{"entity": "researcher", "timestamp": "2026-08-20T21:27:39.858Z", "family": "Se\u00e7ilmi\u015f", "given": "Deniz", "initials": "D", "orcid": "0000-0001-8284-356X", "affiliations": ["Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121, Solna, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/66278168a97a4e54a355f2514a9773c9.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/66278168a97a4e54a355f2514a9773c9"}}, "publications": [{"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"}, {"entity": "publication", "iuid": "cc794446873a4751afd9fe7cf4ba2e95", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/cc794446873a4751afd9fe7cf4ba2e95.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/cc794446873a4751afd9fe7cf4ba2e95"}}, "title": "Inferring the experimental design for accurate gene regulatory network inference.", "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"}, {"family": "Nelander", "given": "Sven", "initials": "S"}, {"family": "Sonnhammer", "given": "Erik L L", "initials": "ELL"}], "type": "journal article", "published": "2021-10-25", "journal": {"title": "Bioinformatics", "issn": "1367-4811", "volume": "37", "issue": "20", "pages": "3553-3559", "issn-l": "1367-4803"}, "abstract": "Accurate inference of gene regulatory interactions is of importance for understanding the mechanisms of underlying biological processes. For gene expression data gathered from targeted perturbations, gene regulatory network (GRN) inference methods that use the perturbation design are the top performing methods. However, the connection between the perturbation design and gene expression can be obfuscated due to problems, such as experimental noise or off-target effects, limiting the methods' ability to reconstruct the true GRN.\n\nIn this study, we propose an algorithm, IDEMAX, to infer the effective perturbation design from gene expression data in order to eliminate the potential risk of fitting a disconnected perturbation design to gene expression. We applied IDEMAX to synthetic data from two different data generation tools, GeneNetWeaver and GeneSPIDER, and assessed its effect on the experiment design matrix as well as the accuracy of the GRN inference, followed by application to a real dataset. The results show that our approach consistently improves the accuracy of GRN inference compared to using the intended perturbation design when much of the signal is hidden by noise, which is often the case for real data.\n\nhttps://bitbucket.org/sonnhammergrni/idemax.\n\nSupplementary data are available at Bioinformatics online.", "doi": "10.1093/bioinformatics/btab367", "pmid": "33978748", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8545292"}, {"db": "pii", "key": "6274652"}], "notes": [], "created": "2026-08-20T09:39:25.153Z", "modified": "2026-08-20T09:39:25.175Z"}, {"entity": "publication", "iuid": "f21d910ecaca414e84e9ae52c1d0f35c", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f21d910ecaca414e84e9ae52c1d0f35c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f21d910ecaca414e84e9ae52c1d0f35c"}}, "title": "Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data.", "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"}, {"family": "Morgan", "given": "Daniel", "initials": "D", "orcid": "0000-0001-8326-6178", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2ae01d9b1d3c487eb66e5379b0faa35e.json"}}, {"family": "Tj\u00e4rnberg", "given": "Andreas", "initials": "A", "orcid": "0000-0003-0064-1791", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2d4d51b0b08948cd8be856ec4c0a50b2.json"}}, {"family": "Nelander", "given": "Sven", "initials": "S"}, {"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", "orcid": "0000-0002-9015-5588", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8c4fb5f5a50a4dbb91bbea3cc6861ffa.json"}}], "type": "journal article", "published": "2020-11-09", "journal": {"title": "NPJ Syst Biol Appl", "issn": "2056-7189", "volume": "6", "issue": "1", "pages": "37", "issn-l": "2056-7189"}, "abstract": "The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where ~1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.", "doi": "10.1038/s41540-020-00154-6", "pmid": "33168813", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC7652823"}, {"db": "pii", "key": "10.1038/s41540-020-00154-6"}], "notes": [], "created": "2026-08-20T08:54:42.056Z", "modified": "2026-08-20T08:54:42.243Z"}]}