cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26180/5619535
title: Recognition of protease inhibition pattern using topological interaction matrix and genetic algorithm-optimized support vector machines

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 (&gt;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.
DOI: 10.26180/5619535.v1
title: Recognition of protease inhibition pattern using topological interaction matrix and genetic algorithm-optimized support vector machines

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 (&gt;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.
DOI: 10.5281/zenodo.17089456
title: Code for manuscript titled "Human-induced intensification of sea surface temperature regime shifts threatens global Large Marine Ecosystems"

description: The four regime shift detection methods described in the manuscript—namely the Sequential t-test Analysis of Regime Shifts (STARS), the structural change method, the chronological clustering method, and the non-parametric method—were implemented in MATLAB R2024b and R v4.4.1. The input variable is the annual SST time series spanning >150 years, and the output variables include the years of detected SST regime shifts as well as their corresponding amplitudes (the absolute difference between consecutive regimes).
DOI: 10.26180/5619532
title: Successful computational prediction of the structure-activity relationship of a potent JAK2 inhibitor

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.
DOI: 10.26180/5619532.v1
title: Successful computational prediction of the structure-activity relationship of a potent JAK2 inhibitor

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.
DOI: 10.26180/5619529
title: Reversible auto-encoding of amino-acid residues in reduced space: an application to predicting DNA-binding proteins

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.
DOI: 10.26180/5619529.v1
title: Reversible auto-encoding of amino-acid residues in reduced space: an application to predicting DNA-binding proteins

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.
DOI: 10.26180/5619526
title: k-Word matches: an alignment-free sequence comparison method

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.
DOI: 10.5281/zenodo.18618077
title: Detecting ferrotoroidic order in LiNi0.8Fe0.2PO4

description: Spherical neutron polarimetry datasets at 2 K and 23 K under different field-cooling conditions
DOI: 10.5281/zenodo.18618078
title: Detecting ferrotoroidic order in LiNi0.8Fe0.2PO4

description: Spherical neutron polarimetry datasets at 2 K and 23 K under different field-cooling conditions
DOI: 10.25442/hku.31261363
title: Supporting Data for "Thermal Regime in the Hyporheic Zone of Pool-Riffle Streams"

description: This dataset contains the supporting data used to plot Figure 3, Figure 4, Figure 5 and Figure 6 in the manuscript "Thermal Regime in the Hyporheic Zone of Pool-Riffle Streams".
DOI: 10.25442/hku.31261363.v1
title: Supporting Data for "Thermal Regime in the Hyporheic Zone of Pool-Riffle Streams"

description: This dataset contains the supporting data used to plot Figure 3, Figure 4, Figure 5 and Figure 6 in the manuscript "Thermal Regime in the Hyporheic Zone of Pool-Riffle Streams".
DOI: 10.26180/5619526.v1
title: k-Word matches: an alignment-free sequence comparison method

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.
DOI: 10.26180/5619523
title: Enhanced laboratory diagnosis of human chlamydia pneumoniae infection through pattern recognition derived from pathology database analysis

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.
DOI: 10.26180/5619523.v1
title: Enhanced laboratory diagnosis of human chlamydia pneumoniae infection through pattern recognition derived from pathology database analysis

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.
DOI: 10.5281/zenodo.18618123
title: LINF_320045400

description: Wdr82; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18618124
title: LINF_320045400

description: Wdr82; Leishmania infantum (strain JPCM5)
DOI: 10.26180/5619517
title: Prediction of paediatric asthma hospitalisation using data mining techniques

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.
DOI: 10.26180/5619517.v1
title: Prediction of paediatric asthma hospitalisation using data mining techniques

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.
DOI: 10.26180/5619514
title: From pattern to causality: using linear discriminant analysis and Bayesian network on microarray data of breast cancers

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.
DOI: 10.26180/5619514.v1
title: From pattern to causality: using linear discriminant analysis and Bayesian network on microarray data of breast cancers

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.