cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Novel classification scheme for temporal genomic and proteomic problems
Creators: name: Kocbek, Simon
name: Stiglic, Gregor
name: Verlic, Mateja
name: Kokol, Peter
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: For over a decade genomic and proteomic datasets present a challenge for various statistical and machine learning methods. Most of microarray or mass spectrometry based datasets consist of a small number of samples with a large number of gene or protein expression measurements, but in the past few years new types of datasets with an additional time component are becoming available. This type of datasets offer new opportunities for development of new classification and gene selection techniques where one of the problems is the reduction of high-dimensionality. This paper presents a novel classification technique which combines feature extraction and feature selection to obtain the optimal set of genes available to a classifier. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
A single-residue affinity scale for DNA-binding using linear perceptron
Creators: name: Andrabi, Munazah
name: Ahmad, Shandar
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: A linear scale to estimate DNA-binding free energy of amino acid residues is reported. Scales derived exclusively for irregular and helical positions give 76% and 68% classification accuracy between stabilizing and destabilizing protein-DNA interaction. Mean absolute error (MAE) in ddG values is 0.786 and 0.883 kcal/mol respectively. Without using structure information of residues to derive affinity scales, 67.0% mutations could be correctly classified between those stabilizing and destabilizing binding. Mean absolute error (MAE) and correlation of ddG predictions are 0.953 kcal/mol and 0.385 respectively. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Recognition of protease inhibition pattern using topological interaction matrix and genetic algorithm-optimized support vector machines
Creators: name: Fernandez, Michael
name: Ahmad, Shandar
name: Sarai, Akinori
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: Proteases are among the most studied diagnostic and therapeutic targets for a variety of human diseases. Intensive research in computational design of protease inhibitors has been done by molecular dynamics simulations, docking and Quantitative Structure-Activity Relationships. In this work, proteochemometrics was applied to the recognition of stable and unstable protease inhibition complexes from a large dataset (>1700) using Genetic Algorithm-optimized Support Vector Machines classifiers. Genetic Algorithm optimized SVM were trained with Topological Autocorrelation Interaction vectors, computed on the protease sequences and the inhibitor 2D structure sketches. Optimum classifier with 10 inputs correctly predicted more than 80% in both training and test sets. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
title: 01_2676
Creators: name: Wesley Baldwin, John
name: Baldwin, John W.
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2018
Subjects: subject: LaMOP
subject: Laboratoire de Médiévistique Occidentale de Paris

description: L'ensemble de ces notes ont été crée par John Baldwin lors d'une de ses premières périodes de résidence à Paris. Ces documents comprennent tous les magistri qu’il avait pu rencontrer dans ses sources. Aux XIIème et XIIIème siècles, les universités n'existant pas encore, le titre de magister s'il évoque certainement une compétence n'est pas rattaché de façon systématique au passage par les écoles. De surcroît, les magistris sont le plus souvent désignés par leur seul prénom. La lecture de ces données est donc difficile et suppose un long travail pour tenter d'identifier un individu auquel on pourrait rattacher plusieurs mentions. John Baldwin avait donc renoncé à utiliser ces données pour donner un prolongement à sa magnifique étude sur les gradués d'université et leur emploi par les Capétiens et les Plantagenêts. Néanmoins, il a conservé ces données et elles sont potentiellement très utile dans le cadre de Studium, comme le sont toutes les mentions dans les volumes des Fasti Ecclesiae Gallicanae de chanoines portant ce titre de magister sans autres précisions. L'ampleur des dépouillements rend ces données très utiles et nous devons une grande reconnaissance à John Baldwin pour avoir pris la peine de faire photocopier ses fiches et de nous les avoir transmises amicalement.
Les données ainsi que les images des fiches sont accessibles sur le site Internet à l'adresse suivante : http://studium-baldwin.lamop.fr/.
Les transcriptions des fiches ont été effectuées par des étudiants de Paris 1 Panthéon - Sorbonne mais elles n'ont pas été corrigées par la suite.
description: Relation: https://lamop.hypotheses.org/
Successful computational prediction of the structure-activity relationship of a potent JAK2 inhibitor
Creators: name: Treutlein, Herbert R.
name: Styles, Michelle
name: Zeng, Jun
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: JAK2 is a member of the JAK family of protein tyrosine kinases (PTK). JAK2 is an important intracellular mediator of cytokine signaling and its mutants are involved in haematological cancers and other chronic diseases. The specificity of inhibitors targeting the JAK2 PTK domain, particularly over other JAK kinases, is a critical design element of clinically relevant drugs. Due to the considerable structural similarity amongst the over 500 kinases in the human genome, designing a highly specific inhibitor of just one kinase is a nontrivial task. We have performed molecular docking studies of a high-affinity pan-JAK inhibitor (tetracyclic pyridone 2-tert-butyl-9-fluoro-3,6-dihydro-7H-benz[h]-imidaz[4,5-f]isoquinoline-7-one) into a JAK2 PTK structure using our in-house software ChemaPhore™. Lacking an X-ray structure, the studies were based initially on a homology model of JAK2. In order to improve the specificity and potency of the inhibitors, a series of modifications to the pan-JAK inhibitor were suggested and the new compounds were then synthesized and biologically tested. The results of the structure-activity relationship (SAR) obtained from wet screening are consistent with the proposed binding mode of the pan-JAK inhibitor. More importantly, recent X-ray crystal structures of JAK2 inhibitor complexes are consistent with our JAK2 homology model and confirm our predictions. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
title: ADCO, Cartulaire général de Cîteaux, dit de Jean de Cirey, (XVe siècle), tome III, Cart. 187 (11 H 72), f. 0141r
Creators: name: de la Côte d'Or, Archives départementales
name: Archives Départementales De La Côte-D'Or
name: Petot, Frédérique
name: Projet CBMA
name: CG21/F.PETOT/2012
name: Petot, Frédéric
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2020
Subjects: subject: LaMOP
subject: Laboratoire de Médiévistique Occidentale de Paris
subject: Corpus Burgundiae Medii Aevi
subject: Chartes de la Bourgogne du Moyen Âge
subject: cartulaire

description: Cartulaire général de Cîteaux, dit de Jean de Cirey, (XVe siècle), tome III, Archives départementales de la Côte-d'Or, Cart. 187 (11 H 72), f. 0141r, Numérisation en mode image.
description: Relation: http://www.cbma-project.eu/
description: Bibliographic citation: Cartulaire général de Cîteaux, dit de Jean de Cirey, (XVe siècle), tome III, Archives départementales de la Côte-d'Or, Cart. 187 (11 H 72), f. 0141r, in Projet CBMA - Corpus Burgundiae Medii Aevi [Ressource en ligne], https:// manuscrits.cbma-project.eu/
description: Provenance: https://www.geonames.org/3024876
description: Provenance: http://www.idref.fr/030974844/id
description: Provenance: https://catalogue.bnf.fr/ark:/12148/cb12228351v
description: Provenance: abbaye de Cîteaux
description: Provenance: http://www.wikidata.org/entity/Q843261
title: Correspondance avec Hervé Latapie, Jean-Sébastien Tisserand, Christine Bergstrom
Creators: name: Latapie, Hervé (1959-....)
Publisher: name: NAKALA - https://nakala.fr (Huma-Num - CNRS)
publisherIdentifier: https://ror.org/04ces3204
publisherIdentifierScheme: ROR
Year: 2020

description: Source: http://www.calames.abes.fr/pub/ms/Calames-20193111133487610
Reversible auto-encoding of amino-acid residues in reduced space: an application to predicting DNA-binding proteins
Creators: name: Ahmad, Shandar
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: There have been a number of recent studies aiming to predict binding sites and other structural and sequence features of proteins using local amino acid sequence as inputs to a machine learning system. This requires representing amino acids in numerical space, which is typically 20 bits per residue. Number of trainable parameters significantly becomes large with the addition of each neighbor information and hence the application of the technique becomes restricted to the prediction of properties for which large amounts of data is available. Thus, there is a need to find alternatives to this type of sparse encoding. Here a method of auto encoding 20-dimensional sparse representation into lower dimensional space is developed with amino-acids in perspective- although the method is general. It is shown that 20-bit sparse encoding could be reduced to 6-dimensional real space without loss of information and to even lower dimensions with varying degrees of information loss. An application to predicting DNA-binding sites was tested to assess the validity of the proposed method and it was observed that auto-encoded neural network prediction was comparable or superior to sparse encoding system. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
k-Word matches: an alignment-free sequence comparison method
Creators: name: Burden, Conrad J.
name: Forêt, Sylvain
name: Wilson, Susan R.
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: k-word matches, the number of words of length k shared between two sequences, also known as the D2 statistic, are used in alignment-free sequence comparison statistic. The advantages of the use of this statistic over alignment-based methods for nucleotide and amino-acid sequence comparisons are firstly that it does not assume that homologous segments are contiguous, and secondly that the algorithm is computationally extremely fast, the runtime being proportional to the size of the sequence under scrutiny. We summarise our results to date on determing the distributional properties of the D2 statistic for a range of biologically relevant parameters and outline the directions in which the research will proceed. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Fuzzy rule generation using data mining techniques to classify two-class BCI experiment
Creators: name: Dehzangi, Omid
name: Boostani, Reza
name: Jahromi, Mansoor Zolghadri
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: In recent years, the Graz Brain-Computer Interface (BCI) has been developed and different aspects of this new field of research such as feature extraction and classification, mode of operation, mental strategy, and type of feedback have been investigated. In this paper, a Fuzzy Rule-Based Classification System (FRBCS) is presented in which a novel approach for fuzzy rule generation is proposed. The proposed algorithm makes the use of data mining principles, which are used by frequent pattern mining algorithms. Employing these principles enables us to well generate rules for subsequent classification purposes. Finally, a rule-weighting mechanism is investigated to tune the rule-base to have better classification ability. To evaluate the performance of the proposed scheme features containing standard bandpower and adaptive autoregressive coefficients are determined on four subjects in order to increase the performance of a cue-based BCI system for imagery classification tasks (left and right hand movements). As comparative classifiers, a number of successful methods of classification including Adaboost, Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) have been assessed. The results show that the proposed method of classification is effective in prediction ability of choosing between the left and right imagery tasks. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Enhanced laboratory diagnosis of human chlamydia pneumoniae infection through pattern recognition derived from pathology database analysis
Creators: name: Richardson, Alice
name: Hawkins, Simon
name: Shadabi, Fariba
name: Sharma, Dharmendra
name: Fulcher, John
name: Lidbury, Brett A.
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: This study focuses on pattern recognition in pathology data collected from patients tested for Chlamydia pneumoniae (Cp) infection, with co-infection by Mycoplasma pneumoniae (Myco) also considered. Both Cp and Myco are microbes that cause respiratory disease in some infected people. As well as the immunoassay results revealing whether the patient had been infected, or not, an extensive range of other routine pathology data was also available for each patient, allowing the analysis of associations between a positive immunoassay laboratory result for Cp or Myco, and a range of tests for biochemical and cellular markers (e.g. liver enzymes, electrolyte balance, haematological indices such as red/white cell counts). Decision trees and logistic regression were used to enhance laboratory diagnosis of these respiratory infections via the formulation of association rules derived from immunoassay results and associated pathology data. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Prediction of paediatric asthma hospitalisation using data mining techniques
Creators: name: Schmidt, Sam
name: Li, Gang
name: Chen, Yi-Ping Phoebe
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: Research into the prevalence of hospitalisation among childhood asthma cases is undertaken, using a data set local to the Barwon region of Victoria. Participants were the parents/guardians on behalf of children aged between 5-11 years. Various data mining techniques are used, including segmentation, association and classification to assist in predicting and exploring the instances of childhood hospitalisation due to asthma. Results from this study indicate that children in inner city and metropolitan areas may overutilise emergency department services. In addition, this study found that the prediction of hospitalisaion for asthma in children was greater for those with a written asthma management plan. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
From pattern to causality: using linear discriminant analysis and Bayesian network on microarray data of breast cancers
Creators: name: Lin, Tsun-Chen
name: Liu, Ru-Sheng
name: Chen, Chien-Yu
name: Chao, Ya-Ting
name: Chen, Shu-Yuan
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: In this paper, we aim at using genetic algorithms for gene selection and propose silhouette statistics as a discriminant function to classify breast cancers on microarray data for pattern discovery. In order to see the causality among these genes, we use the Bayesian method to construct a probability network for the pattern discovered. Consequently, we found a set of genes that is effective to discriminate breast cancer subtypes and present their probability dependencies to construct a diagnostic system. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Consensus clustering of gene expression microarray data using genetic algorithms
Creators: name: Mendes, Alexandre
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: This work presents a new consensus clustering method for gene expression microarray data based on a genetic algorithm. Using two datasets - DA and DB - as input, the genetic algorithm examines putative partitions for the samples in DA, selecting biomarkers that support such partitions. The biomarkers are then used to build a classifier which is used in DB to determine its samples classes. The genetic algorithm is guided by an objective function that takes into account the accuracy of classification in both datasets, the number of biomarkers that support the partition, and the distribution of the samples across the classes for each dataset. To illustrate the method, two whole-genome breast cancer instances from dfferent sources were used. In this application, the results indicate that the method could be used to find unknown subtypes of diseases supported by biomarkers presenting similar gene expression profiles across platforms. Moreover, even though this initial study was restricted to two datasets and two classes, the method can be easily extended to consider both more datasets and classes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Classifying microarray cancer datasets using nearest subspace classification
Creators: name: Cohen, Michael C.
name: Paliwal, Kuldip K.
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: In this paper we implement and test the recently described nearest subspace classifier on a range of microarray cancer datasets. Its classification accuracy is tested against nearest neighbor and nearest centroid algorithms, and is shown to give a significant improvement. This classification system uses class-dependent PCA to construct a subspace for each class. Test vectors are assigned the class label of the nearest subspace, which is defined as the minimum reconstruction error across all subspaces. Furthermore, we demonstrate this distance measure is equivalent to the null-space component of the vector being analyzed. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Spectrum analysis based method for dynamics and collective analysis of protein-protein interaction networks
Creators: name: Shen, Yi-Zhen
name: Ding, Yong-Sheng
name: Gu, Quan
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: The importance of understanding biological interaction networks has fueled the development of numerous interaction data generation techniques, databases and prediction tools. Generation of high-confident interaction networks formulates the first step towards the study for protein–protein interactions (PPI). A number of experimental methods, based on distinct, physical principles have been developed to identify PPI such as the yeast two-hybrid method (Y2H). In this work, we focus on one example of biological networks, namely the yeast protein interaction network (YPIN). In YPIN, we design and implement a computational model that captures the discrete and stochastic nature of protein interactions. In this model, we apply spectrum analysis method to the variance of the protein nodes which play an important role in the PPI networks, which can show the topology structure of dynamic and collective performances of PPI networks. We take YPIN, such as 48 "quasi-cliques" and 6 "quasi-bipartites" separated from 11855 yeast PPI networks with 2617 proteins, as an example and apply spectrum analysis to show the topology structure of dynamic and collective analysis of PPI networks and the performances. The obtained results may be valuable for deciphering unknown protein functions, determining protein complexes, and inventing drugs. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Algorithms for detecting protein complexes in PPI networks: an evaluation study
Creators: name: Wu, Min
name: Li, Xiaoli
name: Kwoh, Chee-Keong
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: Since protein complexes play important biological roles in cells, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) data. In this paper, we first review four reputed protein-complex detection algorithms (MCODE[2], MCL[21], CPA[1] and DECAFF[14]) and then present a comprehensive evaluation among them on two popular yeast PPI data3. We also discuss their relative strengthes and disadvantages to guide interested researchers. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Feature ranking and feature redundancy reduction for prognostic microarray study of tumor clinical outcomes
Creators: name: Tan, Qihua
name: Thomassen, Mads
name: Christensen, Kaare
name: Kruse, Torben A.
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: Different from significant gene expression analysis which looks for all genes that are differentially regulated, feature selection in prognostic gene expression analysis aims at finding a subset of informative marker genes that are discriminative for prediction. Unfortunately feature selection in the literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significance. Since the univariate approach does not take into account the correlated or interactive structure among the genes, classifiers built on genes so selected can be less accurate. More advanced approaches based on multivariate models have to be considered. Here, we introduce a feature ranking method through forward orthogonal search to assist prognostic gene selection. Application to published gene-lists selected by univariate models shows that the feature space can be largely reduced while achieving improved testing performances. Our results indicate that "significant" features selected using the gene-wised approaches can contain irrelevant genes that only serve to complicate model building. Multivariate feature ranking can help to reduce feature redundancy and to select highly informative prognostic marker genes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Gene expression analysis for tumor classification using vector quantization
Creators: name: Márquez, Edna
name: Espinosa, Ana María
name: Berumen, Jaime
name: Lemaitre, Christian
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: Gene expression analysis is one of the most important tasks for genomic medicine, using these it is possible to classify tumors, which are directly related with the development of cancer. This paper presents a clustering method for tumor classification, vector quantization, using gene expression profiles from microarrays of mRNA with samples of cervical cancer and normal cervix. Vector quantization is used to divide the space into regions, and the centroids of the regions represent patients with tumors or healthy ones. Also the regions found by the vector quantizer are used as the base for classifying other tumors, that could help in the prognostics of the illness or for finding new groups of tumors. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Fuzzy forms of the rand , adjusted rand and jaccard indices for fuzzy partitions of gene expression and other data
Creators: name: Brouwer, Roelof K.
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: Clustering is one of the most basic processes that are performed in simplifying data and expressing knowledge in a scientific endeavor. Clustering algorithms have been proposed for the analysis of gene expression data with little guidance available to help choose among them however. Since the output of clustering is a partition of the input data, the quality of the partition must be determined. This paper presents fuzzy extensions to some commonly used clustering measures including the rand index (RI), adjusted rand index(ARI) and the jaccard index(JI) that are already defined for crisp clustering. Fuzzy clustering, and therefore fuzzy cluster indices, is beneficial since it provides more realistic cluster memberships for the objects that are clustered rather than 0 or 1 values. If a crisp partition is still desired the fuzzy partition can be turned in to a crisp partition in an obvious manner. The usefulness of the fuzzy clustering in that case is that it processes noise better. These new indices proposed in this paper, called FRI, FARI, and FJI for fuzzy clustering, give the same values as the original indices do in the special case of crisp clustering. Through use in fuzzy clustering of artificial data and real data, including gene expression data, the effectiveness of the indices is demonstrated. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Multiple sequence alignment for functional correlation among low similarity sequences
Creators: name: Chou, Wei-Yao
name: Chou, Wei-I
name: Pai, Tun-Wen
name: Lin, Shu-Chuan
name: Chang, Fan-Yu
name: Sun, Yuh-Ju
name: Tang, Chuan-Yi
name: Chang, Margaret Dah-Tsyr
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: Multiple sequence alignment is a broadly used methodology in biological applications. It is expected to locate consensus sequence stretches with evolutionary and functional conservation. However, when sequence similarity among the queries becomes low, it works poorly. The aim of this study is to incorporate important biological knowledge and assumption to improve the quality of a general alignment on low similarity sequences such as carbohydrate binding module (CBM) families. Since the recognition of characteristic patterns in CBMs does not apply to a general model, a more accurate scoring function employing secondary-structure-based and key-residue-weighted algorithms for alignment was designed to approach this goal. Our results indicated that the new method was practically applicable to identify the key residues in terms of three-dimensional structures, while conventional tools could fail. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
The strong convergence of visual classification method and its applications in disease diagnosis
Creators: name: Meng, Deyu
name: Xu, Zongben
name: Leung, Yee
name: Fung, Tung
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: Visual classification method is introduced as a learning strategy for pattern classification problem in bioinformatics. In this paper, we show the strong convergence property of the proposed method. In particular, the method is shown to converge to the Bayes estimator, i.e., the learning error of the method tends to achieve the posterior expected minimal value. The method is successfully applied to some practical disease diagnosis problems. The experimental results all verify the validity and effectiveness of the theoretical conclusions. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Noncovalent stable functionalization makes carbon nanotubes hydrophilic and biocompatible
Creators: name: Ernst, Friederike
name: Gao, Zhenghong
name: Arenal, Raúl
name: Heek, Timm
name: Setaro, Antonio
name: Fernandez-Pacheco, Rodrigo
name: Haag, Rainer
name: Cognet, Laurent
name: Reich, Stephanie
Publisher: name: Freie Universität Berlin
Year: 2017
Subjects: subject: dyes
subject: pigments
subject: biocompatibility
subject: electron energy loss spectroscopy
subject: carbon nanotubesSurfactants
subject: 500 Naturwissenschaften und Mathematik::530 Physik::530 Physik

description: Semiconducting single-wall carbon nanotubes possess an intrinsic photoluminescence in the near-infrared region beyond 900 nm, the NIR-II window of biological imaging. Here, we introduce a modular molecule for noncovalent nanotube functionalization which renders carbon nanotubes hydrophilic and fully biocompatible through a one-step process. We demonstrate through electron energy-loss spectroscopy (EELS) spectroscopy that the noncovalent functionalization mechanism relies on tight and extremely robust π–π stacking, which survives an exchange of the solvent as well as drying. Furthermore, the modularity of the molecule design allows for the introduction of functional units into the molecule itself to modify the optical properties of the carbon nanotube, for instance to augment its excitation window through an excitation energy transfer, facilitating the excitation of most carbon nanotube chiralities at one single wavelength.
Additional file 1 of Body-mass index, blood pressure, diabetes and cardiovascular mortality in Cuba: prospective study of 146,556 participants
Creators: name: Armas Rojas, Nurys B
name: Lacey, Ben
name: Soni, Monica
name: Charles, Shaquille
name: Carter, Jennifer
name: Varona-Pérez, Patricia
name: Burrett, Julie Ann
name: Martínez, Marcy Calderón
name: Lorenzo-Vázquez, Elba
name: Constantén, Sonia Bess
name: Taylor, Hannah
name: Sherliker, Paul
name: Rigau, José Manuel Morales
name: Ross, Stephanie
name: Massa, M. Sofia
name: López, Osvaldo Jesús Hernández
name: Islam, Nazrul
name: Morales, Miguel Ángel Martínez
name: Alomá, Ismell Alonso
name: Estupiñan, Fernando Achiong
name: González, Mayda Díaz
name: Muñoz, Noel Rosquete
name: Asencio, Marelis Cendra
name: Díaz-Diaz, Oscar
name: Iglesias-Marichal, Ileydis
name: Emberson, Jonathan
name: Peto, Richard
name: Lewington, Sarah
Publisher: name: figshare
Year: 2021
Subjects: subject: Medicine
subject: Cell Biology
subject: Chemical Sciences not elsewhere classified
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Science Policy

description: Additional file 1. Questionnaire (original Spanish), questionnaire (English translation), supplementary Tables S1-S5, and supplementary Figure S1-S11.
Particle swarm optimization for MEG source localization
Creators: name: Jiang, Chenwei
name: Wang, Bin
name: Zhang, Liming
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: The estimation of three-dimension neural active sources from the magnetoencephalography (MEG) record is a very critical issue for both clinical neurology and brain functions research. Nowadays multiple signal classification (MUSIC) algorithm and recursive MUSIC algorithm are widely used to locate dipolar sources from MEG data. The drawback of these algorithms is that they need excessive calculation and is quite time-consuming when scanning a three-dimensional space. In order to solve this problem, we propose a MEG sources localization scheme based on an improved Particle Swarm Optimization (PSO). This scheme uses the advantage of global searching ability of PSO to estimate the rough source location. Then combining with grids search in small area, the accurate dipolar source localization is performed. In addition, we compare the results of our method with those based on Genetic Algorithm (GA). Computer simulation results show that our PSO strategy is an effective and precise approach to dipole localization which can improve the speed greatly and localize the sources accurately. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Equiangular lines and eigenvalue multiplicities
Creators: name: Zhao, Yufei
Publisher: name: arXiv
Year: 2024
Subjects: subject: Combinatorics (math.CO)
subject: FOS: Mathematics

description: Expository article on the problem of determining the maximum number of equiangular lines with a fixed angle, and the associated problem of second eigenvalue multiplicity in graphs.
description: Invited feature article in the Notices of the American Mathematical Society
Minimal siRNA set cover heuristic for gene family knockdown
Creators: name: Li, Xiaoguang
name: Ngom, Alioune
name: Rueda, Luis
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
A database of high-resolution MS/MS spectra for lichen metabolites
Creators: name: Olivier-Jimenez, Damien
name: Chollet-Krugler, Marylène
name: Rondeau, David
name: Beniddir, Mehdi A.
name: Ferron, Solenn
name: Delhaye, Thomas
name: Allard, Pierre-Marie
name: Wolfender, Jean-Luc
name: Sipma, Harrie J. M.
name: Lücking, Robert
name: Boustie, Joël
name: Pogam, Pierre Le
Publisher: name: Freie Universität Berlin
Year: 2019
Subjects: subject: Mass spectrometry
subject: Metabolomics
subject: Natural products
subject: Secondary metabolism
subject: Small molecules
subject: 500 Naturwissenschaften und Mathematik::500 Naturwissenschaften::500 Naturwissenschaften und Mathematik

description: While analytical techniques in natural products research massively shifted to liquid chromatography-mass spectrometry, lichen chemistry remains reliant on limited analytical methods, Thin Layer Chromatography being the gold standard. To meet the modern standards of metabolomics within lichenochemistry, we announce the publication of an open access MS/MS library with 250 metabolites, coined LDB for Lichen DataBase, providing a comprehensive coverage of lichen chemodiversity. These were donated by the Berlin Garden and Botanical Museum from the collection of Siegfried Huneck to be analyzed by LC-MS/MS. Spectra at individual collision energies were submitted to MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS999) while merged spectra were uploaded to the GNPS platform (CCMSLIB00004751209 to CCMSLIB00004751517). Technical validation was achieved by dereplicating three lichen extracts using a Molecular Networking approach, revealing the detection of eleven unique molecules that would have been missed without LDB implementation to the GNPS. From a chemist’s viewpoint, this database should help streamlining the isolation of formerly unreported metabolites. From a taxonomist perspective, the LDB offers a versatile tool for the chemical profiling of newly reported species.
PSOMF: An algorithm for pattern discovery using PSO
Creators: name: Zare-Mirakabad, F.
name: Ahrabian, H.
name: Sadeghi, M.
name: Mohammadzadeh, J.
name: Hashemifar, S.
name: Nowzari-Dalini, A.
name: Goliaei, B.
Publisher: name: Monash University
Year: 2017
Subjects: subject: 80301 Bioinformatics Software
subject: FOS: Computer and information sciences
subject: 60102 Bioinformatics
subject: 80109 Pattern Recognition and Data Mining

description: The task of transcription factor binding sites discovery from the upstream region of gene, without any prior knowledge of what look likes, is very challenging. In this paper we propose an algorithm based on Particle Swarm Optimization (PSO) to identify motif instances in multiple biological sequences. The experimental results on yeast sac-choromyces Cerevisae transcription factor binding sites, demonstrate that the proposed method is working analogous to YMF, MEME and AlignACE algorithms. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.