Najnowsze osiągnięcia w bioinformatyce

Prowadzący: Bartosz Czech, Magdalena Frąszczak, Michalna Jakimowicz, Barbara Kosinska-Selbi, Magda Mielczarek, Tomasz Suchocki, Joanna Szyda

Wykłady

Ćwiczenia:

  • 1. 05.10: Zajęcia organizacyjne
  • 2. 12.10:

K. Nowak: Deep learning for healthcare: review, opportunities and challenges
Z. Łozowicka: A computational toolset for rapid identification of SARS-CoV-2, other viruses and microorganisms from sequencing data
H. Gabryszak: Community curation of bioinformatics software and data resources
A. Wróbel: Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics

  • 3. 19.10:

J. Ostapiuk: MODELOWANIE EPIDEMIOLOGICZNE DEDYKOWANE POLSCE
D. Słomian: LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting

  • 4. 26.10:

E. Piszczek: Variable Number Tandem Repeats mediate the expression of proximal genes
M. Gabryś: MolTrans: Molecular interaction transformer for drug target interaction prediction
E. Stachowiak: An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data
J. Olszewski: Inference attacks against differentially private query results from genomic datasets including dependent tuples

  • 5. 09.11:

D. Sikorski: Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery
K. Nowak: Computational modeling of cellular structures using conditional deep generative networks

  • 6. 16.11:

E. Stachowiak: A Simplified Quantitative Real-Time PCR Assay for Monitoring SARS-CoV-2 Growth in Cell Culture
M. Groch: VirHostNet: a knowledge base for the management and the analysis of proteome-wide virus–host interaction networks
E. Piszczek: A computational toolset for rapid identification of SARS-CoV-2, other viruses and microorganisms from sequencing data
J. Kawalec: Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes
D. Boginskaya: BioAider: An efficient tool for viral genome analysis and its application in tracing SARS-CoV-2 transmission

  • 7. 23.11:

J. Szafrańska: Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods
K. Siewiera: Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2
E. Płużek: In-silico approaches to detect inhibitors of the human severe acute respiratory syndrome coronavirus envelope protein ion channel
K. Nowak: Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2
Y. Nikanovich: Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes

  • 8. 30.11:

K. Sałata: Visualizing COVID-19 pandemic risk through network connectedness
J. Olszewski: Analysing the Covid-19 Cases in Kerala: a Visual Exploratory Data Analysis Approach
Z. Łozowicka: A Visual Approach for the SARS (Severe Acute Respiratory Syndrome) Outbreak Data Analysis
O. Khoroshevskyi: COVID19-world: a shiny application to perform comprehensive country-specific data visualization for SARS-CoV-2 epidemic
D. Sikorski: Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review
M. Sołek: Analyzing the epidemiological outbreak of COVID‐19: A visual exploratory data analysis approach
K. Kvitko: Data Visualization and Analyzation of COVID-19

  • 9. 07.12:

K. Siewiera: Analysis of COVID-19 Impact using Data Visualization
J. Szafrańska: Performance characteristics of five immunoassays for SARS-CoV-2: a head-to-head benchmark comparison
H. Gabryszak: Web tools to fight pandemics: the COVID-19 experience
P. Wilaszek: Effects of Weather on Coronavirus Pandemic
F. Kozak: Analysing COVID-19 pandemic through cases, deaths, and recoveries
R. Błach: Visualizing time-related data in biology, a review

  • 10. 14.12:

M. Gabryś: Statistical Modeling of Heart Rate Variability to Unravel the Factors Affecting Autonomic Regulation in Preterm Infants
K. Siewiera: Vitamin D status in early childhood is not associated with cognitive development and linear growth at 6–9 years of age in North Indian children: a cohort study
J. Ostapiuk: Models for the analysis of repeated continuous outcome measures in clinical trials
D. Słomian: Mathematical modeling of the immune system recognition to mammary carcinoma antigen
J. Kawalec: Statistical Modeling of Heart Rate Variability to Unravel the Factors Affecting Autonomic Regulation in Preterm Infants

  • 11. 21.12:

J. Pierścińska: A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure–activity relationship modeling
O. Khoroshevskyi: Machine learning: A New Approach to Drug Discovery
D. Sikorski: Virtual screening for potential inhibitors of b(1,3)-D-glucan synthase as drug candidates against fungal cell wall
K. Kvitko: From machine learning to deep learning: Progress in machine intelligence for rational drug discovery
M. Sołek: Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

  • 12. 04.01:

H. Gabryszak: Exploring and visualizing multidimensional data in translational research platforms
J. Szafrańska: Evaluation on interactive visualization data with scatterplots
K. Sałata: BLASTGrabber: a bioinformatic tool for visualization, analysis and sequence selection of massive BLAST data

  • 13. 12.01:

M. Sołek: Making Sense of the Epigenome Using Data Integration Approaches
F. Kozak: Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification
P. Wilaszek: POTENTIAL MEDICAL APPLICATIONS OF EPIGENETICS: AN OVERVIEW
E. Płużek: Exploring patterns of epigenetic information with data mining techniques
M. Gabryś: Neuropeptide receptor genes GHSR and NMUR1 are candidate epigenetic biomarkers and predictors for surgically treated patients with oropharyngeal cancer

  • 14. 18.01:

J. Pierścińska: A machine learning approach to predict metabolic pathwaydynamics from time-series multiomics data
J. Olszewski: Intestinal Metaproteomics Reveals Host-Microbiota Interactions in Subjects at Risk for Type 1 Diabetes

  • 15. 25.01:

O. Khoroshevskyi: Phylogenetics Algorithms and Applications

Plan prezentacji