{"entity": "researcher", "timestamp": "2026-08-20T20:57:07.367Z", "family": "Gustafsson", "given": "Mika", "initials": "M", "orcid": "0000-0002-0048-4063", "affiliations": ["Bioinformatics, Department of Physics, Chemistry and Biology, Link\u00f6ping University, Link\u00f6ping, Sweden. mika.gustafsson@liu.se."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/d87fbeece2db4a9dbaf2254bfd9a1100.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/d87fbeece2db4a9dbaf2254bfd9a1100"}}, "publications": [{"entity": "publication", "iuid": "355b1be279a146b7910a98c767eace44", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/355b1be279a146b7910a98c767eace44.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/355b1be279a146b7910a98c767eace44"}}, "title": "Deep neural network prediction of genome-wide transcriptome signatures - beyond the Black-box.", "authors": [{"family": "Magnusson", "given": "Rasmus", "initials": "R", "orcid": "0000-0001-9395-6025", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1f30adaf454d431c9680b9a95aefeac1.json"}}, {"family": "Tegn\u00e9r", "given": "Jesper N", "initials": "JN", "orcid": "0000-0002-9568-5588", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b115e5faf68a4acbb6f947c711fd37ec.json"}}, {"family": "Gustafsson", "given": "Mika", "initials": "M", "orcid": "0000-0002-0048-4063", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d87fbeece2db4a9dbaf2254bfd9a1100.json"}}], "type": "journal article", "published": "2022-02-23", "journal": {"title": "NPJ Syst Biol Appl", "issn": "2056-7189", "volume": "8", "issue": "1", "pages": "9", "issn-l": "2056-7189"}, "abstract": "Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). We find that the expression of 1600 TFs can explain >95% of the variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an over-representation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the dysregulation of the target genes (rho = 0.61, P < 10-216). By inspecting the model, key causative TFs could be extracted for subsequent validation using disease-associated genetic variants. We demonstrate a methodology for constructing an interpretable neural network predictor, where analyses of the predictors identified key TFs that were inducing transcriptional changes during disease.", "doi": "10.1038/s41540-022-00218-9", "pmid": "35197482", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8866467"}, {"db": "pii", "key": "10.1038/s41540-022-00218-9"}], "notes": [], "created": "2026-08-20T08:54:43.920Z", "modified": "2026-08-20T08:54:44.035Z"}, {"entity": "publication", "iuid": "7c1cb7d6bb354c10bd7b51364f4bb4d1", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/7c1cb7d6bb354c10bd7b51364f4bb4d1.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/7c1cb7d6bb354c10bd7b51364f4bb4d1"}}, "title": "Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder.", "authors": [{"family": "Dwivedi", "given": "Sanjiv K", "initials": "SK", "orcid": "0000-0003-3400-4133", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fa4b2a4afa3942beac5115b5ab505825.json"}}, {"family": "Tj\u00e4rnberg", "given": "Andreas", "initials": "A", "orcid": "0000-0003-0064-1791", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2d4d51b0b08948cd8be856ec4c0a50b2.json"}}, {"family": "Tegn\u00e9r", "given": "Jesper", "initials": "J", "orcid": "0000-0002-9568-5588", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b115e5faf68a4acbb6f947c711fd37ec.json"}}, {"family": "Gustafsson", "given": "Mika", "initials": "M", "orcid": "0000-0002-0048-4063", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d87fbeece2db4a9dbaf2254bfd9a1100.json"}}], "type": "journal article", "published": "2020-02-12", "journal": {"title": "Nat Commun", "issn": "2041-1723", "volume": "11", "issue": "1", "pages": "856", "issn-l": "2041-1723"}, "abstract": "Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein-protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes.", "doi": "10.1038/s41467-020-14666-6", "pmid": "32051402", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC7016183"}, {"db": "pii", "key": "10.1038/s41467-020-14666-6"}], "notes": [], "created": "2026-08-20T08:51:03.573Z", "modified": "2026-08-20T08:51:03.799Z"}]}