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DTSTAMP:20260615T204102Z
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DESCRIPTION:Tumours are highly complex tissues composed of cancerous cells\
 , surrounded by a heterogeneous cellular microenvironment. A tumor’s res
 ponse to treatments is governed by an interaction of the cancer cell’s i
 ntrinsic factors with external influences of the tumor microenvironment. D
 isentangling the heterogeneity within a tumor is a crucial step in the dev
 elopment and utilisation of effective cancer therapies. The single cell se
 quencing technology enables an effective molecular characterisation of sin
 gle cells within the tumor. This technology can help deconvolute heterogen
 eous tumor samples and thus revolutionise personalised medicine. However\,
  a governing challenge in cancer single cell analysis is cell annotation\,
  that is\, the assignment of a particular cell type or a cell state to eac
 h sequenced cell. The identification of tumor cells within single cell or 
 spatial sequencing experiments remains a critical and limiting step for re
 search\, clinical\, and commercial applications. In this webinar\, we will
  discuss these challenges and a novel machine learning pipeline aimed at p
 erforming automatic annotation and distinguishing tumor cells from normal 
 cells at the single cell level.\n\n### About the speaker\n\n[Altuna Akalin
 ](https://al2na.co/) is a bioinformatics scientist and the head of Bioinf
 ormatics and Omics Data Science Platform at the Berlin [Institute of Medi
 cal Systems Biology\, Max Delbrück Center (MDC)](https://www.mdc-berlin.d
 e/bimsb) in Berlin. He has developed computational methods for analysing 
 and integrating large-scale genomics data sets since 2002. He uses machine
  learning and statistics to uncover biological patterns\, for example thos
 e related to disease state and type. In his current work he uses complex m
 olecular signatures to provide decision support systems for disease diagno
 stics and biomarker discovery. Additionally\, he is actively involved in o
 rganising and teaching at computational genomics courses.
LOCATION:\, 
SUMMARY:Identifying tumor cells at the single cell level through machine le
 arning
URL;VALUE=URI:https://www.ebi.ac.uk/training/events/identifying-tumor-cells
 -single-cell-level-through-machine-learning
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