Abstract：
:Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research. In this work we present several multi-classifiers that combine the predictions of the best current classifiers available on Internet. Our results prove that combining the predictions of a set of classifiers by creating composite classifiers is a fruitful one. We have created multi-classifiers that are more accurate than any of the component classifiers. The multi-classifiers are based on Bayesian networks. They are validated with 9 different datasets. Their predictive accuracy results outperform the best secondary structure predictors by 1.21% on average. Our main contributions are: (i) we improved the best know predictive accuracy by 1.21%, (ii) our best results have been obtained with a new semi naïve Bayes approach named Pazzani-EDA and (iii) our multi-classifiers combine results of previously build classifiers predictions obtained through Internet, thanks to our development of a Java application.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Robles V,Larrañaga P,Peña JM,Menasalvas E,Pérez MS,Herves V,Wasilewska Adoi
10.1016/j.artmed.2004.01.009subject
Has Abstractpub_date
2004-06-01 00:00:00pages
117-36issue
2eissn
0933-3657issn
1873-2860pii
S0933365704000326journal_volume
31pub_type
杂志文章abstract：:We introduce a novel approach to couple temporal similarity with spatial neighborhood information. This is achieved by concatenating the K nearest, spatially contiguous neighbors of a pixel time-course (TC) of T time-instances. This produces a new TC of (K+1)T time instances. Depending on how "nearest" is defined, we ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(02)00005-2
更新日期：2002-05-01 00:00:00
abstract：:The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neurological disorders. This work proposes a pairwise distance learning approach for schizophrenia classification relying on the spectral pr...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2020.101852
更新日期：2020-05-01 00:00:00
abstract：:Hospital laboratories perform examination tests upon patients, in order to assist medical diagnosis or therapy progress. Planning and scheduling patient requests for examination tests is a complicated problem because it concerns both minimization of patient stay in hospital and maximization of laboratory resources uti...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(00)00061-0
更新日期：2000-10-01 00:00:00
abstract：OBJECTIVE:Recently, fuzzy soft sets-based decision making has attracted more and more interest. Although plenty of works have been done, they cannot provide the uncertainty or certainty of their results. To manage uncertainty is one of the most important and toughest tasks of decision making especially in medicine. In ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2016.04.004
更新日期：2016-05-01 00:00:00
abstract：OBJECTIVE:In this paper a new nonlinear system identification approach is developed for dynamical quantification of cardiovascular regulation. This approach is specifically focused on the identification of the heart rate (HR) baroreflex mechanism. The principal objective of this paper is to improve the model accuracy i...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2011.01.002
更新日期：2011-05-01 00:00:00
abstract：OBJECTIVE:Is it possible to predict the severity staging of a Parkinson's disease (PD) patient using scores of non-motor symptoms? This is the kickoff question for a machine learning approach to classify two widely known PD severity indexes using individual tests from a broad set of non-motor PD clinical scales only. ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2013.04.002
更新日期：2013-07-01 00:00:00
abstract：OBJECTIVE:The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing int...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2006.08.005
更新日期：2007-02-01 00:00:00
abstract：:The automated analysis of retinal images is a widely researched area which can help to diagnose several diseases like diabetic retinopathy in early stages of the disease. More specifically, separation of vessels and lesions is very critical as features of these structures are directly related to the diagnosis and trea...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.07.010
更新日期：2019-08-01 00:00:00
abstract：OBJECTIVE:In this paper, we extend a preliminary proposal and discuss in a deeper and more formal way an approach to evaluate temporal similarity between clinical workflow cases (i.e., executions of clinical processes). More precisely, we focus on (i) the representation of clinical processes by using a temporal concept...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.07.013
更新日期：2009-05-01 00:00:00
abstract：:Rough sets (Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data, Dordrecht: Kluwer Academic Publishers, 1991) is a relatively new approach to representing and reasoning with incomplete and uncertain knowledge. This article introduces the basic concepts of rough sets and Boolean reasoning (Brown FM. Boole...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(98)00051-7
更新日期：1999-02-01 00:00:00
abstract：:As part of a plan to promote semi-automatic knowledge acquisition for the medical consultant system CADIAG-II/RHEUMA, this study sought to explore and cope with the variability of results that may be anticipated when performing knowledge acquisition with patient data from different patient settings. Patient data were ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(02)00025-8
更新日期：2002-07-01 00:00:00
abstract：:The decompressive laminectomy is a common operation for treatment of lumbar spinal stenosis. The tools for grinding and drilling are used for fenestration and internal fixation, respectively. The state recognition is one of the main technologies in robot-assisted surgery, especially in tele-surgery, because surgeons h...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.101763
更新日期：2020-01-01 00:00:00
abstract：:Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be ut...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(98)00057-8
更新日期：1999-03-01 00:00:00
abstract：OBJECTIVE:Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2014.06.003
更新日期：2014-09-01 00:00:00
abstract：:In this paper, we propose an approach for managing clinical guidelines. We outline a modular architecture, allowing us to separate two conceptually distinct aspects: the representation (and acquisition) of clinical guidelines and their execution. We propose an expressive formalism, which allows one to deal with the co...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章,评审
doi：10.1016/s0933-3657(01)00087-2
更新日期：2001-11-01 00:00:00
abstract：:Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2020.101950
更新日期：2020-08-01 00:00:00
abstract：:Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer p...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.101706
更新日期：2019-09-01 00:00:00
abstract：OBJECTIVE:Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The ma...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.07.014
更新日期：2009-02-01 00:00:00
abstract：OBJECTIVE:We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. METHODS:Both time and f...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2016.01.005
更新日期：2016-02-01 00:00:00
abstract：OBJECTIVE:High dose radiation has been well known for increasing the risk of carcinogenesis. However, the understanding of biological effects of low dose radiation is limited. Low dose radiation is reported to affect several signaling pathways including deoxyribonucleic acid repair, survival, cell cycle, cell growth, a...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2010.04.001
更新日期：2010-07-01 00:00:00
abstract：OBJECTIVE:Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2004.07.004
更新日期：2005-06-01 00:00:00
abstract：:This paper presents a computer-based model for differential diagnosis of specific language impairment (SLI), a language disorder that, in many cases, cannot be easily diagnosed. This difficulty necessitates the development of a methodology to assist the speech therapist in the diagnostic process. The methodology tool ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(02)00076-3
更新日期：2003-11-01 00:00:00
abstract：:As a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2019.101783
更新日期：2020-03-01 00:00:00
abstract：:Multichannel transcranial magnetic stimulation (mTMS) is a therapeutic method to improve psychiatric diseases, which has a flexible working pattern used to different applications. In order to make the electric field distribution in the brain meet the treatment expectations, we have developed a novel multi-swam particl...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2020.101790
更新日期：2020-04-01 00:00:00
abstract：:In this contribution we present an application of a knowledge-based neural network technique in the domain of medical research. We consider the crucial problem of intensive care patients developing a septic shock during their stay at the intensive care unit. Septic shock is of prime importance in intensive care medici...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/s0933-3657(03)00057-5
更新日期：2003-06-01 00:00:00
abstract：OBJECTIVE:Most practically deployed Arden-Syntax-based clinical decision support (CDS) modules process data from individual patients. The specification of Arden Syntax, however, would in principle also support multi-patient CDS. The patient data management system (PDMS) at our local intensive care units does not native...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2015.09.009
更新日期：2018-11-01 00:00:00
abstract：OBJECTIVE:One of the major topics towards robot consciousness is to give a robot the capabilities of self-consciousness. We propose that robot self-consciousness is based on higher order perception of the robot, in the sense that first-order robot perception is the immediate perception of the outer world, while higher ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2008.07.003
更新日期：2008-10-01 00:00:00
abstract：BACKGROUND:After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more inform...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2016.01.001
更新日期：2016-02-01 00:00:00
abstract：OBJECTIVE:This work presents a system for a simultaneous non-invasive estimate of the blood glucose level (BGL) and the systolic (SBP) and diastolic (DBP) blood pressure, using a photoplethysmograph (PPG) and machine learning techniques. The method is independent of the person whose values are being measured and does n...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/j.artmed.2011.05.001
更新日期：2011-10-01 00:00:00
abstract：:INTERNIST-I was an expert system designed in the early 1970's to diagnose multiple diseases in internal medicine by modelling the behaviour of clinicians. Its form and operation are described, and evaluations of the system are surveyed. The major result of the project was its knowledge base which has been used in succ...
journal_title：Artificial intelligence in medicine
pub_type： 杂志文章
doi：10.1016/0933-3657(94)00028-q
更新日期：1995-04-01 00:00:00
