{"entity": "journal", "iuid": "3cb81c998fe64e798b94218b1bf306c1", "timestamp": "2026-08-29T04:23:45.055Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/Pharmacol%20Ther.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/Pharmacol%20Ther"}}, "title": "Pharmacol Ther", "issn": "1879-016X", "issn-l": null, "publications_count": 2, "publications": [{"entity": "publication", "iuid": "a6653b3597d842eebde6ce4b210ce3f5", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/a6653b3597d842eebde6ce4b210ce3f5.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/a6653b3597d842eebde6ce4b210ce3f5"}}, "title": "TSLP as druggable target - a silver-lining for atopic diseases?", "authors": [{"family": "Adhikary", "given": "Partho Protim", "initials": "PP"}, {"family": "Tan", "given": "Zheng", "initials": "Z"}, {"family": "Page", "given": "Brent D G", "initials": "BDG"}, {"family": "Hedtrich", "given": "Sarah", "initials": "S"}], "type": "journal article", "published": "2021-01-00", "journal": {"title": "Pharmacol Ther", "issn": "1879-016X", "volume": "217", "pages": "107648", "issn-l": null}, "abstract": "Atopic diseases refer to common allergic inflammatory diseases such as atopic dermatitis (AD), allergic rhinitis (AR), and allergic asthma (AA). AD often develops in early childhood and may herald the onset of other allergic disorders such as food allergy (FA), AR, and AA. This progression of the disease is also known as the atopic march, and it goes hand in hand with a significantly impaired quality of life as well as a significant economic burden. Atopic diseases usually are considered as T helper type 2 (Th2) cell-mediated inflammatory diseases. Thymic stromal lymphopoietin (TSLP), an epithelium-derived pro-inflammatory cytokine, activates distinct immune and non-immune cells. It has been shown to be a master regulator of type 2 immune responses and atopic diseases. In experimental settings, the inhibition or knockout of TSLP signaling has shown great therapeutic potential. This, in conjunction with the increasing knowledge about the central role of TSLP in the pathogenesis of atopic diseases, has sparked an interest in TSLP as a druggable target. In this review, we will discuss the autocrine and paracrine effects of TSLP, how it regulates the tissue microenvironment and drives atopic diseases, which provide the rationale for the increasing interest in TSLP as a druggable target.", "doi": "10.1016/j.pharmthera.2020.107648", "pmid": "32758645", "labels": [], "xrefs": [{"db": "pii", "key": "S0163-7258(20)30178-9"}], "notes": [], "created": "2026-08-21T11:27:23.255Z", "modified": "2026-08-21T11:27:23.283Z"}, {"entity": "publication", "iuid": "f9c72aff23ec495aadbe61a32376078f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f9c72aff23ec495aadbe61a32376078f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f9c72aff23ec495aadbe61a32376078f"}}, "title": "Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining.", "authors": [{"family": "Vougas", "given": "Konstantinos", "initials": "K"}, {"family": "Sakellaropoulos", "given": "Theodore", "initials": "T"}, {"family": "Kotsinas", "given": "Athanassios", "initials": "A"}, {"family": "Foukas", "given": "George-Romanos P", "initials": "GP"}, {"family": "Ntargaras", "given": "Andreas", "initials": "A"}, {"family": "Koinis", "given": "Filippos", "initials": "F"}, {"family": "Polyzos", "given": "Alexander", "initials": "A"}, {"family": "Myrianthopoulos", "given": "Vassilios", "initials": "V"}, {"family": "Zhou", "given": "Hua", "initials": "H"}, {"family": "Narang", "given": "Sonali", "initials": "S"}, {"family": "Georgoulias", "given": "Vassilis", "initials": "V"}, {"family": "Alexopoulos", "given": "Leonidas", "initials": "L"}, {"family": "Aifantis", "given": "Iannis", "initials": "I"}, {"family": "Townsend", "given": "Paul A", "initials": "PA"}, {"family": "Sfikakis", "given": "Petros", "initials": "P"}, {"family": "Fitzgerald", "given": "Rebecca", "initials": "R"}, {"family": "Thanos", "given": "Dimitris", "initials": "D"}, {"family": "Bartek", "given": "Jiri", "initials": "J"}, {"family": "Petty", "given": "Russell", "initials": "R"}, {"family": "Tsirigos", "given": "Aristotelis", "initials": "A"}, {"family": "Gorgoulis", "given": "Vassilis G", "initials": "VG"}], "type": "journal article", "published": "2019-11-00", "journal": {"title": "Pharmacol Ther", "issn": "1879-016X", "volume": "203", "pages": "107395", "issn-l": null}, "abstract": "A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big \"omic\" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.", "doi": "10.1016/j.pharmthera.2019.107395", "pmid": "31374225", "labels": [], "xrefs": [{"db": "pii", "key": "S0163-7258(19)30138-X"}], "notes": [], "created": "2026-08-20T08:03:34.397Z", "modified": "2026-08-20T08:03:34.476Z"}], "created": "2026-08-20T08:03:34.431Z", "modified": "2026-08-20T08:03:34.431Z"}