New paper in Frontiers in Genetics!

Polewko-Klim A, Zhu S, Wu W, Xie Y, Cai N, Zhang K, Zhu Z, Qing T, Yuan Z, Xu K, Zhang T, Lu M, Ye W, Chen X, Suo C and Rudnicki WR. Identification of Candidate Therapeutic Genes for More Precise Treatment of Esophageal Squamous Cell Carcinoma and Adenocarcinoma. Frontiers in Genetics 2022 13:844542. doi: 10.3389/fgene.2022.844542 The standard therapy administered to patients with advanced esophageal cancer remains uniform, despite its…

New paper!

Moszczuk, B.; Krata, N.; Rudnicki, W.; Foroncewicz, B.; Cysewski, D.; Pączek, L.; Kaleta, B.; Mucha, K. Osteopontin—A Potential Biomarker for IgA Nephropathy: Machine Learning Application. Biomedicines 2022, 10, 734. https://doi.org/10.3390/biomedicines10040734 Abstract Many potential biomarkers in nephrology have been studied, but few are currently used in clinical practice. One is osteopontin (OPN). We compared urinary OPN concentrations in 80 participants: 67 patients with various biopsy-proven glomerulopathies (GNs)—immunoglobulin A nephropathy (IgAN, 29),…

New paper in Cells!

M. Pac, N. Krata, B. Moszczuk, A. Wyczałkowska-Tomasik, B. Kaleta, B. Foroncewicz, W. Rudnicki, L. Pączek,K. Mucha NR3C1 Glucocorticoid Receptor Gene Polymorphisms Are Associated with Membranous and IgA Nephropathies Cells 2021, 10(11), 3186 https://doi.org/10.3390/cells10113186 Abstract: Glomerular diseases (GNs) are responsible for approximately 20% of chronic kidney diseases. Glucocorticoid receptor gene (NR3C1) single nucleotide polymorphisms (SNPs) are implicated in differences in predisposition to autoimmunity and steroid sensitivity. The aim of this…

New paper in Frontiers in Genetics!

W. Lesiński, K. Mnich, W.R. Rudnicki. Prediction of alternative Drug Induced Liver Injury classifications using molecular descriptors, gene expression perturbation, and toxicology reportsFrontiers in Genetics 12:661075 (2021), DOI: 10.3389/fgene.2021.661075 (100 pkt. MNiSzW) Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI, based on the chemical properties of substances and experiments performed on cell lines, would bring a significant reduction in the…

New paper in Journal of Medical Systems!

A. Polewko-Klim, K. Mnich, W.R. Rudnicki. Robust Data Integration Method for Classification of Biomedical Data. Journal of Medical Systems 45(4):45 (2021), DOI: 10.1007/s10916-021-01718-7 (100 pkt. MNiSzW) Wczesna diagnoza jest kluczowa dla wyboru i zwiekszenia skuteczność leczenia nowotworów. W niniejszej pracy, wykorzystując szereg zaawansowanych metod uczenia maszynowego, przedstawiliśmy i porównaliśmy różne modele predykcyjne, w tym model hybrydowy, których celem jest określanie klinicznego punktu końcowego pacjenta (KPKP) na podstawie danych klinicznich i…

New paper in Biology Direct!

W. Lesiński, K. Mnich, A. Kitlas Golińska, W.R. Rudnicki. Integration of human cell lines gene expression and chemical properties of drugs for Drug Induced Liver Injury prediction. Biology Direct 16:2 (2021). DOI: 0.1186/s13062-020-00286-z (100 pkt. MNiSzW) Motivation Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined…

Our new paper is out!

A. Polewko-Klim, W. Lesiński, A. Kitlas Golińska, K. Mnich, M. Siwek, W. R. Rudnicki. Sensitivity analysis based on the Random Forest machine learning algorithm identifies candidate genes for regulation of innate and adaptive immune response of chicken. Poultry Science 99 (2020), 12, 6341-6354. DOI: 10.1016/j.psj.2020.08.059 (140 pkt. MNiSzW) Spożywanie zdrowej żywności m.in. produktów drobiarskich ma wielkie znaczenie dla zdrowia ludzi. Zwalczanie chorób drobiu to jeden z największych problemów jego producentów….

Our new conference paper is out!

K. Mnich, A. Polewko-Klim, A. Kitlas Golińska, W. Lesiński and W. R. Rudnicki, “Super Learning with Repeated Cross Validation,” 2020 International Conference on Data Mining Workshops (ICDMW), Sorrento, Italy, 2020, pp. 629-635, doi: 10.1109/ICDMW51313.2020.00089. Super learner algorithm was created to combine results of multiple base learners with the use of cross validation. However, in many cases it does not outperform significantly a simple average of the base results. We propose…