Lercher, Martin J.

Chance and necessity in the evolution of minimal metabolic networks

Pál, C., Papp, B., Lercher, M. J., Csermely, P., Oliver, S. G., & Hurst, L. D. (2006) · Nature

Microbial Metabolic Engineering and BioproductionEvolution and Genetic Dynamics

Cited by 239

Comparative Functional Analysis of the Caenorhabditis elegans and Drosophila melanogaster Proteomes

Schrimpf, S. P., Weiss, M., Reiter, L., Ahrens, C. H., Jovanovic, M., Malmström, J., Brunner, E., Mohanty, S., Lercher, M. J., Hunziker, P. E., et al. (2009) · PLoS Biology

Advanced Proteomics Techniques and ApplicationsGenomics and Phylogenetic Studies

Cited by 229

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

Heckmann, D., Lloyd, C. J., Mih, N., Ha, Y., Zielinski, D. C., Haiman, Z. B., Desouki, A. A., Lercher, M. J., & Palsson, B. O. (2018) · Nature Communications

Microbial Metabolic Engineering and BioproductionBioinformatics and Genomic Networks

Cited by 218

A general model to predict small molecule substrates of enzymes based on machine and deep learning

Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. J. (2023) · Nature Communications

Machine Learning in BioinformaticsComputational Drug Discovery Methods

Cited by 193

Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning

Kroll, A., Rousset, Y., Hu, X.-P., Liebrand, N. A., & Lercher, M. J. (2023) · Nature Communications

Machine Learning in BioinformaticsMicrobial Metabolic Engineering and Bioproduction

Cited by 186

Deep learning allows genome-scale prediction of Michaelis constants from structural features

Kroll, A., Engqvist, M. K. M., Heckmann, D., & Lercher, M. J. (2021) · PLOS Biology

Microbial Metabolic Engineering and BioproductionMachine Learning in Bioinformatics

Cited by 132

Shotgun proteomics data from multiple organisms reveals remarkable quantitative conservation of the eukaryotic core proteome

Weiss, M., Schrimpf, S., Hengartner, M. O., Lercher, M. J., & von Mering, C. (2010) · PROTEOMICS

Advanced Proteomics Techniques and ApplicationsBioinformatics and Genomic Networks

Cited by 64

Mathematical properties of optimal fluxes in cellular reaction networks at balanced growth

Dourado, H., Liebermeister, W., Ebenhöh, O., & Lercher, M. J. (2023) · PLOS Computational Biology

Microbial Metabolic Engineering and BioproductionGene Regulatory Network Analysis

Cited by 14