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

作者： Toh, Kok Sin Nguyen, Minh N. Rajapakse, Jagath C. IEEE BioInformatics Research Centre School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore

ISBN: (纸本)0780393872

Knowledge of subcellular localisation of proteins is Important in determining their function and Involvement in different pathways. A wide variety of methods has been proposed over the recent years in order to predict the subcellular localisation of proteins, mainly based on amino acid composition or single sequence inputs. We propose a Learning Vector Quantization (LVQ) method for protein subcellular localisation prediction based on N-terminal sorting signals by using the information derived from Amino Acid (AA) index database. The LVQ approach achieved overall prediction accuracies of 84.7% for 2427 eukaryotic protein sequences on Reinhardt and Hubbard dataset and upto 86.8% on the non-plant (eukaryotes) dataset of 2738 sequences from the TargetP website, which are comparable or better than the results of existing prediction methods. © 2005 ieee.

关键词： Proteins

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An integrative bioinformatics approach for identifying subtypes and subtype-specific drivers in cancer

An integrative bioinformatics approach for identifying subty...

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2012 ieee Symposium on computational intelligence and computational biology, CIBCB 2012

作者： Chen, Peikai Hung, Y.S. Fan, Yubo Wong, Stephen T.-C. University of Hong Kong Hong Kong Hong Kong Methodist Hospital Research Institute Houston TX 77030 United States

ISBN: (纸本)9781467311892

Cancer is a complex disease and within a cancer, subtypes of patients with distinct behaviors often exist. The subtypes might have been caused by different hits, such as copy number aberrations (CNAs) and point mutations, on different pathways/cells-of-origin in a common tissue/organ. Identifying the subtypes with subtype-specific drivers, i.e., hits, is key to the understanding of cancer and development of novel treatments. © 2012 ieee.

关键词： Diseases

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An Exact Algorithm for Sorting by Weighted Preserving Genome Rearrangements

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2019年第1期16卷 52-62页

作者： Hartmann, Tom Bernt, Matthias Middendorf, Martin Univ Leipzig Swarm Intelligence & Complex Syst Grp Fac Math & Comp Sci Augustuspl 10 D-04109 Leipzig Germany UFZ Helmholtz Ctr Environm Res Permoserstr 15 D-04318 Leipzig Germany

The preserving Genome Sorting Problem (pGSP) asks for a shortest sequence of rearrangement operations that transforms a given gene order into another given gene order by using rearrangement operations that preserve common intervals, i.e., groups of genes that form an interval in both given gene orders. The wpGSP is the weighted version of the problem were each type of rearrangement operation has a weight and a minimum weight sequence of rearrangement operations is sought. An exact algorithm - called CREx2 - is presented, which solves the wpGSP for arbitrary gene orders and the following types of rearrangement operations: inversions, transpositions, inverse transpositions, and tandem duplication random loss operations. CREx2 has a (worst case) exponential runtime, but a linear runtime for problem instances where the common intervals are organized in a linear structure. The efficiency of CREx2 and its usefulness for phylogenetic analysis is shown empirically for gene orders of fungal mitochondrial genomes.

关键词： Genome rearrangements transposition inversion tandem duplication random loss mitochondria fungi common interval

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EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and computational intelligence Approaches and Their Applications

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1645-1666页

作者： Gu, Xiaotong Cao, Zehong Jolfaei, Alireza Xu, Peng Wu, Dongrui Jung, Tzyy-Ping Lin, Chin-Teng Univ Tasmania Hobart Tas 7005 Australia Macquarie Univ N Ryde NSW 2109 Australia Univ Elect Sci & Technol China Chengdu 610054 Peoples R China Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China Univ Calif San Diego La Jolla CA 92093 USA Univ Technol Sydney Sydney NSW 2007 Australia

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.

关键词： Electroencephalography Brain Deep learning Sensors Monitoring Functional magnetic resonance imaging Entertainment industry

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bioinformatics for Healthcare Applications

Bioinformatics for Healthcare Applications

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Amity International conference on Artificial intelligence (AICAI)

作者： Majhi, Vinayak Paul, Sudip Jain, Rachna North Eastern Hill Univ Dept Biomed Engn Shillong Meghalaya India Univ Western Australia Sch Comp Sci & Software Engn Perth WA Australia Amity Univ Amity Inst Informat Technol Noida Uttar Pradesh India

ISBN: (纸本)9781538693469

bioinformatics is the combination of health information, data and knowledge. It uses computational techniques and tools to analyze the enormous biological data bases. The diseases such as metabolic disorders, urea cycle disorders, inborn errors and path-aligner can be identified at the early stage using various bioinformatics computational tools. These tools are used to process genetics and proteomics data and compare with health care data. Health care data consists of physiological data from various muscles or organs co acquiesced by using various electronic components, cost reports, bill claims and surveys related to patient satisfaction. Health care analysts are helpful to provide best health care at the lower price which is good for family economic system. This paper briefly explains the history applications of bioinformatics. The brief information about various kinds of computational tools and databases with respect to the application are mentioned. The current research trends in the bioinformatics are concluded.

关键词： bioinformatics Healthcare Engineering Healthcare Informatics

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Self-Supervised Learning for Label Sparsity in computational Drug Repositioning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第5期20卷 3245-3256页

作者： Yang, Xinxing Yang, Genke Chu, Jian Shanghai Jiao Tong Univ Ningbo Artificial Intelligence Inst Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

The computational drug repositioning aims to discover new uses for marketed drugs, which can accelerate the drug development process and play an important role in the existing drug discovery system. However, the number of validated drug-disease associations is scarce compared to the number of drugs and diseases in the real world. Too few labeled samples will make the classification model unable to learn effective latent factors of drugs, resulting in poor generalization performance. In this work, we propose a multi-task self-supervised learning framework for computational drug repositioning. The framework tackles label sparsity by learning a better drug representation. Specifically, we take the drug-disease association prediction problem as the main task, and the auxiliary task is to use data augmentation strategies and contrast learning to mine the internal relationships of the original drug features, so as to automatically learn a better drug representation without supervised labels. And through joint training, it is ensured that the auxiliary task can improve the prediction accuracy of the main task. More precisely, the auxiliary task improves drug representation and serving as additional regularization to improve generalization. Furthermore, we design a multi-input decoding network to improve the reconstruction ability of the autoencoder model. We evaluate our model using three real-world datasets. The experimental results demonstrate the effectiveness of the multi-task self-supervised learning framework, and its predictive ability is superior to the state-of-the-art model.

关键词： computational drug repositioning drug discovery label sparsity self-supervised learning

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Data Mining Supported Analysis of Medical Atomic Force Microscopy Images

Data Mining Supported Analysis of Medical Atomic Force Micro...

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ieee Symposium on computational intelligence in Healthcare and e-Health (CICARE)

作者： Hutterer, Stephan Mayr, Sandra Zauner, Gerald Silye, Rene Schilcher, Kurt Univ Appl Sci Upper Austria Sch Engn & Environm Sci Wels Austria Univ Appl Sci Upper Austria Sch Appl Hlth & Social Sci Linz Austria Neuropathic Hosp Wagner Jauregg Dept Pathol Linz Austria

ISBN: (纸本)9781467358835

Taking a look at actual developments in the field of bioinformatics, computer support in different fields of medicine and biology is an increasing field of interest. Especially for recognition and classification of various kinds of diseases, researchers already identified the usage of data mining techniques as supporting tool for computer supported analysis and diagnosis. In order to build such tools, highly accurate and machine-readable data is needed. Therefore, atomic force microscopy (AFM) is shown within this paper as measurement technology, that provides true 3-D data of any tissue and thus is able to build the fundament for further computational processing steps. Within two practical examples dealing with brain tumor tissue on the one hand and myocardial muscle tissue on the other hand, data mining supported analysis of AFM images will be demonstrated. The combination of data mining techniques with AFM measurements at nanoscale therefore forms a promising fundament for future computer enabled support systems in medicine and biology.

关键词： atomic force microscopy bioinformatics biomedical optical imaging brain computer aided analysis data mining diseases image classification medical image processing muscle pattern recognition tumours

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An ontology design methodology for Knowledge-Based systems with application to bioinformatics

An ontology design methodology for Knowledge-Based systems w...

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2012 ieee Symposium on computational intelligence and computational biology, CIBCB 2012

作者： Fiannaca, Antonino La Rosa, Massimo Rizzo, Riccardo Urso, Alfonso Gaglio, Salvatore ICAR-CNR National Research Council of Italy Viale delle Scienze Ed. 11 90128 Palermo Italy DICGIM University of Palermo Viale delle Scienze Ed. 6 90128 Palermo Italy

ISBN: (纸本)9781467311892

Ontologies are formal knowledge representation models. Knowledge organization is a fundamental requirement in order to develop Knowledge-Based systems. In this paper we present Data-Problem-Solver (DPS) approach, a new ontological paradigm that allows the knowledge designer to model and represent a Knowledge Base (KB) for expert systems. Our approach clearly distinguishes among the knowledge about a problem to resolve (answering the what to do question), the solver method to resolve it (answering the how to do question) and the type of input data required (answering the what I need question). The main purpose of the proposed paradigm is to facilitate the generalization of the application domain and the modularity and the expandability of the represented knowledge. The proposed DPS ontological approach is applied to the modelling of the knowledge about a bioinformatics application scenario: the protein complex extraction from a protein-protein interaction network. © 2012 ieee.

关键词： Expert systems

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Multiobjective Optimization Applied to the Eradication of Persistent Pathogens

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ieee TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2010年第5期14卷 759-765页

作者： Steuernagel, Ole Polani, Daniel Univ Hertfordshire Sch Phys Astron & Math Hatfield AL10 9AB Herts England Univ Hertfordshire Sch Comp Sci Hatfield AL10 9AB Herts England

In scenarios such as therapeutic modeling or pest control, one aims to suppress infective agents or maximize crop yields while minimizing the side-effects of interventions, such as cost, environmental impact, and toxicity. Here, we consider the eradication of persistent microbes (e. g., Escherichia coli, multiply resistant Staphylococcus aureus (MRSA-"superbug"), Mycobacterium tuberculosis, Pseudomonas aeruginosa) through medication. Such microbe populations consist of metabolically active and metabolically inactive (persistent) subpopulations. It turns out that, for efficient medication strategies, the two goals, eradication of active bacteria on one hand and eradication of inactive bacteria on the other, are in conflict. Using multiobjective optimization, we obtain a survey of the full spectrum of best solutions. We find that, if treatment time is limited and the total medication dose is constant, the application of the medication should be concentrated both at the beginning and end of the treatment. If the treatment time is increased, the medication should become increasingly spread out over the treatment period until it is uniformly spread over the entire period. The transition between short and long overall treatment times sees optimal medication strategies clustered into groups.

关键词： Biochemistry biological systems biology biomedical chemistry computational bioinformatics computational intelligence ecology evolutionary biology evolvable hardware game theory mathematics

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Gene - Nutrition Interactions in the Onset of Obesity as Cardiovascular Disease Risk Factor based on a computational intelligence Method

Gene - Nutrition Interactions in the Onset of Obesity as Car...

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8th ieee International conference on bioinformatics and Bioengineering

作者： Valavanis, Ioannis K. Mougiakakou, Stavroula G. Marinos, Stathis Karkalis, George Grimaldi, Keith A. Gill, Rosalynn Nikita, Konstantina S. Natl Tech Univ Athens Sch Elect & Comp Engn 9 Heroon Polytech Str Zografos 15780 Greece

ISBN: (纸本)9781424428441

Identification of gene-gene and gene-environment interactions that contribute in the onset of a multi-factorial disease supports the prevention of diseases like the Cardiovascular Disease (CVD). Body Mass Index (BMI), a measure of human obesity, is an independent risk factor of CVD. Furthermore, it is known that a subject's BMI is affected both by his/her lifestyle, e.g. nutrition, and genetic profile. Aim of the paper is to predict a subject's onset of obesity using lifestyle and genetic information. The prediction is performed by a computational intelligence based system using a Parameter Decreasing Method (PDM) combined with an Artificial Neural Network (ANN). The system uses an initial set of 63 input variables corresponding to sex, average nutrition intake measurements, and genetic variations to identify the 32 most important ones that affect BMI. The selected variables are the ones to interact with each other towards the complex trait of BMI, which is used as a 2-class output variable (BMI <= 25 vs. BMI>25) in the ANN. The system achieved a mean accuracy of the system evaluated by a 3-cross validation resampling technique equal to 77.89%.

关键词： Genes

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