Vladimir Makarenkov

发表

A-Wardpβ: Effective hierarchical clustering using the Minkowski metric and a fast k-means initialisation

Boris G. Mirkin, Renato Cordeiro de Amorim, Vladimir Makarenkov, 2016, Inf. Sci..

Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening

Robert Nadon, Vladimir Makarenkov, Bogdan Mazoure, 2018, SLAS discovery : advancing life sciences R & D.

New Efficient Algorithm for Modeling Partial and Complete Gene Transfer Scenarios

Vladimir Makarenkov, Alix Boc, Charles F. Delwiche, 2006, Data Science and Classification.

Circular orders of tree metrics, and their uses for the reconstruction and fitting of phylogenetic trees

Vladimir Makarenkov, Bruno Leclerc, V. Makarenkov, 1996, Mathematical Hierarchies and Biology.

Comparison of Additive Trees Using Circular Orders

Vladimir Makarenkov, Bruno Leclerc, V. Makarenkov, 2000, J. Comput. Biol..

An efficient method for the detection and elimination of systematic error in high-throughput screening

Robert Nadon, Andrei V. Gagarin, Vladimir Makarenkov, 2007, Bioinform..

Ancestors 1.0: a web server for ancestral sequence reconstruction

Mathieu Blanchette, Vladimir Makarenkov, Abdoulaye Baniré Diallo, 2010, Bioinform..

Using the stability of objects to determine the number of clusters in datasets

François-Joseph Lapointe, Vladimir Makarenkov, Etienne Lord, 2017, Inf. Sci..

Finding Maximum Likelihood Indel Scenarios

Mathieu Blanchette, Vladimir Makarenkov, Abdoulaye Baniré Diallo, 2006, Comparative Genomics.

Core Clustering as a Tool for Tackling Noise in Cluster Labels

Renato Cordeiro de Amorim, Vladimir Makarenkov, Boris Mirkin, 2019, J. Classif..

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

U. Rajendra Acharya, Moloud Abdar, Vladimir Makarenkov, 2019, IEEE Access.

Virtual High Throughput Screening Using Machine Learning Methods

Vladimir Makarenkov, Cherif Mballo, 2010 .

Using Unsupervised Machine Learning Methods in High-Throughput Screening

Vladimir Makarenkov, Chérif Mballo, V. Makarenkov, 2010, DMIN.

Using Clustering Techniques to Improve Hit Selection in High-Throughput Screening

Vladimir Makarenkov, Pablo Zentilli, Andrei Gagarin, 2006, Journal of biomolecular screening.

Classification of the Indo-European Languages Using a Phylogenetic Network Approach

Vladimir Makarenkov, Anna Maria Di Sciullo, Alix Boc, 2010 .

Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces.

Joelle Pineau, Doina Precup, Thang Doan, 2020 .

Transfer index, NetUniFrac and some useful shortest path-based distances for community analysis in sequence similarity networks

Vladimir Makarenkov, Henry Xing, Steven W Kembel, 2020, Bioinform..

Classification of bioinformatics workflows using weighted versions of partitioning and hierarchical clustering algorithms

Vladimir Makarenkov, Abdoulaye Baniré Diallo, Etienne Lord, 2015, BMC Bioinformatics.

Generalized trees related with tree metrics

Alain Guénoche, Vladimir Makarenkov, Bruno Leclerc, 2000, Electron. Notes Discret. Math..

Exact and Heuristic Algorithms for the Indel Maximum Likelihood Problem

Mathieu Blanchette, Vladimir Makarenkov, Abdoulaye Baniré Diallo, 2007, J. Comput. Biol..

A New Effective Method for Elimination of Systematic Error in Experimental High-Throughput Screening

Robert Nadon, Vladimir Makarenkov, Plamen Dragiev, 2013, Algorithms from and for Nature and Life.

New Efficient Algorithm for Detection of Horizontal Gene Transfer Events

Vladimir Makarenkov, Alix Boc, V. Makarenkov, 2003, WABI.

A Hybrid Latent Space Data Fusion Method for Multimodal Emotion Recognition

Moloud Abdar, Neil Y. Yen, Mohammad Ehsan Basiri, 2019, IEEE Access.

Two effective methods for correcting experimental high-throughput screening data

Robert Nadon, Vladimir Makarenkov, Plamen Dragiev, 2012, Bioinform..

The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning

Boris G. Mirkin, Renato Cordeiro de Amorim, Vladimir Makarenkov, 2017, Pattern Recognit..

Using machine learning methods to predict experimental high-throughput screening data.

Vladimir Makarenkov, Chérif Mballo, 2010, Combinatorial chemistry & high throughput screening.

An Evolutionary Study of the Human Papillomavirus Genomes

Mathieu Blanchette, Vladimir Makarenkov, Abdoulaye Baniré Diallo, 2008, RECOMB-CG.

T-REX: a web server for inferring, validating and visualizing phylogenetic trees and networks

Vladimir Makarenkov, Alix Boc, Alpha Boubacar Diallo, 2012, Nucleic Acids Res..

VecHGrad for Solving Accurately Tensor Decomposition

Vladimir Makarenkov, Jérémy Charlier, 2020, Canadian Conference on AI.

XtracTree for Regulator Validation of Bagging Methods Used in Retail Banking pdf

Vladimir Makarenkov, Jeremy Charlier, 2020, ArXiv.

Systematic error detection in experimental high-throughput screening

Robert Nadon, Vladimir Makarenkov, Plamen Dragiev, 2011, BMC Bioinformatics.

Identification of patient classes in low back pain data using crisp and fuzzy clustering methods

Vladimir Makarenkov, Alexandre Gondeau, V. Makarenkov, 2019 .

On some relations between 2-trees and tree metrics

Vladimir Makarenkov, Bruno Leclerc, 1998, Discret. Math..

Provably efficient reconstruction of policy networks pdf

Joelle Pineau, Doina Precup, Thang Doan, 2020, ArXiv.

DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring

U. Rajendra Acharya, Moloud Abdar, Vladimir Makarenkov, 2020, Inf. Sci..

Optimal Variable Weighting for Ultrametric and Additive Trees and K-means Partitioning: Methods and Software

Vladimir Makarenkov, Pierre Legendre, P. Legendre, 2001, J. Classif..

Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions

Robert Nadon, Vladimir Makarenkov, Iurie Caraus, 2015, Briefings Bioinform..

A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges pdf

Li Liu, Xiaochun Cao, Abbas Khosravi, 2020, Inf. Fusion.

An Efficient Algorithm for the Detection and Classification of Horizontal Gene Transfer Events and Identification of Mosaic Genes

Vladimir Makarenkov, Alix Boc, Pierre Legendre, 2013, Algorithms from and for Nature and Life.

A mixed solution-based high agreement filtering method for class noise detection in binary classification

Moloud Abdar, Mohammad Ehsan Basiri, Ebrahim Akbari, 2020 .

IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment

U. Rajendra Acharya, Roohallah Alizadehsani, Moloud Abdar, 2019, Journal of Medical Systems.

BRIDES: A New Fast Algorithm and Software for Characterizing Evolving Similarity Networks Using Breakthroughs, Roadblocks, Impasses, Detours, Equals and Shortcuts

François-Joseph Lapointe, Vladimir Makarenkov, Etienne Lord, 2016, PloS one.

A new efficient algorithm for inferring explicit hybridization networks following the Neighbor-Joining principle

Vladimir Makarenkov, Matthieu Willems, Nadia Tahiri, 2014, J. Bioinform. Comput. Biol..

A Whole Genome Study and Identification of Specific Carcinogenic Regions of the Human Papilloma Viruses

Mathieu Blanchette, Vladimir Makarenkov, Abdoulaye Baniré Diallo, 2009, J. Comput. Biol..

A weighted least-squares approach for inferring phylogenies from incomplete distance matrices

François-Joseph Lapointe, Vladimir Makarenkov, F. Lapointe, 2004, Bioinform..

Comparison of Two Methods for Detecting and Correcting Systematic Error in High-throughput Screening Data

Andrei V. Gagarin, Vladimir Makarenkov, Pablo Zentilli, 2006, Data Science and Classification.

Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies

Robert Nadon, Vladimir Makarenkov, Bogdan Mazoure, 2017, Scientific Reports.

From a Phylogenetic Tree to a Reticulated Network

Vladimir Makarenkov, Pierre Legendre, P. Legendre, 2004, J. Comput. Biol..

Effect of Hundreds Sequenced Genomes on the Classification of Human Papillomaviruses

Vladimir Makarenkov, Abdoulaye Baniré Diallo, Bruno Daigle, 2013, ECDA.

Spatial and species compositional networks for inferring connectivity patterns in ecological communities

Vladimir Makarenkov, Mehdi Layeghifard, Pedro R. Peres-Neto, 2015 .

On the extension of a partial metric to a tree metric

Alain Guénoche, Vladimir Makarenkov, Bruno Leclerc, 2004, Discret. Math..

Improving cluster recovery with feature rescaling factors pdf

Renato Cordeiro de Amorim, Vladimir Makarenkov, V. Makarenkov, 2020, Applied Intelligence.

T-REX: reconstructing and visualizing phylogenetic trees and reticulation networks

Vladimir Makarenkov, V. Makarenkov, 2001, Bioinform..

A Novel Effective Ensemble Model for Early Detection of Coronary Artery Disease

Moloud Abdar, Vladimir Makarenkov, Nadia Tahiri, 2019 .

HTS-Corrector: software for the statistical analysis and correction of experimental high-throughput screening data

Robert Nadon, Andrei V. Gagarin, Vladimir Makarenkov, 2006, Bioinform..

A new effective method for estimating missing values in the sequence data prior to phylogenetic analysis

Vladimir Makarenkov, Abdoulaye Baniré Diallo, François-Joseph Lapointe, 2006, Evolutionary bioinformatics online.

Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

Renato Cordeiro de Amorim, Vladimir Makarenkov, V. Makarenkov, 2016, Neurocomputing.

Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index

Vladimir Makarenkov, Alix Boc, Abdoulaye Baniré Diallo, 2011, BMC Bioinformatics.

Detecting and removing multiplicative spatial bias in high‐throughput screening technologies

Robert Nadon, Vladimir Makarenkov, Bogdan Mazoure, 2017, Bioinform..

Fast Mining of Iceberg Lattices: A Modular Approach Using Generators

Amedeo Napoli, Petko Valtchev, Robert Godin, 2011, CLA.

A New Efficient Method for Assessing Missing Nucleotides in DNA Sequences in the Framework of a Generic Evolutionary Model

Mathieu Blanchette, François-Joseph Lapointe, Vladimir Makarenkov, 2006, Data Science and Classification.

SimPlot++: a Python application for representing sequence similarity and detecting recombination pdf

Vladimir Makarenkov, Etienne Lord, Stéphane Samson, 2021, ArXiv.

DUNEScan: A Web Application for Uncertainty Estimation in Skin Cancer Detection with Deep Neural Networks

Vladimir Makarenkov, Bogdan Mazoure, Jocelyn Bédard, 2021 .

A Fast Electric Vehicle Planner Using Clustering

Vladimir Makarenkov, Éric Beaudry, Jaël Champagne Gareau, 2021 .

Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning

Saeid Nahavandi, Abbas Khosravi, Moloud Abdar, 2021, Comput. Biol. Medicine.

Accurate deep learning off-target prediction with novel sgRNA-DNA sequence encoding in CRISPR-Cas9 gene editing

Robert Nadon, Vladimir Makarenkov, Jérémy Charlier, 2021, Bioinform..

Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance

Vladimir Makarenkov, Bernard Fichet, Nadia Tahiri, 2021, bioRxiv.

Improving cluster recovery with feature rescaling factors

Renato Cordeiro de Amorim, Vladimir Makarenkov, 2021, Appl. Intell..

Automated detection of shockable ECG signals: A review

Saeid Nahavandi, Ahmed A. Abd El-Latif, Abbas Khosravi, 2021, Inf. Sci..

Incomplete distance matrices, supertrees and bat phylogeny.

Vladimir Makarenkov, Claudine Levasseur, F. Lapointe, 2003, Molecular phylogenetics and evolution.

Reconstruction of biogeographic and evolutionary networks using reticulograms.

Vladimir Makarenkov, Pierre Legendre, P. Legendre, 2002, Systematic biology.

Using directed phylogenetic networks to retrace species dispersal history.

Vladimir Makarenkov, Mehdi Layeghifard, Pedro R Peres-Neto, 2012, Molecular phylogenetics and evolution.

Phylogenetic Network Construction Approaches

Vladimir Makarenkov, Pierre Legendre, P. Legendre, 2006 .

Modeling functional specialization of a cell colony under different fecundity and viability rates and resource constraint

Vladimir Makarenkov, V. Makarenkov, Denis Tverskoi, 2018, PloS one.

Towards an accurate identification of mosaic genes and partial horizontal gene transfers

Vladimir Makarenkov, Alix Boc, V. Makarenkov, 2011, Nucleic acids research.

Armadillo 1.1: An Original Workflow Platform for Designing and Conducting Phylogenetic Analysis and Simulations

Vladimir Makarenkov, Alix Boc, Abdoulaye Baniré Diallo, 2012, PloS one.

A new fast method for inferring multiple consensus trees using k-medoids

Vladimir Makarenkov, Nadia Tahiri, V. Makarenkov, 2018, BMC Evolutionary Biology.

Weighted bootstrapping: a correction method for assessing the robustness of phylogenetic trees

François-Joseph Lapointe, Vladimir Makarenkov, Alix Boc, 2010, BMC Evolutionary Biology.

An Algorithm for the Fitting of a Tree Metric According to a Weighted Least-Squares Criterion

Vladimir Makarenkov, Bruno Leclerc, V. Makarenkov, 1999 .

Nonlinear redundancy analysis and canonical correspondence analysis based on polynomial regression

Vladimir Makarenkov, Pierre Legendre, P. Legendre, 2002 .

Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin

Vladimir Makarenkov, Bogdan Mazoure, Guillaume Rabusseau, 2020, BMC Ecology and Evolution.

Improving the Additive Tree Representation of a Dissimilarity Matrix Using Reticulations

Vladimir Makarenkov, Pierre Legendre, P. Legendre, 2000 .

Identification of Specific Genomic Regions Responsible for the Invasivity of Neisseria Meningitidis

Vladimir Makarenkov, Abdoulaye Baniré Diallo, Dunarel Badescu, 2010 .

Comparison of Four Methods for Inferring Additive Trees from Incomplete Dissimilarity Matrices

Vladimir Makarenkov, V. Makarenkov, 2002 .

Evolutionary history of bacteriophages with double-stranded DNA genomes

Jing Liu, Vladimir Makarenkov, Arcady Mushegian, 2007, Biology Direct.