It is interesting that domain specific mechanisms always improve performance for other domains. I wonder how performance would be if you applied these mechanisms independently on cross domain tasks.

The values are reset to the original initial random values, or to new random values?

Why would this be beneficial? Obviously this limit prevents pathways from using entire layers, but how does that help the agents find a suitable pathway? Does this make the agent less localized?

Asynchronus Advantage Actor-Critic (A3C)

Agents find pathways in the giant neural network that would be useful in other networks.

This is a nice definition of non-integer dimension.

This is a nice example of self-similarity.

A good description of p-value.

There are many ways to do something wrong, but only one way to do it right! When adhering to a standard, there is only one way that you do it correctly, but many ways to do it incorrectly.

The code is updated on Github at this repository.

The irony is that large sample sizes are needed for rare diseases alleles and small effect size alleles.

It is helpful to note what the axises of a Manhattan Plot represent. As stated, the x-axis is the locus (location of a nucleotide) of the SNP in the genome, and the y-axis is the negative p-value that is scaled using logarithms. This means that the highest points in the Manhattan Plot have the lowest p-values, and are therefore the statistically significant SNPs.

General Linear Model (GLM)- is a statistical linear model that generalizes multiple linear regression models such that there are multiple dependent variables.

The ANOVA analysis compares one nominal variable with one measurement variable. In this case the measurement variable would be the genotype of an individual, and the nominal variable is whether that individual is case or control.

Consanguinity- is the property of being related to someone, in essence having the same ancestor as someone else.

This is a good definition of a haplotype.

It seems that there is no weighting scheme/method that consistently performs the best for each organism and for each validation criteria, except perhaps SIM-ATN.

This is not the case for the Boss Taurus or the Danio rerio cmp groups.

APrate- Annotation Predicted, equivalent to a false positive.

ACrate- Annotation Confirmed, equivalent to a true positive.

Receiver Operating Characteristic (ROC) curves- these curves compare the true positive rate against the false positive rate.

Why were there 102,118 less annotations after 4 years?

missing equation

I think that this is very similar to a softmax layer in a neural network.

I still see a limitation with a bias towards genes that have few or no annotations. How can you cluster based on annotation terms if it has no annotation terms? What about if there are only a few terms for a gene?

Orthonormal matrix- a square matrix where each row and column are orthogonal unit vectors.

Orthogonal- when two vectors are perpendicular.

Unit vector- a vector of length one.

The inverse gene frequency (IGF) function provides a way to quantify how influential a term is based on how far away the term is from the ontology root.

Basically, the terms that are more specific are more important than the general terms.

This is the basis for the weighted matrix:

1. The relevance of a function term increases as there are more descendant terms annotated to the gene.
2. The more rare a term is, the better it describes a set of genes.

1. Hypothesis test, use a t-test or something similar to see if your hypothesis is supported by the data.
2. Find how tightly the variables are associated, calculate r and the higher the r the stronger the relationship is between the two variables (a.k.a. a large r means that one can accurately predict one variable from the other).
3. Determine the equation of a line that generalizes the data, allowing for predictions given only one variable.

I believe that the labels for the x- and y-axis should be switched.

I find it interesting that even simple neural networks can achieve good results. However, I'm not sure that this network would be considered simple given that it has so many parameters, it is hardly a simple function.

I agree that there wouldn't be any problems, but one issue could be in comparing studies that use different intervals for their Likert items.

This is an important concept in Data Science, which allows you to generalize the data so that you can get a high level picture of what the data is. It is important to keep the original measured values so that you can do more precise testing later.

There can be fundamentally different ways to display data, and to analyze using statistics. Keep in mind to use raw values as opposed to percentages, averages, etc. for statistical tests.

Orthologous groups are groups of genes that developed from a common ancestor, source. Orthologous genes have the same (or similar) function, but may vary in sequence.