| 1. |
- Marschall, Tobias, et al.
(author)
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Computational pan-genomics : status, promises and challenges
- 2018
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In: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; 19:1, s. 118-135
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Journal article (peer-reviewed)abstract
- Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.
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| 2. |
- Jiao, Wei, et al.
(author)
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A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
- 2020
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11
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Journal article (peer-reviewed)abstract
- In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA.
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| 3. |
- Morreau, Michael, et al.
(author)
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Learning from Ranters : The Effect of Information Resistance on the Epistemic Quality of Social Network Deliberation
- 2022
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In: Social Virtue Epistemology. - New York : Routledge. - 9780367407643 - 9780367808952
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Book chapter (other academic/artistic)abstract
- People who spread misinformation in public debates expose others to the risk of forming false beliefs. Excluding them from participation can limit this exposure, but fact-checking takes up resources of time and money, and censorship violates social and political norms. Here, computer simulations of Bayesian learning in social networks suggest that, in some contexts anyway, the epistemic benefits of excluding sources of misinformation might be small or nonexistent, and not worth associated costs. It is shown more specifically that, under certain conditions, open-minded agents in a network can learn just as well in the presence of false ranters: information resistant agents that repeatedly broadcast falsity within the network. Relevant conditions are that the open-minded agents can keep track of their social sources and maintain appropriate levels of trust in them, and that some sufficiently reliable sources introduce truth into the network.
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| 4. |
- Morreau, Michael, et al.
(author)
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Michael Morreau and Erik J. Olsson's Response to Commentaries
- 2022
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In: Social Virtue Epistemology. - New York : Routledge. - 9780367808952 - 9780367407643
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Book chapter (peer-reviewed)abstract
- This chapter reports preliminary findings from a study of epistemic consequences of misinformation in social networks. It uses the methodology of computer simulation, constructing computer models of Bayesian learning in networks that include untrustworthy sources. Censorship and de-platforming take time and money, and they violate social and democratic norms of inclusiveness and free speech. Enquirers came to recognize false ranters as such, and to treat their testimony as evidence to the contrary. Gardiner in her commentary recognizes that there are “cases where assertions can be a reason to believe the opposite” but claims that these are “marginal, require significant background evidence and context, and are about limited domains of assertion”. Real-life sources of misinformation, Nguyen argues, are harder to identify than our reliably false ranters. They “provide a mixture of true claims and false claims” and “strategically clever misinformation specialists will work to ensure that their true claims are relatively easy to verify, and their false claims relatively hard to”.
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| 5. |
- Olsson, Erik J, et al.
(author)
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Commentaries from Erik J. Olsson
- 2022
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In: Social Virtue Epistemology. - New York : Routledge. - 9780367808952 - 9780367407643
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Book chapter (other academic/artistic)abstract
- In this chapter, the author argues that there are cases in which a nonexpert’s autonomously-formed belief is based on evidence that would otherwise be sufficient for justification, but where this belief is rendered unjustified by (potential) evidence which the nonexpert fails to take into account. He gives various examples in support of his claim. One involves Roger, a food scientist for a large food corporation, who is also an enthusiastic cook. The rules of thumb he has derived from his cooking experience are very reliable, but not as reliable as the scientific method he masters. The author reports that he has argued, in earlier work, that the source of the “ought” is in the normative expectations others are entitled to have based on a person’s participation in various social practices. He thinks that his account underpins a kind of “social-epistemic bootstrapping”, which he thinks is “happy”.
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