Publications by authors named "Dmitry Konovalov"

20 Publications

  • Page 1 of 1

Self-Assembled Microporous M-HOFs Based on an Octahedral Rhenium Cluster with Benzimidazole.

Inorg Chem 2021 Sep 13. Epub 2021 Sep 13.

Nikolaev Institute of Inorganic Chemistry, Siberian Branch of the Russian Academy of Science, 3 Acad. Lavrentiev Avenue, Novosibirsk 630090, Russian Federation.

Substitution of apical halide ligands in [{ReSe}X] (X = Cl, Br) by benzimidazole (bimzH) accompanied by a self-assembly process leads to the formation of microporous Re-based hydrogen-bonded organic frameworks (Re-HOFs) constructed on N-H···X hydrogen bonds and π-π-stacking interactions between bimzH ligands. Re-HOFs demonstrate sorption properties with a Brunauer-Emmett-Teller surface area of up to 443 m g and luminescence with a quantum yield and an emission lifetime of up to 0.16 and 16 μs, respectively. The compounds obtained complement small groups of transition-metal cluster-based HOFs, which are a perspective for the development of multifunctional frameworks.
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http://dx.doi.org/10.1021/acs.inorgchem.1c01771DOI Listing
September 2021

A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis.

Sci Rep 2020 09 4;10(1):14671. Epub 2020 Sep 4.

James Cook University, Townsville, Australia.

Visual analysis of complex fish habitats is an important step towards sustainable fisheries for human consumption and environmental protection. Deep Learning methods have shown great promise for scene analysis when trained on large-scale datasets. However, current datasets for fish analysis tend to focus on the classification task within constrained, plain environments which do not capture the complexity of underwater fish habitats. To address this limitation, we present DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine-environments of tropical Australia. The dataset originally contained only classification labels. Thus, we collected point-level and segmentation labels to have a more comprehensive fish analysis benchmark. These labels enable models to learn to automatically monitor fish count, identify their locations, and estimate their sizes. Our experiments provide an in-depth analysis of the dataset characteristics, and the performance evaluation of several state-of-the-art approaches based on our benchmark. Although models pre-trained on ImageNet have successfully performed on this benchmark, there is still room for improvement. Therefore, this benchmark serves as a testbed to motivate further development in this challenging domain of underwater computer vision.
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http://dx.doi.org/10.1038/s41598-020-71639-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473859PMC
September 2020

RNA Sequencing-Based Identification of Ganglioside GD2-Positive Cancer Phenotype.

Biomedicines 2020 May 30;8(6). Epub 2020 May 30.

Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho- Maklaya St., 117997 Moscow, Russia.

The tumor-associated ganglioside GD2 represents an attractive target for cancer immunotherapy. GD2-positive tumors are more responsive to such targeted therapy, and new methods are needed for the screening of GD2 molecular tumor phenotypes. In this work, we built a gene expression-based binary classifier predicting the GD2-positive tumor phenotypes. To this end, we compared RNA sequencing data from human tumor biopsy material from experimental samples and public databases as well as from GD2-positive and GD2-negative cancer cell lines, for expression levels of genes encoding enzymes involved in ganglioside biosynthesis. We identified a 2-gene expression signature combining ganglioside synthase genes and that serves as a more efficient predictor of GD2-positive phenotype (Matthews Correlation Coefficient (MCC) 0.32, 0.88, and 0.98 in three independent comparisons) compared to the individual ganglioside biosynthesis genes (MCC 0.02-0.32, 0.1-0.75, and 0.04-1 for the same independent comparisons). No individual gene showed a higher MCC score than the expression signature MCC score in two or more comparisons. Our diagnostic approach can hopefully be applied for pan-cancer prediction of GD2 phenotypes using gene expression data.
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http://dx.doi.org/10.3390/biomedicines8060142DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7344710PMC
May 2020

Water-Soluble Rhenium Clusters with Triazoles: The Effect of Chemical Structure on Cellular Internalization and the DNA Binding of the Complexes.

Chemistry 2020 Nov 29;26(61):13904-13914. Epub 2020 Sep 29.

Nikolaev Institute of Inorganic Chemistry SB RAS, 3 acad. Lavrentiev ave., 630090, Novosibirsk, Russia.

Here we explore the effect of the nature of organic ligands in rhenium cluster complexes [Re Q L ] (where Q=S or Se, and L=benzotriazole, 1,2,3-triazole or 1,2,4-triazole) on the biological properties of the complexes, in particular on the cellular toxicity, cellular internalization and localization. Specifically, the study describes the synthesis and detailed characterization of the structure, luminescence and electrochemical properties of the four new Re clusters with 1,2,3- and 1,2,4-triazoles. Biological assays of these complexes are also discussed in addition to those with benzotriazole using cervical cancer (HeLa) and immortalized human fibroblasts (CRL-4025) as model cell lines. Our study demonstrates that the presence of hydrophobic and π-bonding rich units such as the benzene ring in benzotriazole significantly enhances cellular internalization of rhenium clusters. These ligands facilitate binding of the clusters to DNA, which results in increased cytotoxicity of the complexes.
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http://dx.doi.org/10.1002/chem.202001680DOI Listing
November 2020

Areca catechu-From farm to food and biomedical applications.

Phytother Res 2020 Sep 11;34(9):2140-2158. Epub 2020 Mar 11.

Zabol Medicinal Plants Research Center, Zabol University of Medical Sciences, Zabol, Iran.

The family Arecaceae includes 181 genera and 2,600 species with a high diversity in physical characteristics. Areca plants, commonly palms, which are able to grow in nearly every type of habitat, prefer tropical and subtropical climates. The most studied species Areca catechu L. contains phytochemicals as phenolics and alkaloids with biological properties. The phenolics are mainly distributed in roots followed by fresh unripe fruits, leaves, spikes, and veins, while the contents of alkaloids are in the order of roots, fresh unripe fruits, spikes, leaves, and veins. This species has been reputed to provide health effects on the cardiovascular, respiratory, nervous, metabolic, gastrointestinal, and reproductive systems. However, in many developing countries, quid from this species has been associated with side effects, which include the destruction of the teeth, impairment of oral hygiene, bronchial asthma, or oral cancer. Despite these side effects, which are also mentioned in this work, the present review collects the main results of biological properties of the phytochemicals in A. catechu. This study emphasizes the in vitro and in vivo antioxidant, antimicrobial, anticancer, and clinical effectiveness in humans. In this sense, A. catechu have demonstrated effectiveness in several reports through in vitro and in vivo experiments on disorders such as antimicrobial, antioxidant, or anticancer. Moreover, our findings demonstrate that this species presents clinical effectiveness on neurological disorders. Hence, A. catechu extracts could be used as a bioactive ingredient for functional food, nutraceuticals, or cosmeceuticals. However, further studies, especially extensive and comprehensive clinical trials, are recommended for the use of Areca in the treatment of diseases.
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http://dx.doi.org/10.1002/ptr.6665DOI Listing
September 2020

Composite Epstein-Barr virus-positive mucosa-associated lymphoid tissue lymphoma and Epstein-Barr virus-negative diffuse large B-cell lymphoma in the parotid salivary gland of a patient with Sjögren's syndrome and rheumatoid arthritis: a case report.

J Med Case Rep 2020 Jan 17;14(1):12. Epub 2020 Jan 17.

Department of Nuclear Diagnostics, A.N. Bakulev National Medical Research Center of Cardiovascular Surgery, Roublyevskoe Shosse 135, Moscow, 121552, Russia.

Background: Epstein-Barr virus is associated with many human hematopoietic neoplasms; however, Epstein-Barr virus-positive mucosa-associated lymphoid tissue lymphoma is extremely rare. In routine clinical practice, detection of mucosa-associated lymphoid tissue lymphoma and diffuse large B-cell lymphoma in a tissue sample presumes a clonal relation between these neoplasms and that diffuse large B-cell lymphoma developed by transformation of the mucosa-associated lymphoid tissue lymphoma. However, evidence to support this presumption is sparse and controversial. Assessment of the clonal relationship of the lymphoid components of a composite lymphoma is important for understanding its pathogenesis and correct diagnosis.

Case Presentation: We present an unusual case of composite lymphoma (Epstein-Barr virus-positive mucosa-associated lymphoid tissue lymphoma/Epstein-Barr virus-negative diffuse large B-cell lymphoma) in the parotid salivary gland of a 62-year-old Caucasian woman with Sjögren's syndrome and rheumatoid arthritis. Simultaneous occurrence of mucosa-associated lymphoid tissue lymphoma and diffuse large B-cell lymphoma in the parotid salivary gland led us to initially assume a clonal relationship between diffuse large B-cell lymphoma and mucosa-associated lymphoid tissue lymphoma. Epstein-Barr virus was detected by in situ hybridization and polymerase chain reaction in the mucosa-associated lymphoid tissue lymphoma, but not in diffuse large B-cell lymphoma, suggesting that these lymphomas were not clonally related. Fragment analysis of frame region 3 polymerase chain reaction products from microdissected mucosa-associated lymphoid tissue lymphoma and diffuse large B-cell lymphoma components revealed different clonal pattern rearrangements of the immunoglobulin heavy chain gene.

Conclusions: Our patient's case highlights the importance of assessing the clonal relationships of the lymphoid components of a composite lymphoma and Epstein-Barr virus screening in mucosa-associated lymphoid tissue lymphoma in patients with autoimmune disease.
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http://dx.doi.org/10.1186/s13256-019-2331-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6966905PMC
January 2020

Convolvulus plant-A comprehensive review from phytochemical composition to pharmacy.

Phytother Res 2020 Feb 11;34(2):315-328. Epub 2019 Nov 11.

Department of Nutrition and Dietetics, Faculty of Pharmacy, University Concepcion, Concepcion, VIII-Bio Bio Region, Chile.

Convolvulus genus is a representative of the family of Convolvulaceae. Convolvulus plants are broadly distributed all over the world and has been used for many centuries as herbal medicine. Convolvulus genus contains various phytochemicals such as flavonoids, alkaloids, carbohydrates, phenolic compounds, mucilage, unsaturated sterols or terpenes, resin, tannins, lactones, and proteins. This review highlights the phytochemical composition, antimicrobial and antioxidant activities, application as food preservative, traditional medicine use, anticancer activities, and clinical effectiveness in human of Convolvulus plants. All the parts of Convolvulus plants possess therapeutic benefits; preliminary pharmacological data validated their use in traditional medicine. However, further preclinical and clinical experiments are warranted before any application in human health.
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http://dx.doi.org/10.1002/ptr.6540DOI Listing
February 2020

DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning.

Sci Rep 2019 02 14;9(1):2058. Epub 2019 Feb 14.

College of Science and Engineering, James Cook University, Townsville, QLD, 4811, Australia.

Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands.
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http://dx.doi.org/10.1038/s41598-018-38343-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6375952PMC
February 2019

Finding the mean in a partition distribution.

BMC Bioinformatics 2018 Oct 12;19(1):375. Epub 2018 Oct 12.

School of Information Technology, James Cook University, 1 James Cook Drive, Townsville, QLD 4811, Australia.

Background: Bayesian clustering algorithms, in particular those utilizing Dirichlet Processes (DP), return a sample of the posterior distribution of partitions of a set. However, in many applied cases a single clustering solution is desired, requiring a 'best' partition to be created from the posterior sample. It is an open research question which solution should be recommended in which situation. However, one such candidate is the sample mean, defined as the clustering with minimal squared distance to all partitions in the posterior sample, weighted by their probability. In this article, we review an algorithm that approximates this sample mean by using the Hungarian Method to compute the distance between partitions. This algorithm leaves room for further processing acceleration.

Results: We highlight a faster variant of the partition distance reduction that leads to a runtime complexity that is up to two orders of magnitude lower than the standard variant. We suggest two further improvements: The first is deterministic and based on an adapted dynamical version of the Hungarian Algorithm, which achieves another runtime decrease of at least one order of magnitude. The second improvement is theoretical and uses Monte Carlo techniques and the dynamic matrix inverse. Thereby we further reduce the runtime complexity by nearly the square root of one order of magnitude.

Conclusions: Overall this results in a new mean partition algorithm with an acceleration factor reaching beyond that of the present algorithm by the size of the partitions. The new algorithm is implemented in Java and available on GitHub (Glassen, Mean Partition, 2018).
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http://dx.doi.org/10.1186/s12859-018-2359-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186144PMC
October 2018

Gene expression and molecular pathway activation signatures of -amplified neuroblastomas.

Oncotarget 2017 Oct 28;8(48):83768-83780. Epub 2017 Jul 28.

D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.

Neuroblastoma is a pediatric cancer arising from sympathetic nervous system. Remarkable heterogeneity in outcomes is one of its widely known features. One of the traits strongly associated with the unfavorable subtype is the amplification of oncogene . Here, we performed cross-platform biomarker detection by comparing gene expression and pathway activation patterns from the two literature reports and from our experimental dataset, combining profiles for the 761 neuroblastoma patients with known amplification status. We identified 109 / 25 gene expression / pathway activation biomarkers strongly linked with the amplification. The marker genes/pathways are involved in the processes of purine nucleotide biosynthesis, ATP-binding, tetrahydrofolate metabolism, building mitochondrial matrix, biosynthesis of amino acids, tRNA aminoacylation and NADP-linked oxidation-reduction processes, as well as in the tyrosine phosphatase activity, p53 signaling, cell cycle progression and the G1/S and G2/M checkpoints. To connect molecular functions of the genes involved in -amplified phenotype, we built a new molecular pathway using known intracellular protein interaction networks. The activation of this pathway was highly selective in discriminating -amplified neuroblastomas in all three datasets. Our data also suggest that the phosphoinositide 3-kinase (PI3K) inhibitors may provide new opportunities for the treatment of the -amplified neuroblastoma subtype.
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http://dx.doi.org/10.18632/oncotarget.19662DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663553PMC
October 2017

Integrated Molecular Meta-Analysis of 1,000 Pediatric High-Grade and Diffuse Intrinsic Pontine Glioma.

Cancer Cell 2017 10 28;32(4):520-537.e5. Epub 2017 Sep 28.

Department of Cytogenetics and Reproductive Biology, Farhat Hached Hospital, Sousse, Tunisia.

We collated data from 157 unpublished cases of pediatric high-grade glioma and diffuse intrinsic pontine glioma and 20 publicly available datasets in an integrated analysis of >1,000 cases. We identified co-segregating mutations in histone-mutant subgroups including loss of FBXW7 in H3.3G34R/V, TOP3A rearrangements in H3.3K27M, and BCOR mutations in H3.1K27M. Histone wild-type subgroups are refined by the presence of key oncogenic events or methylation profiles more closely resembling lower-grade tumors. Genomic aberrations increase with age, highlighting the infant population as biologically and clinically distinct. Uncommon pathway dysregulation is seen in small subsets of tumors, further defining the molecular diversity of the disease, opening up avenues for biological study and providing a basis for functionally defined future treatment stratification.
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http://dx.doi.org/10.1016/j.ccell.2017.08.017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5637314PMC
October 2017

Brentuximab vedotin in the treatment of a patient with refractory Hodgkin disease and Proteus syndrome - a case report and discussion.

Clin Case Rep 2015 Jul 11;3(7):646-9. Epub 2015 Jun 11.

Federal Center for Pediatric Hematology, Oncology and Immunology, Named by D. Rogachev Moscow, Russia.

Treatment of patients with refractory Hodgkin lymphoma is a significant issue. We report a patient with Proteus syndrome and relapsed Hodgkin lymphoma, whose remission was finally achieved after brentuximab vedotin therapy, allowing her to receive a haploidentical stem cell transplant. The possible relationship between both disorders was discussed.
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http://dx.doi.org/10.1002/ccr3.297DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527816PMC
July 2015

Malignant rhabdoid tumor of the liver presented with initial tumor rupture.

Cancer Genet 2014 Sep 21;207(9):412-4. Epub 2014 Apr 21.

Department of Clinical Oncology, Federal Scientific and Clinical Center of Pediatric Hematology, Oncology and Immunology named after Dmitry Rogachev, Moscow, Russian Federation.

Malignant rhabdoid tumor (MRT) of the liver is a rare, highly aggressive tumor of early childhood. We report a 6-month-old boy who was diagnosed with MRT of the liver and presented with spontaneous tumor rupture. The patient underwent intensified chemotherapy and a radical surgical procedure. Twenty four months from the time of the diagnosis, he is alive without evidence of disease. This is the second report of prolonged survival after initial rupture of hepatic MRT.
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http://dx.doi.org/10.1016/j.cancergen.2014.04.006DOI Listing
September 2014

Genome-wide SNP validation and mantle tissue transcriptome analysis in the silver-lipped pearl oyster, Pinctada maxima.

Mar Biotechnol (NY) 2013 Dec 30;15(6):647-58. Epub 2013 May 30.

Centre for Sustainable Tropical Fisheries and Aquaculture & School of Marine and Tropical Biology, James Cook University, Townsville, QLD, 4811, Australia,

Pearl oysters are not only farmed for their gemstone quality pearls worldwide, but they are also becoming important model organisms for investigating genetic mechanisms of biomineralisation. Despite their economic and scientific significance, limited genomic resources are available for this important group of bivalves, hampering investigations into identifying genes that regulate important pearl quality traits and unique biological characteristics (i.e. biomineralisation). The silver-lipped pearl oyster, Pinctada maxima, is one species where there is interest in understanding genes that regulate commercially important pearl traits, but presently, there is a dearth of genomic information. The objective of this study was to develop and validate a large number of type I genome-wide single nucleotide polymorphisms (SNPs) for P. maxima suitable for high-throughput genotyping. In addition, sequence annotations and Gene Ontology terms were assigned to a large mantle tissue 454 expressed sequence tag assembly (96,794 contigs) and information on known bivalve biomineralisation genes was incorporated into SNP discovery. The SNP discovery effort resulted in the de novo identification of 172,625 SNPs, of which 9,108 were identified as high value [minor allele frequency (MAF)≥ 0.15, read depth  ≥ 8]. Validation of 2,782 of these SNPs using Illumina iSelect Infinium genotyping technology returned some of the highest assay conversion (86.6 %) and validation (59.9 %; mean MAF 0.28) rates observed in aquaculture species to date. Genomic resources presented here will be pivotal to future research investigating the biological mechanisms behind biomineralisation and will form a strong foundation for genetic selective breeding programs in the P. maxima pearling industry.
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http://dx.doi.org/10.1007/s10126-013-9514-3DOI Listing
December 2013

Robust cross-validation of linear regression QSAR models.

J Chem Inf Model 2008 Oct 1;48(10):2081-94. Epub 2008 Oct 1.

School of Mathematics, Physics & Information Technology, James Cook University, Townsville, Queensland 4811, Australia.

A quantitative structure-activity relationship (QSAR) model is typically developed to predict the biochemical activity of untested compounds from the compounds' molecular structures. "The gold standard" of model validation is the blindfold prediction when the model's predictive power is assessed from how well the model predicts the activity values of compounds that were not considered in any way during the model development/calibration. However, during the development of a QSAR model, it is necessary to obtain some indication of the model's predictive power. This is often done by some form of cross-validation (CV). In this study, the concepts of the predictive power and fitting ability of a multiple linear regression (MLR) QSAR model were examined in the CV context allowing for the presence of outliers. Commonly used predictive power and fitting ability statistics were assessed via Monte Carlo cross-validation when applied to percent human intestinal absorption, blood-brain partition coefficient, and toxicity values of saxitoxin QSAR data sets, as well as three known benchmark data sets with known outlier contamination. It was found that (1) a robust version of MLR should always be preferred over the ordinary-least-squares MLR, regardless of the degree of outlier contamination and that (2) the model's predictive power should only be assessed via robust statistics. The Matlab and java source code used in this study is freely available from the QSAR-BENCH section of www.dmitrykonovalov.org for academic use. The Web site also contains the java-based QSAR-BENCH program, which could be run online via java's Web Start technology (supporting Windows, Mac OSX, Linux/Unix) to reproduce most of the reported results or apply the reported procedures to other data sets.
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http://dx.doi.org/10.1021/ci800209kDOI Listing
October 2008

TECHNICAL ADVANCES: A maximum-likelihood relatedness estimator allowing for negative relatedness values.

Mol Ecol Resour 2008 Mar;8(2):256-63

School of Mathematics, Physics & Information Technology, James Cook University, Townsville, Queensland 4811, Australia, Department of Behavioural Ecology, Zoological Institute, University of Bern, Hinterkappelen, Switzerland.

Previously reported maximum-likelihood pairwise relatedness (r) estimator of Thompson and Milligan (M) was extended to allow for negative r estimates under the regression interpretation of r. This was achieved by establishing the equivalency of the likelihoods used in the kinship program and the likelihoods of Thompson. The new maximum-likelihood (ML) estimator was evaluated by Monte Carlo simulations. It was found that the new ML estimator became unbiased significantly faster compared to the original M estimator when the amount of genotype information was increased. The effects of allele frequency estimation errors on the new and existing relatedness estimators were also considered.
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http://dx.doi.org/10.1111/j.1471-8286.2007.01940.xDOI Listing
March 2008

Statistical confidence for variable selection in QSAR models via Monte Carlo cross-validation.

J Chem Inf Model 2008 Feb 31;48(2):370-83. Epub 2008 Jan 31.

School of Mathematics, Physics and Information Technology, James Cook University, Townsville, Queensland 4811, Australia.

A new variable selection wrapper method named the Monte Carlo variable selection (MCVS) method was developed utilizing the framework of the Monte Carlo cross-validation (MCCV) approach. The MCVS method reports the variable selection results in the most conventional and common measure of statistical hypothesis testing, the P-values, thus allowing for a clear and simple statistical interpretation of the results. The MCVS method is equally applicable to the multiple-linear-regression (MLR)-based or non-MLR-based quantitative structure-activity relationship (QSAR) models. The method was applied to blood-brain barrier (BBB) permeation and human intestinal absorption (HIA) QSAR problems using MLR to demonstrate the workings of the new approach. Starting from more than 1600 molecular descriptors, only two (TPSA(NO) and ALOGP) yielded acceptably low P-values for the BBB and HIA problems, respectively. The new method has been implemented in the QSAR-BENCH v2 program, which is freely available (including its Java source code) from www.dmitrykonovalov.org for academic use.
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http://dx.doi.org/10.1021/ci700283sDOI Listing
February 2008

Benchmarking of QSAR models for blood-brain barrier permeation.

J Chem Inf Model 2007 Jul-Aug;47(4):1648-56. Epub 2007 Jun 30.

School of Mathematics, Physics and Information Technology, James Cook University, Townsville, Australia.

Using the largest available database of 328 blood-brain distribution (logBB) values, a quantitative benchmark was proposed to allow for a consistent comparison of the predictive accuracy of current and future logBB/quantitative structure-activity relationship (-QSAR) models. The usefulness of the benchmark was illustrated by comparing the global and k-nearest neighbors (kNN) multiple-linear regression (MLR) models based on the linear free-energy relationship (LFER) descriptors, and one non-LFER-based MLR model. The leave-one-out (LOO) and leave-group-out Monte Carlo (MC) cross-validation results (q(2) = 0.766, qms = 0.290, and qms(mc) = 0.311) indicated that the LFER-based kNN-MLR model was currently one of the most accurate predictive logBB-QSAR models. The LOO, MC, and kNN-MLR methods have been implemented in the QSAR-BENCH program, which is freely available from www.dmitrykonovalov.org for academic use.
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http://dx.doi.org/10.1021/ci700100fDOI Listing
October 2007

Modified SIMPSON O(n3) algorithm for the full sibship reconstruction problem.

Bioinformatics 2005 Oct 23;21(20):3912-7. Epub 2005 Aug 23.

School of Information Technology, James Cook University Townsville, QLD, Australia.

Motivation: The problem of reconstructing full sibling groups from DNA marker data remains a significant challenge for computational biology. A recently published heuristic algorithm based on Mendelian exclusion rules and the Simpson index was successfully applied to the full sibship reconstruction (FSR) problem. However, the so-called SIMPSON algorithm has an unknown complexity measure, questioning its applicability range.

Results: We present a modified version of the SIMPSON (MS) algorithm that behaves as O(n(3)) and achieves the same or better accuracy when compared with the original algorithm. Performance of the MS algorithm was tested on a variety of simulated diploid population samples to verify its complexity measure and the significant improvement in efficiency (e.g. 100 times faster than SIMPSON in some cases). It has been shown that, in theory, the SIMPSON algorithm runs in non-polynomial time, significantly limiting its usefulness. It has been also verified via simulation experiments that SIMPSON could run in O(n(a)), where a > 3.

Availability: Computer code written in Java is available upon request from the first author.

Contact: [email protected]
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http://dx.doi.org/10.1093/bioinformatics/bti642DOI Listing
October 2005

Partition-distance via the assignment problem.

Bioinformatics 2005 May 3;21(10):2463-8. Epub 2005 Mar 3.

School of Information Technology, James Cook University, Townsville, QLD 4811, Australia.

Motivation: Accuracy testing of various pedigree reconstruction methods requires an efficient algorithm for the calculation of distance between a known partition and its reconstruction. The currently used algorithm of Almudevar and Field takes a prohibitively long time for certain partitions and population sizes.

Results: We present an algorithm that very efficiently reduces the partition-distance calculation to the classic assignment problem of weighted bipartite graphs that has known polynomial-time solutions. The performance of the algorithm is tested against the Almudevar and Field partition-distance algorithm to verify the significant improvement in speed.

Availability: Computer code written in java is available upon request from the first author.
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http://dx.doi.org/10.1093/bioinformatics/bti373DOI Listing
May 2005
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