D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 38 Citations 12,939 97 World Ranking 4995 National Ranking 2452

Overview

What is he best known for?

The fields of study he is best known for:

  • Programming language
  • Software
  • Artificial intelligence

Michael Hucka spends much of his time researching SBML, BioModels Database, Systems biology, Resource and Systems Biology Ontology. The various areas that Michael Hucka examines in his SBML study include CellML and Programming language. He mostly deals with Systems Biology Graphical Notation in his studies of Programming language.

He combines subjects such as Data mining, Software, Software suite, Toolbox and Data science with his study of Systems biology. The study incorporates disciplines such as Annotation, Controlled vocabulary, Information retrieval, Computational biology and Scope in addition to Resource. While the research belongs to areas of Systems Biology Ontology, he spends his time largely on the problem of Modelling biological systems, intersecting his research to questions surrounding Physiome.

His most cited work include:

  • The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. (2637 citations)
  • A community-driven global reconstruction of human metabolism (764 citations)
  • The Systems Biology Graphical Notation (670 citations)

What are the main themes of his work throughout his whole career to date?

SBML, Software, Programming language, Systems biology and Markup language are his primary areas of study. His SBML research is multidisciplinary, relying on both Theoretical computer science and Computational model. The Software development research Michael Hucka does as part of his general Software study is frequently linked to other disciplines of science, such as Reuse, therefore creating a link between diverse domains of science.

His Systems biology study which covers Data science that intersects with Variety. Michael Hucka has included themes like Software system and File format in his Markup language study. His work deals with themes such as CellML, Resource and Information retrieval, which intersect with BioModels Database.

He most often published in these fields:

  • SBML (46.27%)
  • Software (38.06%)
  • Programming language (33.58%)

What were the highlights of his more recent work (between 2016-2020)?

  • SBML (46.27%)
  • Programming language (33.58%)
  • Systems biology (33.58%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in SBML, Programming language, Systems biology, Interoperability and Data science. SBML is closely attributed to CellML in his work. In general Programming language, his work in Software package and Correctness is often linked to Test suite, Conformance testing and Nostril linking many areas of study.

The concepts of his Systems biology study are interwoven with issues in Software, Java annotation, Software engineering and Synthetic biology. His Interoperability study incorporates themes from Set, Protocol, Computational model and Visualization. His Data science study integrates concerns from other disciplines, such as Variety, Field and Source code.

Between 2016 and 2020, his most popular works were:

  • Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0 (297 citations)
  • The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core. (121 citations)
  • SBML Level 3: an extensible format for the exchange and reuse of biological models (42 citations)

In his most recent research, the most cited papers focused on:

  • Programming language
  • Software
  • Artificial intelligence

His primary areas of study are Interoperability, Computational model, SBML, Software and Protocol. His work in Computational model addresses subjects such as Annotation, which are connected to disciplines such as Information retrieval, Controlled vocabulary, Python and CellML. SBML is a subfield of Markup language that he investigates.

His study in Markup language is interdisciplinary in nature, drawing from both Programming language and Software system. His study in Systems biology extends to Software with its themes. As a member of one scientific family, Michael Hucka mostly works in the field of Protocol, focusing on Cobra and, on occasion, Visualization, Graph drawing and Systems engineering.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

M. Hucka;A. Finney;H. M. Sauro;H. Bolouri;H. Bolouri.
Bioinformatics (2003)

3338 Citations

A community-driven global reconstruction of human metabolism

Ines Thiele;Neil Swainston;Ronan M T Fleming;Andreas Hoppe.
Nature Biotechnology (2013)

996 Citations

The Systems Biology Graphical Notation

Nicolas Le Novere;Michael Hucka;Huaiyu Mi;Stuart Moodie.
Nature Biotechnology (2009)

898 Citations

BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems

Nicolas Le Novère;Benjamin J. Bornstein;Alexander Broicher;Mélanie Courtot.
Nucleic Acids Research (2006)

897 Citations

The BioPAX community standard for pathway data sharing

Emek Demir;Emek Demir;Michael P. Cary;Suzanne Paley;Ken Fukuda.
Nature Biotechnology (2010)

650 Citations

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Markus Herrgard;Neil Swainston;Paul Dobson;Warwick B. Dunn.
Nature Biotechnology (2008)

630 Citations

Minimum information requested in the annotation of biochemical models (MIRIAM)

Nicolas Le Novère;Andrew Finney;Michael Hucka;Upinder S. Bhalla.
Nature Biotechnology (2005)

624 Citations

Biomodels database: an enhanced curated and annotated resource for published quantitative kinetic models

Chen Li;Marco Donizelli;Nicolas Rodriguez;Harish Dharuri.
BMC Systems Biology (2010)

596 Citations

Rules for Modeling Signal-Transduction Systems

William S. Hlavacek;James R. Faeder;Michael L. Blinov;Richard G. Posner.
Science Signaling (2006)

433 Citations

LibSBML: an API library for SBML.

Benjamin J. Bornstein;Sarah M. Keating;Akiya Jouraku;Michael Hucka.
Bioinformatics (2008)

415 Citations

Best Scientists Citing Michael Hucka

Jens Nielsen

Jens Nielsen

Chalmers University of Technology

Publications: 114

Pedro Mendes

Pedro Mendes

University of Connecticut

Publications: 94

Bernhard O. Palsson

Bernhard O. Palsson

University of California, San Diego

Publications: 78

Hans V. Westerhoff

Hans V. Westerhoff

Vrije Universiteit Amsterdam

Publications: 78

Douglas B. Kell

Douglas B. Kell

University of Liverpool

Publications: 77

Ines Thiele

Ines Thiele

National University of Ireland, Galway

Publications: 73

Falk Schreiber

Falk Schreiber

University of Konstanz

Publications: 73

Herbert M. Sauro

Herbert M. Sauro

University of Washington

Publications: 70

Edda Klipp

Edda Klipp

Humboldt-Universität zu Berlin

Publications: 63

Andrei Zinovyev

Andrei Zinovyev

PSL Research University

Publications: 63

Nicolas Le Novère

Nicolas Le Novère

Babraham Institute

Publications: 61

Olaf Wolkenhauer

Olaf Wolkenhauer

University of Rostock

Publications: 60

Hiroaki Kitano

Hiroaki Kitano

Okinawa Institute of Science and Technology

Publications: 50

Emmanuel Barillot

Emmanuel Barillot

PSL Research University

Publications: 49

Laurence Calzone

Laurence Calzone

PSL Research University

Publications: 46

Henning Hermjakob

Henning Hermjakob

European Bioinformatics Institute

Publications: 44

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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