Ross Tian, Yu-Chu Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld 4001 Australia

ISBN: (纸本)9781467358750

The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.

关键词： Correlation matrix bioinformatics computing scalable computing memory management phylogenetic analysis

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Application of computational intelligence to biology 1

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丛书名： SpringerBriefs in Applied Sciences and Technology

2016年

作者： Ravi Bhramaramba, Akula Chandra Sekhar (eds.)

ISBN: (数字)9789811003912

ISBN: (纸本)9789811003905;9789811003912

This book is a contribution of translational and allied research to the proceedings of the International conference on computational intelligence and Soft Computing. It explains how various computational intelligence techniques can be applied to investigate various biological problems. It is a good read for Research Scholars, Engineers, Medical Doctors and bioinformatics researchers.

关键词： computational intelligence computational and Systems biology Diseases Proteins Biomedical Engineering and Bioengineering Health Informatics

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The Business Machine in biology-The Commercialization of AI in the Life Sciences

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ieee ANNALS OF THE HISTORY OF COMPUTING 2022年第1期44卷 8-18页

作者： Stevens, Hallam Nanyang Technol Univ Singapore 639798 Singapore

This article traces one important trajectory in the history of expert systems. Through a collaboration between Edward Feigenbaum and the geneticist Joshua Lederberg, Nobel Laureate in Medicine, AI became deeply connected to the life sciences. biology was a crucial test bed for some of Feigenbaum's systems and, in the long term, these systems had a transformative effect on biology. In particular, the work of Feigenbaum and his collaborators and students brought biology and computing together in especially powerful ways. We now take for granted that biology can be computerized-we have whole subdisciplines such as bioinformatics, biocomputing, and computational biology devoted to the task of studying life as information. The computer systems and software that Feigenbaum's lab helped to develop played an important role in establishing the possibility of these kinds of work.

关键词： Artificial intelligence Expert systems History biology Business Presses Life sciences expert systems Edward A Feigenbaum IntelliGenetics IntelliCorp Teknowledge Joshua Lederberg MOLGEN

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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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bioinformatics: Implementation of a proposed upgraded smith-waterman algorithm for local alignment

Bioinformatics: Implementation of a proposed upgraded smith-...

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2009 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2009

作者： Sadi, Md. Shihabuddin Mohammad Sami, AbuZ. Uddin Ahmed, Imtiaz Ruhunnabi, Abdullahil Baki Md. Das, Nirmalendu University of Science and Technology

ISBN: (纸本)9781424427567

This paper presents a new algorithm for local alignment search which has less computational complexity than the Smith- Waterman algorithm. Increasing the accuracy of sequence matching and reducing computational complexity is a grand challenge to bioinformatics computing. The proposed Algorithm is a more competent local alignment searching Algorithm since it reduces running time well enough to search a database in order to find. © 2009 ieee.

关键词： bioinformatics

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Guest Editorial: Introduction to the Special Issue on bioinformatics Research and Applications

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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ieee TRANSACTIONS ON NANOBIOSCIENCE 2016年第2期15卷 74-76页

作者： He, Jing Li, Yaohang Zelikovsk, Alexander Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA Old Dominion Univ Comp Sci Norfolk VA 23529 USA Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

This Special Issue includes a selection of papers presented at the 11th International Symposium on bioinformatics Research and Application (ISBRA), which was held at Old Dominion University in Norfolk, VA, USA, on May 7-10, 2015. The ISBRA symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications. In 2015, 98 papers were submitted in response to the call for papers, out of which 12 papers were invited to submit extended versions of their conference abstracts to this Special Issue. Selected papers illustrate the variety of applications that computational methods find in the field of nanobioscience, ranging from protein classification to de novo sequencing. Furthermore, selected papers convincingly demonstrate the central role played by computational methods in contemporary nanobioscience research - a role that is bound only to increase in the future. The Guest Editors then briefly describe each of 12 accepted papers.

关键词： Special issues and sections Meetings bioinformatics

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computational prediction of replication origins in herpesviruses

Computational prediction of replication origins in herpesvir...

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ieee Symposium on computational intelligence in bioinformatics and computational biology

作者： Cruz-Cano, Raul Chandran, Deepak Leung, Ming-Ying Univ Texas Bioinformat Program 500 W Univ Ave El Paso TX 79968 USA Univ Texas Dept Elect & Comp Engn El Paso TX 79968 USA Univ Texas Dept Math Sci El Paso TX 79968 USA

ISBN: (纸本)9781424407101

computational methods for replication origin prediction in individual herpesvirus genomes have been previously devised based on the locations of high concentrations of palindromes. In order to make use of similarities in genome composition and organization of related herpesviruses, an artificial neural network approach is explored. We implement feed-forward artificial neural networks trained by 17 input variables comprising the positions of known replication origins relative to the genome lengths and the dinucleotide scores. The overall prediction accuracy of the neural network approach for our data set is better than that of the palindrome based approach. Furthermore, suitable combinations of the prediction results given by the two approaches substantially increase the prediction accuracy achieved by either method applied individually.

关键词： Forecasting

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Comparing Interpretable AI Approaches for the Clinical Environment: an Application to COVID-19

Comparing Interpretable AI Approaches for the Clinical Envir...

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

作者： Onari, Mohsen Abbaspour Nobile, Marco S. Grau, Isel Fuchs, Caro Zhang, Yingqian Boer, Arjen-Kars Scharnhorst, Volkher Eindhoven Univ Technol Informat Syst Grp Eindhoven Artificial Intelligence Syst Inst Eindhoven Netherlands Ca Foscari Univ Environm Sci Informat & Stat Venice Italy Eindhoven Univ Technol Informat Syst Grp Eindhoven Netherlands Catharina Hosp Clin Chem Eindhoven Netherlands Eindhoven Univ Technol Biomed Engn Eindhoven Netherlands

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

Machine Learning (ML) models play an important role in healthcare thanks to their remarkable performance in predicting complex phenomena. During the COVID-19 pandemic, different ML models were implemented to support decisions in the medical settings. However, clinical experts need to ensure that these models are valid, provide clinically useful information, and are implemented and used correctly. In this vein, they need to understand the logic behind the models to be able to trust them. Hence, developing transparent and interpretable models has increasing relevance. In this work, we applied four interpretable ML models including logistic regression, decision tree, pyFUME, and RIPPER to classify suspected COVID-19 patients based on clinical data collected from blood samples. After preprocessing the data set and training the models, we evaluate the models based on their predictive performance. Then, we illustrate that interpretability can be achieved in different ways. First, SHAP explanations are built from logistic regression and decision trees to obtain the features' importance. Then, the potential of pyFUME and RIPPER in providing inherent interpretability are reflected. Finally, potential ways to achieve trust in future studies are briefly discussed.

关键词： Machine Learning Explainable Artificial intelligence Interpretability Trust COVID-19

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