SciLifeLab
Browse

Nikolay Oskolkov

Biological sciences

Lund

Nikolay Oskolkov is a bioinformatician at Lund University, Sweden, and Science for Life Laboratory (SciLifeLab). He got his PhD in theoretical physics in 2007 and switched to Life Sciences in 2012 working on medical genetics. His research interests include computational biology and data science as well as mathematical statistics and machine learning.

Publications

  • Increased burden of ultra-rare structural variants localizing to boundaries of topologically associated domains in schizophrenia
  • App-based COVID-19 syndromic surveillance and prediction of hospital admissions: The COVID Symptom Study Sweden
  • Author Correction: Increased burden of ultra-rare structural variants localizing to boundaries of topologically associated domains in schizophrenia
  • Epigenetic modulators link mitochondrial redox homeostasis to cardiac function
  • Mercury–Selenium Accumulation Patterns in Muscle Tissue of Two Freshwater Fish Species, Eurasian Perch (Perca fluviatilis) and Vendace (Coregonus albula)
  • Historical RNA expression profiles from the extinct Tasmanian tiger
  • Facilitating accessible, rapid, and appropriate processing of ancient metagenomic data with AMDirT [version 1; peer review: awaiting peer review]
  • Facilitating accessible, rapid, and appropriate processing of ancient metagenomic data with AMDirT [version 1; peer review: 1 approved]
  • Facilitating accessible, rapid, and appropriate processing of ancient metagenomic data with AMDirT [version 1; peer review: 1 approved, 1 approved with reservations]
  • Unravelling the ancient fungal DNA from the Iceman's gut
  • Epigenetic modulators link mitochondrial redox homeostasis to cardiac function in a sex-dependent manner
  • Dimension Reduction Methods for Life Sciences
  • Machine Learning for Computational Biology
  • Facilitating accessible, rapid, and appropriate processing of ancient metagenomic data with AMDirT [version 2; peer review: 1 approved, 2 approved with reservations]
  • Facilitating accessible, rapid, and appropriate processing of ancient metagenomic data with AMDirT [version 2; peer review: 1 approved, 3 approved with reservations]
  • Clustering High-Dimensional Data
  • Single Cell Data Analysis
  • Deep Learning for the Life Sciences
  • Disinfecting eukaryotic reference genomes to improve taxonomic inference from ancient environmental metagenomic data
  • Refining filtering criteria for accurate taxonomic classification of ancient metagenomic data

Nikolay Oskolkov's public data