We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics.
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Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work.
Different quantities that go by the name of entropy are used in variational principles to infer probability distributions from limited data. Shore and Johnson showed that maximizing the Boltzmann-Gibbs form of the entropy ensures that probability distributions inferred satisfy the multiplication rule of probability for independent events in the absence of data coupling such events. Other types of entropies that violate the Shore and Johnson axioms, including nonadditive entropies such as the Tsallis entropy, violate this basic consistency requirement. Here we use the axiomatic framework of Shore and Johnson to show how such nonadditive entropy functions generate biases in probability distributions that are not warranted by the underlying data.
Maintained by the Nextstrain team. Enabled by data from GISAID
Showing 838 of 838 genomes sampled between Dec 2019 and Mar 2020.Latest Nextstrain COVID-19 situation report in English and in other languages. Follow @nextstrain for continual updates to data and analysis.
This phylogeny shows evolutionary relationships of hCoV-19 (or SARS-CoV-2) viruses from the ongoing novel coronavirus COVID-19 pandemic. This phylogeny shows an initial emergence in Wuhan, China, in Nov-Dec 2019 followed by sustained human-to-human transmission leading to sampled infections. Although the genetic relationships among sampled viruses are quite clear, there is considerable uncertainty surrounding estimates of transmission dates and in reconstruction of geographic spread. Please be aware that specific inferred transmission patterns are only a hypothesis.
Site numbering and genome structure uses Wuhan-Hu-1/2019 as reference. The phylogeny is rooted relative to early samples from Wuhan. Temporal resolution assumes a nucleotide substitution rate of 8 × 10^-4 subs per site per year. Full details on bioinformatic processing can be found here.
Phylogenetic context of nCoV in SARS-related betacoronaviruses can be seen here.
We gratefully acknowledge the authors, originating and submitting laboratories of the genetic sequence and metadata made available through GISAID on which this research is based. A full listing of all originating and submitting laboratories is available below. An attribution table is available by clicking on "Download Data" at the bottom of the page and then clicking on "Strain Metadata" in the resulting dialog box.
The transgenic-list (tg-l) was created by Peter Sobieszczuk in 1996, to serve the global research community specializing in genetic modifications of laboratory animals. Since then, three academic institutions have hosted the tg-l: the IGBMC in Strasbourg, France; the University of Manchester, UK; and the Imperial College in London, UK. In 2011, the transgenic-list was moved to the ISTT web server. The ISTT would like to acknowledge the excellent work of Peter and his assistants in establishing the list for the benefit of everyone who has been a part of this list. The tg-l has proven to be a valuable source of knowledge and advice, helping many newcomers to the field of animal transgenesis, and facilitating the exchange of protocols and experiences.
The ISTT is most proud to host the tg-l for the benefit of the entire community of scientists, technicians, students and all others, interested in animal transgenesis.
Tg-l members are active researchers at all levels, from graduate students to full professors, and the technicians, managers, and directors who operate transgenic core facilities.
The tg-l is public (subject to email address verification), unmoderated (messages will not be altered by the list administrator) and closed (only subscribers may post messages). The tg-l currently has about 1800 subscribers from all over the world.To see the collection of prior postings to the list, visit the Tg-list Archives. (The current archive is only available to the list members.)
A crowdsourced set of tech, tools and data relating to the Coronavirus Pandemic.
Many professors don’t know how to teach online, and may not know how to improve at it. Our comprehensive guide can help.
With all of the concern the past few weeks about getting courses online, many people are collecting or creating resources for how to get courses online in case of a last minute emergency switch to …
With the rapid spread of Covid-19 (aka “the Coronavirus” in shorthand for now), there has been an explosion of discussions about preparing for quarantines and societal closures of vario…
Welcome, this is a co-authored and rapidly evolving resource. Thank you to those who are helping! Send me a note if you have resources to share too and/or if you’ve found this resource helpful. Contributors include: Jacqueline Wernimont (Dartmouth, USA), Cathy N. Davidson (CUNY Grad College, USA)...
Sometimes the unexpected happens. When it does, VCU will be prepared to keep on teaching and keep on learning. Were we to have a blizzard or some other surprise event, no doubt we will eventually experience a moment where all in-person academic meetings will need to transition to a remote format. If all or part of VCU instructional locations become unavailable or need to be closed, academic continuity can maintain course progression.
Erin Meyer (Erin Meyer)With this tool you can compare how two (or more) cultures build trust, give negative feedback, and make decisions.
Browser support tables for modern web technologies
Operations Dashboard for ArcGIS
On this site we have a curated collection of open textbooks that align with the top 40 highest enrolled 1st and 2nd year post-secondary subject areas in British Columbia. But there are many other places to find open textbooks that have already been developed and are ready for adaptation or adoption. The following resources contain existing open texts and other teaching resources that are openly-licensed and free for educators to use and adapt to their own needs.