Optimal density of bacterial cells von Pang, Tin Yau, Lercher, Martin J

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Deep learning allows genome-scale prediction of Michaelis constants from structural features von Kroll, Alexander, Engqvist, Martin K. M., Heckmann, David, Lercher, Martin J.

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Evolview v3: a webserver for visualization, annotation, and management of phylogenetic trees von Subramanian, Balakrishnan, Gao, Shenghan, Lercher, Martin J, Hu, Songnian, Chen, Wei-Hua

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A hypothesis is a liability von Yanai, Itai, Lercher, Martin

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Mathematical properties of optimal fluxes in cellular reaction networks at balanced growth von Dourado, Hugo, Liebermeister, Wolfram, Ebenhöh, Oliver, Lercher, Martin J

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An analytical theory of balanced cellular growth von Dourado, Hugo, Lercher, Martin J.

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Evolview v2: an online visualization and management tool for customized and annotated phylogenetic trees von He, Zilong, Zhang, Huangkai, Gao, Shenghan, Lercher, Martin J, Chen, Wei-Hua, Hu, Songnian

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A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships von Kroll, Alexander, Ranjan, Sahasra, Lercher, Martin J

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Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal von Fritzemeier, Claus Jonathan, Hartleb, Daniel, Szappanos, Balázs, Papp, Balázs, Lercher, Martin J

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An optimal growth law for RNA composition and its partial implementation through ribosomal and tRNA gene locations in bacterial genomes von Hu, Xiao-Pan, Lercher, Martin J.

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On the optimality of the enzyme-substrate relationship in bacteria von Dourado, Hugo, Mori, Matteo, Hwa, Terence, Lercher, Martin J.

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PopGenome: an efficient Swiss army knife for population genomic analyses in R von Pfeifer, Bastian, Wittelsbürger, Ulrich, Ramos-Onsins, Sebastian E, Lercher, Martin J

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A general model to predict small molecule substrates of enzymes based on machine and deep learning von Kroll, Alexander, Ranjan, Sahasra, Engqvist, Martin K. M., Lercher, Martin J.

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It takes two to think von Yanai, Itai, Lercher, Martin J.

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What puzzle are you in? von Yanai, Itai, Lercher, Martin J.

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Night science von Yanai, Itai, Lercher, Martin

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The two languages of science von Yanai, Itai, Lercher, Martin

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Improvisational science von Yanai, Itai, Lercher, Martin

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Minimal regulatory spaces in yeast genomes von Chen, Wei-Hua, Wei, Wu, Lercher, Martin J

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Make science disruptive again von Yanai, Itai, Lercher, Martin J.

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