A turning point for the future treatment of tumours


nature journal

The Pan-Cancer project provides a turning point for further treatment of tumours. More that one thousand scientists from the entire world built to date the most detailed picture of tumours. Their studies were published last week in two articles in the Nature journal and in 4 other articles in Nature Communications and Biology Communications and report an almost complete picture of all tumours. These studies will enable individualised treatment for every patient and an earlier discovery of the tumours.

The Pan-Cancer Consortium analysed the complete genetic code for 2.658 tumours. On average, it was found that there are four to five driver mutations that are responsible for the tumour. These mutations are likely to become the point of attack of anti-tumour treatments and be personalised for each patient. A major focus in the investigations were the non-coding elements that occur in the regulatory regions of the genomes.

It was also possible to “carbon-date” the age of mutations. About 20% of them appear years or even decades prior to the tumour and thus offer a potential for very early diagnosis even before the tumour occurs.

Our employee, Visiting Professor Jan Komorowski, participates in the Pan-Cancer project and is a co-author of the six publications. The main contribution of his team is identification of significant mutations in the regulatory regions in genomes that has been achieved using advanced bioinformatics methods, including machine learning, applied to Big Data.

Rheinbay, E., Nielsen, M.M., Abascal, F. et al. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Nature 578, 102–111 (2020). doi: 10.1038/s41586-020-1965-x,
Campbell, P.J., Getz, G., Korbel, J.O. et al. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020). doi: 10.1038/s41586-020-1969-6,
Carlevaro-Fita, J., Lanzós, A., Feuerbach, L. et al. Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis. Commun Biol 3, 56 (2020). doi: 10.1038/s42003-019-0741-7,
Reyna, M.A., Haan, D., Paczkowska, M. et al. Pathway and network analysis of more than 2500 whole cancer genomes. Nat Commun 11, 729 (2020). doi: 10.1038/s41467-020-14367-0,
Paczkowska, M., Barenboim, J., Sintupisut, N. et al. Integrative pathway enrichment analysis of multivariate omics data. Nat Commun 11, 735 (2020). doi: 10.1038/s41467-019-13983-9,
Shuai, S., Abascal, F., Amin, S.B. et al. Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nat Commun 11, 734 (2020). doi: 10.1038/s41467-019-13929-1

Professor Wojciech Penczek elected a Corresponding Member of the Polish Academy of Sciences


penczek 
ZOPAN WPenczek

Professor Wojciech Penczek, Director of ICS PAS has been elected a Corresponding Member of the Polish Academy of Sciences. The 140 sesion of General Assembly of the Polish Academy of Sciences took plase on December 5. In accordance with the Regulations on the selection of Academy members, candidates for members should be distinguished by their special scientific achievements, environmental authority and good repute.

Congratulations!

 

Picture 1: Private Archive
Picture 2: Jarosław Deluga-Góra / PAS

Work of Piotr Przybyła was accepted at AAAI 2020


aaai20 

Work of Piotr Przybyła on automatic methods that can detect online documents of low credibility was accepted at AAAI 2020.

AAAI conference series is to promote research in artificial intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers in AI and its affiliated disciplines. AAAI-20 is the Thirty-Fourth AAAI Conference on Artificial Intelligence.

In this study we aim to explore automatic methods that can detect online documents of low credibility, especially fake news, based on the style they are written in. We show that general-purpose text classifiers, despite seemingly good performance when evaluated simplistically, in fact overfit to sources of documents in training data. In order to achieve a truly style-based prediction, we gather a corpus of 103,219 documents from 223 online sources labelled by media experts, devise realistic evaluation scenarios and design two new classifiers: a neural network and a model based on stylometric features. The evaluation shows that the proposed classifiers maintain high accuracy in case of documents on previously unseen topics (e.g. new events) and from previously unseen sources (e.g. emerging news websites). An analysis of the stylometric model indicates it indeed focuses on sensational and affective vocabulary, known to be typical for fake news.

3rd International Summer School on Deep Learning


DeepLearn2019 logo 

Institute of Computer Science PAS is a coorganiyer of the 3rd International Summer School on Deep Learning "DeepLearn 2019". Summer School will take place on Julz 22-26, 2019 in Warsaw.

The summer school covers the issues of deep learning.

Summer school website: http://deeplearn2019.irdta.eu

XLIII Conference on Mathematical Statistics 2017


276204

ICSPAS is a co-ogranizer of XLIII Conference on Mathematical Statistics 2017.
The conference will take place in Będlewo, Poland. The conference will start on 4/12/2017 and it will end on 8/12/2017.

The conference covers the issues of mathematical statistics and related applications.

Conference website: https://www.impan.pl/en/activities/banach-center/conferences/17-xliiistatistics

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