World's Best Scientists 2026 revealed!

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Computer Science

D-Index
40
Citations
10858
World Ranking
9086
National Ranking
219

Overview

Martin Weigt is affiliated with Sorbonne University in France and has contributed extensively to the fields of biochemistry, genetics, and molecular biology. Their research covers a range of subfields, including molecular biology, genetics, infectious diseases, ecology, and radiology, nuclear medicine, and imaging.

The scientist's work engages with several core topics in biological sciences, focusing on RNA and protein synthesis mechanisms, genomics and phylogenetic studies, protein structure and dynamics, evolution and genetic dynamics, machine learning applications in bioinformatics, bioinformatics and genomic networks, as well as vaccines and immunoinformatics approaches.

Martin Weigt's notable recent papers include:

  • "An evolution-based model for designing chorismate mutase enzymes" (2020, Science)
  • "Efficient generative modeling of protein sequences using simple autoregressive models" (2021, Nature Communications)
  • "Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes" (2022, Proceedings of the National Academy of Sciences)
  • "Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution" (2022, IRIS Research product catalog, Sapienza University of Rome)
  • "TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes" (2024, Proceedings of the National Academy of Sciences)

Frequent collaborators in the scientist's body of work include Francesco Zamponi, Andrea Pagnani, Juan Rodriguez-Rivas, Giancarlo Croce, and Philippe Nghe. These collaborations have contributed to advancing knowledge in the intersecting domains of protein evolution, genomics, and computational biology.

Martin Weigt has published prolifically in multiple venues, with a significant number of publications appearing in:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Nature Communications
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the National Academy of Sciences

Best Publications

  • Direct-coupling analysis of residue coevolution captures native contacts across many protein families

    Faruck Morcos;Andrea Pagnani;Bryan Lunt;Arianna Bertolino

  • On the properties of small-world network models

    A. Barrat;M. Weigt

  • Identification of direct residue contacts in protein-protein interaction by message passing.

    Martin Weigt;Robert A. White;Hendrik Szurmant;James A. Hoch

  • An evolution-based model for designing chorismate mutase enzymes.

    William P. Russ;Matteo Figliuzzi;Christian Stocker;Pierre Barrat-Charlaix;Pierre Barrat-Charlaix

  • Inverse statistical physics of protein sequences: a key issues review.

    Simona Cocco;Christoph Feinauer;Matteo Figliuzzi;Rémi Monasson

  • Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1

    Matteo Figliuzzi;Hervé Jacquier;Alexander Schug;Olivier Tenaillon

  • Genomics-aided structure prediction

    Joanna I. Sułkowska;Faruck Morcos;Martin Weigt;Terence Hwa

  • Coloring random graphs.

    Roberto Mulet;Andrea Pagnani;Martin Weigt;Riccardo Zecchina

  • High-resolution protein complexes from integrating genomic information with molecular simulation.

    Alexander Schug;Martin Weigt;José N. Onuchic;Terence Hwa

  • Number of guards needed by a museum: a phase transition in vertex covering of random graphs.

    Martin Weigt;Alexander K. Hartmann

  • Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    Evan J. Molinelli;Evan J. Molinelli;Anil Korkut;Weiqing Wang;Martin L. Miller

  • Clustering by soft-constraint affinity propagation

    Michele Leone;Sumedha;Martin Weigt

  • How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins

    Matteo Figliuzzi;Pierre Barrat-Charlaix;Martin Weigt

  • Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

    Eleonora De Leonardis;Eleonora De Leonardis;Benjamin Lutz;Sebastian Ratz;Simona Cocco

  • Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

    Carlo Baldassi;Marco Zamparo;Christoph Feinauer;Andrea Procaccini

  • A variational description of the ground state structure in random satisfiability problems

    Giulio Biroli;Rémi Monasson;Martin Weigt

  • From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.

    Simona Cocco;Remi Monasson;Martin Weigt

  • Simplest random K-satisfiability problem.

    Federico Ricci-Tersenghi;Martin Weigt;Riccardo Zecchina

  • Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

    Thomas Gueudré;Carlo Baldassi;Marco Zamparo;Martin Weigt

  • Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis

    Guido Uguzzoni;Shalini John Lovis;Francesco Oteri;Alexander Schug

Frequent Co-Authors

Terence Hwa
Terence Hwa University of California, San Diego
James A. Hoch
James A. Hoch Scripps Research Institute
Chris Sander
Chris Sander Harvard University
Olivier Tenaillon
Olivier Tenaillon Université Paris Cité
Rama Ranganathan
Rama Ranganathan University of Chicago
Debora S. Marks
Debora S. Marks Harvard University
Claude Thermes
Claude Thermes University of Paris-Saclay
Alain Barrat
Alain Barrat Centre de Physique Théorique
Gordon B. Mills
Gordon B. Mills Oregon Health & Science University
Florent Krzakala
Florent Krzakala École Polytechnique Fédérale de Lausanne

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