Detecting conserved coding genomic regions through signal processing of nucleotide substitution patterns.

Abstract:

OBJECTIVE:In the last few years several complete genome sequences have been made available to the research community. The annotation of their complete inventory of protein coding genes, however, has been so far an elusive goal. Classical ab initio gene prediction methods have been of great support for this task, but show notable weakness in the prediction of genes with unusual structural features. On the other hand, annotation on the basis of similarity to already known genes in other species does not permit the detection of genuinely novel genes and also introduces a potential source of classification error when based on similarity to sequences erroneously annotated as protein coding. Finally, several methods for the functional classification and assessment of evolutionarily conserved regions have been proposed, but, to our knowledge, signal processing techniques have not been applied yet to this problem, despite their proven usefulness at the single genome level. RESULTS:In this article we introduce the use of signal processing in comparative genomics and we propose a simple test able to evaluate the coding potential of a pairwise genomic sequence alignment according to the pattern and periodicity with which substitutions and gaps appear in the alignment. We assess the feasibility of our approach on an annotated set of human-mouse genomic alignments. CONCLUSION:Results show that the application of signal processing techniques to sequence alignments can be a useful tool for the identification of evolutionarily conserved protein-coding regions.

journal_name

Artif Intell Med

authors

Ré M,Pavesi G

doi

10.1016/j.artmed.2008.07.015

subject

Has Abstract

pub_date

2009-02-01 00:00:00

pages

117-23

issue

2-3

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(08)00105-X

journal_volume

45

pub_type

杂志文章
  • Employing decomposable partially observable Markov decision processes to control gene regulatory networks.

    abstract:OBJECTIVE:Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs). METHODS AND MATERIAL:Different approaches exist to model GRNs; they are mostly simulated as mathematical models that represent relationships between ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2017.06.007

    authors: Erdogdu U,Polat F,Alhajj R

    更新日期:2017-11-01 00:00:00

  • An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem.

    abstract:OBJECTIVE:The aim of this paper is to provide an improved method for solving the so-called dynamic patient admission scheduling (DPAS) problem. This is a complex scheduling problem that involves assigning a set of patients to hospital beds over a given time horizon in such a way that several quality measures reflecting...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2016.10.002

    authors: Lusby RM,Schwierz M,Range TM,Larsen J

    更新日期:2016-11-01 00:00:00

  • Case-based reasoning for medical decision support tasks: the Inreca approach.

    abstract::We describe an approach for developing knowledge-based medical decision support systems based on the new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca + project which mainly deals with medical applications. One goal was to s...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(97)00038-9

    authors: Althoff KD,Bergmann R,Wess S,Manago M,Auriol E,Larichev OI,Bolotov A,Zhuravlev YI,Gurov SI

    更新日期:1998-01-01 00:00:00

  • Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors.

    abstract:OBJECTIVE:We explore the link between dataset complexity, determining how difficult a dataset is for classification, and classification performance defined by low-variance and low-biased bolstered resubstitution error made by k-nearest neighbor classifiers. METHODS AND MATERIAL:Gene expression based cancer classificat...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2008.08.004

    authors: Okun O,Priisalu H

    更新日期:2009-02-01 00:00:00

  • The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes.

    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

    authors: Spänig S,Emberger-Klein A,Sowa JP,Canbay A,Menrad K,Heider D

    更新日期:2019-09-01 00:00:00

  • Accessing complex patient data from Arden Syntax Medical Logic Modules.

    abstract:OBJECTIVE:Arden Syntax is a standard for representing and sharing medical knowledge in form of independent modules and looks back on a history of 25 years. Its traditional field of application is the monitoring of clinical events such as generating an alert in case of occurrence of a critical laboratory result. Arden S...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2015.09.003

    authors: Kraus S,Enders M,Prokosch HU,Castellanos I,Lenz R,Sedlmayr M

    更新日期:2018-11-01 00:00:00

  • Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection.

    abstract::Computer-aided detection (CADe) systems play a crucial role in pulmonary nodule detection via chest radiographs (CXRs). A two-stage CADe scheme usually includes nodule candidate detection and false positive reduction. A pure deep learning model, such as faster region convolutional neural network (faster R-CNN), has be...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101881

    authors: Chen S,Han Y,Lin J,Zhao X,Kong P

    更新日期:2020-07-01 00:00:00

  • Constructing explanatory process models from biological data and knowledge.

    abstract:OBJECTIVE:We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. METHODS:We cast both models and background knowledge in terms of processes that interact to account for behavior. We a...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2006.04.003

    authors: Langley P,Shiran O,Shrager J,Todorovski L,Pohorille A

    更新日期:2006-07-01 00:00:00

  • A fuzzy cognitive map approach to differential diagnosis of specific language impairment.

    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

    authors: Georgopoulos VC,Malandraki GA,Stylios CD

    更新日期:2003-11-01 00:00:00

  • Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods.

    abstract::Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart soun...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2017.05.005

    authors: Eslamizadeh G,Barati R

    更新日期:2017-05-01 00:00:00

  • Bridge and brick network motifs: identifying significant building blocks from complex biological systems.

    abstract:OBJECTIVE:A major focus in computational system biology research is defining organizing principles that govern complex biological network formation and evolution. The task is considered a major challenge because network behavior and function prediction requires the identification of functionally and statistically impor...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2007.07.006

    authors: Huang CY,Cheng CY,Sun CT

    更新日期:2007-10-01 00:00:00

  • Bounded-depth threshold circuits for computer-assisted CT image classification.

    abstract::We present a stochastic algorithm that computes threshold circuits designed to discriminate between two classes of computed tomography (CT) images. The algorithm employs a partition of training examples into several classes according to the average grey scale value of images. For each class, a sub-circuit is computed,...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(01)00101-4

    authors: Albrecht A,Hein E,Steinhöfel K,Taupitz M,Wong CK

    更新日期:2002-02-01 00:00:00

  • Combining analysis of multi-parametric MR images into a convolutional neural network: Precise target delineation for vestibular schwannoma treatment planning.

    abstract::Manual delineation of vestibular schwannoma (VS) by magnetic resonance (MR) imaging is required for diagnosis, radiosurgery dose planning, and follow-up tumor volume measurement. A rapid and objective automatic segmentation method is required, but problems have been encountered due to the low through-plane resolution ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101911

    authors: Lee WK,Wu CC,Lee CC,Lu CF,Yang HC,Huang TH,Lin CY,Chung WY,Wang PS,Wu HM,Guo WY,Wu YT

    更新日期:2020-07-01 00:00:00

  • An object-oriented approach to knowledge representation in a biomedical domain.

    abstract::An object-oriented approach has been applied to the different stages involved in developing a knowledge base about insulin metabolism. At an early stage the separation of terminological and assertional knowledge was made. The terminological component was developed by medical experts and represented in CORE. An object-...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/0933-3657(94)90025-6

    authors: Ensing M,Paton R,Speel PH,Rada R

    更新日期:1994-12-01 00:00:00

  • A knowledge-based clinical toxicology consultant for diagnosing multiple exposures.

    abstract:OBJECTIVE:This paper presents continued research toward the development of a knowledge-based system for the diagnosis of human toxic exposures. In particular, this research focuses on the challenging task of diagnosing exposures to multiple toxins. Although only 10% of toxic exposures in the United States involve multi...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2013.02.002

    authors: Schipper JD,Dankel DD 2nd,Arroyo AA,Schauben JL

    更新日期:2013-05-01 00:00:00

  • Computer models of hippocampal circuit changes of the kindling model of epilepsy.

    abstract::Abnormalities in the organization of brain circuits may underlie many types of epilepsy. This hypothesis can best be evaluated in the case of temporal lobe epilepsy, where evidence of rewiring (synaptic reorganization) can be found in the dentate gyrus. Computer modeling of normal and reorganized dentate gyrus was use...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(98)00005-0

    authors: Lytton WW,Hellman KM,Sutula TP

    更新日期:1998-05-01 00:00:00

  • A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis.

    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

    authors: Reyes O,Pérez E,Luque RM,Castaño J,Ventura S

    更新日期:2020-08-01 00:00:00

  • Argumentation-logic for creating and explaining medical hypotheses.

    abstract:OBJECTIVE:While EIRA has proved to be successful in the detection of anomalous patient responses to treatments in the Intensive Care Unit, it could not describe to clinicians the rationales behind the anomalous detections. The aim of this paper is to address this problem. METHODS:Few attempts have been made in the pas...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2013.02.003

    authors: Grando MA,Moss L,Sleeman D,Kinsella J

    更新日期:2013-05-01 00:00:00

  • Fuzzy ensemble clustering based on random projections for DNA microarray data analysis.

    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

    authors: Avogadri R,Valentini G

    更新日期:2009-02-01 00:00:00

  • User-defined functions in the Arden Syntax: An extension proposal.

    abstract:BACKGROUND:The Arden Syntax is a knowledge-encoding standard, started in 1989, and now in its 10th revision, maintained by the health level seven (HL7) organization. It has constructs borrowed from several language concepts that were available at that time (mainly the HELP hospital information system and the Regenstrie...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2015.11.003

    authors: Karadimas H,Ebrahiminia V,Lepage E

    更新日期:2018-11-01 00:00:00

  • Prediction of visual perceptions with artificial neural networks in a visual prosthesis for the blind.

    abstract::Within the framework of the OPTIVIP project, an optic nerve based visual prosthesis is developed in order to restore partial vision to the blind. One of the main challenges is to understand, decode and model the physiological process linking the stimulating parameters to the visual sensations produced in the visual fi...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2004.02.004

    authors: Archambeau C,Delbeke J,Veraart C,Verleysen M

    更新日期:2004-11-01 00:00:00

  • Multichannel mixture models for time-series analysis and classification of engagement with multiple health services: An application to psychology and physiotherapy utilization patterns after traffic accidents.

    abstract:BACKGROUND:Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial. There is little literature on the health service utilization patterns ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101997

    authors: Esmaili N,Buchlak QD,Piccardi M,Kruger B,Girosi F

    更新日期:2021-01-01 00:00:00

  • Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI).

    abstract::Over the years, there has been growing interest in using machine learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc.) or the acquisition technology, which mi...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2020.101804

    authors: Ferrari E,Retico A,Bacciu D

    更新日期:2020-03-01 00:00:00

  • Knowledge modeling and acquisition of traditional Chinese herbal drugs and formulae from text.

    abstract::Traditional Chinese medicine has developed over more than 4000 years. A tremendous amount of medical knowledge has been accumulated, among which herbal drugs and formulae are an important portion. This paper presents an ontology for traditional Chinese drugs and formulae, and an ontology-based system for extracting kn...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2004.01.015

    authors: Cao C,Wang H,Sui Y

    更新日期:2004-09-01 00:00:00

  • Granular support vector machines with association rules mining for protein homology prediction.

    abstract:OBJECTIVE:Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an ess...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2005.02.003

    authors: Tang Y,Jin B,Zhang YQ

    更新日期:2005-09-01 00:00:00

  • A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: An application in medical diagnosis.

    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

    authors: Wang J,Hu Y,Xiao F,Deng X,Deng Y

    更新日期:2016-05-01 00:00:00

  • Case-based distance measurements for the selection of controls in case-matched studies: application in coronary interventions.

    abstract::In case-based studies, controls are retrospectively assigned to patients in order to permit a statistical evaluation of the study results through a comparison of the main outcome measures for the patient and retrieved control groups. Inappropriate selection of the controls by using false retrieval parameters or a fals...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(02)00084-2

    authors: Gyöngyösi M,Ploner M,Porenta G,Sperker W,Wexberg P,Strehblow C,Glogar D

    更新日期:2002-11-01 00:00:00

  • Logarithmic simulated annealing for X-ray diagnosis.

    abstract::We present a new stochastic learning algorithm and first results of computational experiments on fragments of liver CT images. The algorithm is designed to compute a depth-three threshold circuit, where the first layer is calculated by an extension of the Perceptron algorithm by a special type of simulated annealing. ...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(00)00112-3

    authors: Albrecht A,Steinhöfel K,Taupitz M,Wong CK

    更新日期:2001-06-01 00:00:00

  • Bayesian learning for cardiac SPECT image interpretation.

    abstract::In this paper, we describe a system for automating the diagnosis of myocardial perfusion from single-photon emission computerized tomography (SPECT) images of male and female hearts. Initially we had several thousand of SPECT images, other clinical data and physician-interpreter's descriptions of the images. The image...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/s0933-3657(02)00055-6

    authors: Sacha JP,Goodenday LS,Cios KJ

    更新日期:2002-09-01 00:00:00

  • Predicting ICU readmission using grouped physiological and medication trends.

    abstract:BACKGROUND:Patients who are readmitted to an intensive care unit (ICU) usually have a high risk of mortality and an increased length of stay. ICU readmission risk prediction may help physicians to re-evaluate the patient's physical conditions before patients are discharged and avoid preventable readmissions. ICU readmi...

    journal_title:Artificial intelligence in medicine

    pub_type: 杂志文章

    doi:10.1016/j.artmed.2018.08.004

    authors: Xue Y,Klabjan D,Luo Y

    更新日期:2019-04-01 00:00:00