{"entity": "researcher", "timestamp": "2026-08-20T21:03:18.124Z", "family": "Hillerton", "given": "Thomas", "initials": "T", "orcid": "0000-0002-6362-0659", "affiliations": ["Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, 17121 Solna, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/07f7f1ec854d4c309f77524357f64400.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/07f7f1ec854d4c309f77524357f64400"}}, "publications": [{"entity": "publication", "iuid": "27e51c5d49b84d9a8e5d54b690400a59", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/27e51c5d49b84d9a8e5d54b690400a59.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/27e51c5d49b84d9a8e5d54b690400a59"}}, "title": "Topology-based metrics for finding the optimal sparsity in gene regulatory network inference.", "authors": [{"family": "Lundqvist", "given": "Nils", "initials": "N", "orcid": "0009-0004-7638-5979", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2e3ded13534544249e258b6c63858cb1.json"}}, {"family": "Garbulowski", "given": "Mateusz", "initials": "M", "orcid": "0000-0002-2497-194X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/98de355d5a1247cebb494b16444857e6.json"}}, {"family": "Hillerton", "given": "Thomas", "initials": "T", "orcid": "0000-0002-6362-0659", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/07f7f1ec854d4c309f77524357f64400.json"}}, {"family": "Sonnhammer", "given": "Erik L L", "initials": "ELL", "orcid": "0000-0002-9015-5588", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8c4fb5f5a50a4dbb91bbea3cc6861ffa.json"}}], "type": "journal article", "published": "2025-05-06", "journal": {"title": "Bioinformatics", "issn": "1367-4811", "volume": "41", "issue": "5", "issn-l": "1367-4803"}, "abstract": "Gene regulatory network (GRN) inference is a complex task aiming to unravel regulatory interactions between genes in a cell. A major shortcoming of most GRN inference methods is that they do not attempt to find the optimal sparsity, i.e. the single best GRN, which is important when applying GRN inference in a real situation. Instead, the sparsity tends to be controlled by an arbitrarily set hyperparameter.\n\nIn this paper, two new methods for predicting the optimal sparsity of GRNs are formulated and benchmarked on simulated perturbation-based gene expression data using four GRN inference methods: LASSO, Zscore, LSCON, and GENIE3. Both sparsity prediction methods are defined using the hypothesis that the topology of real GRNs is scale-free, and are evaluated based on their ability to predict the sparsity of the true GRN. The results show that the new topology-based approaches reliably predict a sparsity close to the true one. This ability is valuable for real-world applications where a single GRN is inferred from real data. In such situations, it is vital to be able to infer a GRN with the correct sparsity.\n\nhttps://bitbucket.org/sonnhammergrni/powerlaw_sparsity/ and https://codeocean.com/capsule/4393635/.", "doi": "10.1093/bioinformatics/btaf120", "pmid": "40127172", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12057811"}, {"db": "pii", "key": "8092552"}], "notes": [], "created": "2026-08-20T09:40:01.145Z", "modified": "2026-08-20T09:40:01.286Z"}, {"entity": "publication", "iuid": "6252faf3867b48f2a93e2961d7453c73", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/6252faf3867b48f2a93e2961d7453c73.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/6252faf3867b48f2a93e2961d7453c73"}}, "title": "GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data.", "authors": [{"family": "Garbulowski", "given": "Mateusz", "initials": "M", "orcid": "0000-0002-2497-194X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/98de355d5a1247cebb494b16444857e6.json"}}, {"family": "Hillerton", "given": "Thomas", "initials": "T", "orcid": "0000-0002-6362-0659", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/07f7f1ec854d4c309f77524357f64400.json"}}, {"family": "Morgan", "given": "Daniel", "initials": "D", "orcid": "0000-0001-8326-6178", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2ae01d9b1d3c487eb66e5379b0faa35e.json"}}, {"family": "Se\u00e7ilmi\u015f", "given": "Deniz", "initials": "D"}, {"family": "Sonnhammer", "given": "Lisbet", "initials": "L"}, {"family": "Tj\u00e4rnberg", "given": "Andreas", "initials": "A"}, {"family": "Nordling", "given": "Torbj\u00f6rn E M", "initials": "TEM", "orcid": "0000-0003-4867-6707", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1ff2e55d28354900a39b69cb1de8c532.json"}}, {"family": "Sonnhammer", "given": "Erik L L", "initials": "ELL"}], "type": "journal article", "published": "2024-09-00", "journal": {"title": "NAR Genomics and Bioinformatics", "issn": "2631-9268", "volume": "6", "issue": "3", "pages": "lqae121", "issn-l": null}, "abstract": "Single-cell data is increasingly used for gene regulatory network (GRN) inference, and benchmarks for this have been developed based on simulated data. However, existing single-cell simulators cannot model the effects of gene perturbations. A further challenge lies in generating large-scale GRNs that often struggle with computational and stability issues. We present GeneSPIDER2, an update of the GeneSPIDER MATLAB toolbox for GRN benchmarking, inference, and analysis. Several software modules have improved capabilities and performance, and new functionalities have been added. A major improvement is the ability to generate large GRNs with biologically realistic topological properties in terms of scale-free degree distribution and modularity. Another major addition is a simulation of single-cell data, which is becoming increasingly popular as input for GRN inference. Specifically, we introduced the unique feature to generate single-cell data based on genetic perturbations. Finally, the simulated single-cell data was compared to real single-cell Perturb-seq data from two cell lines, showing that the synthetic and real data exhibit similar properties.", "doi": "10.1093/nargab/lqae121", "pmid": "39296931", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC11409065"}, {"db": "pii", "key": "lqae121"}, {"db": "figshare", "key": "10.25452/figshare.plus.20029387.v1"}], "notes": [], "created": "2026-08-20T09:42:58.540Z", "modified": "2026-08-20T09:42:58.579Z"}, {"entity": "publication", "iuid": "5ce610c132e144b3a80a22801a4af51f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/5ce610c132e144b3a80a22801a4af51f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/5ce610c132e144b3a80a22801a4af51f"}}, "title": "GRNbenchmark - a web server for benchmarking directed gene regulatory network inference methods.", "authors": [{"family": "Se\u00e7ilmi\u015f", "given": "Deniz", "initials": "D", "orcid": "0000-0001-8284-356X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/66278168a97a4e54a355f2514a9773c9.json"}}, {"family": "Hillerton", "given": "Thomas", "initials": "T", "orcid": "0000-0002-6362-0659", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/07f7f1ec854d4c309f77524357f64400.json"}}, {"family": "Sonnhammer", "given": "Erik L L", "initials": "ELL", "orcid": "0000-0002-9015-5588", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8c4fb5f5a50a4dbb91bbea3cc6861ffa.json"}}], "type": "journal article", "published": "2022-07-05", "journal": {"title": "Nucleic Acids Res.", "issn": "1362-4962", "volume": "50", "issue": "W1", "pages": "W398-W404", "issn-l": "0305-1048"}, "abstract": "Accurate inference of gene regulatory networks (GRN) is an essential component of systems biology, and there is a constant development of new inference methods. The most common approach to assess accuracy for publications is to benchmark the new method against a selection of existing algorithms. This often leads to a very limited comparison, potentially biasing the results, which may stem from tuning the benchmark's properties or incorrect application of other methods. These issues can be avoided by a web server with a broad range of data properties and inference algorithms, that makes it easy to perform comprehensive benchmarking of new methods, and provides a more objective assessment. Here we present https://GRNbenchmark.org/ - a new web server for benchmarking GRN inference methods, which provides the user with a set of benchmarks with several datasets, each spanning a range of properties including multiple noise levels. As soon as the web server has performed the benchmarking, the accuracy results are made privately available to the user via interactive summary plots and underlying curves. The user can then download these results for any purpose, and decide whether or not to make them public to share with the community.", "doi": "10.1093/nar/gkac377", "pmid": "35609981", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9252735"}, {"db": "pii", "key": "6591524"}], "notes": [], "created": "2026-08-20T09:49:50.136Z", "modified": "2026-08-20T09:49:50.212Z"}, {"entity": "publication", "iuid": "629f30362a644ded8147411333833e1e", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/629f30362a644ded8147411333833e1e.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/629f30362a644ded8147411333833e1e"}}, "title": "Fast and accurate gene regulatory network inference by normalized least squares regression.", "authors": [{"family": "Hillerton", "given": "Thomas", "initials": "T", "orcid": "0000-0002-6362-0659", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/07f7f1ec854d4c309f77524357f64400.json"}}, {"family": "Se\u00e7ilmi\u015f", "given": "Deniz", "initials": "D", "orcid": "0000-0001-8284-356X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/66278168a97a4e54a355f2514a9773c9.json"}}, {"family": "Nelander", "given": "Sven", "initials": "S"}, {"family": "Sonnhammer", "given": "Erik L L", "initials": "ELL", "orcid": "0000-0002-9015-5588", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8c4fb5f5a50a4dbb91bbea3cc6861ffa.json"}}], "type": "journal article", "published": "2022-04-12", "journal": {"title": "Bioinformatics", "issn": "1367-4811", "volume": "38", "issue": "8", "pages": "2263-2268", "issn-l": "1367-4803"}, "abstract": "Inferring an accurate gene regulatory network (GRN) has long been a key goal in the field of systems biology. To do this, it is important to find a suitable balance between the maximum number of true positive and the minimum number of false-positive interactions. Another key feature is that the inference method can handle the large size of modern experimental data, meaning the method needs to be both fast and accurate. The Least Squares Cut-Off (LSCO) method can fulfill both these criteria, however as it is based on least squares it is vulnerable to known issues of amplifying extreme values, small or large. In GRN this manifests itself with genes that are erroneously hyper-connected to a large fraction of all genes due to extremely low value fold changes.\n\nWe developed a GRN inference method called Least Squares Cut-Off with Normalization (LSCON) that tackles this problem. LSCON extends the LSCO algorithm by regularization to avoid hyper-connected genes and thereby reduce false positives. The regularization used is based on normalization, which removes effects of extreme values on the fit. We benchmarked LSCON and compared it to Genie3, LASSO, LSCO and Ridge regression, in terms of accuracy, speed and tendency to predict hyper-connected genes. The results show that LSCON achieves better or equal accuracy compared to LASSO, the best existing method, especially for data with extreme values. Thanks to the speed of least squares regression, LSCON does this an order of magnitude faster than LASSO.\n\nData: https://bitbucket.org/sonnhammergrni/lscon; Code: https://bitbucket.org/sonnhammergrni/genespider.\n\nSupplementary data are available at Bioinformatics online.", "doi": "10.1093/bioinformatics/btac103", "pmid": "35176145", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9004640"}, {"db": "pii", "key": "6530276"}], "notes": [], "created": "2026-08-20T09:39:30.922Z", "modified": "2026-08-20T09:39:31.022Z"}]}