Summations Only

Precisely.We could look at the Theory of Relativity as an example. From that theory, or model, you could hypothesize that starlight would be bent as it passes by the Sun. You can test that hypothesis by measuring the location of stars during an eclipse as well as when the stars are farther away from the Sun. If their position changes with relation to other stars, then the hypothesis is confirmed. You can conclude that starlight is bent as it passes by the Sun. That conclusion supports the larger theory. The Theory of Relativity also makes many other testable predictions about things such as time dilation. Relativity started as a theory, not as a hypothesis.

If there were one or multiple simple original life forms that were created by a deity, and those simple life forms produced the biodiversity we see over billions of years, what would we need to change in the theory of evolution?

As already shown, no amount of evidence or facts will change your views.

If you can't understand that explanation, then you have no place calling evolution a religion, or claiming that it is false.

There is an example in the paper I already cited for you:

quote:

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We selected a well-characterized protein family, the adenosine-5‘-monophosphate (AMP)/adenosine deaminase family, for evaluation of SIFTER's predictions against a gold standard set of function annotations. We assessed these using experimental annotations that we manually identified in the literature, accepting only first-hand experimental results that were successful in unambiguously characterizing the specific chemical reaction in question. References are provided in Dataset S1 for each protein characterized in this way. The gaccuracyh percentages presented here reflect the product of the percentage of proteins that received a prediction and, of those, the percentage that were gcorrect,h i.e., had the same GO terms as the gold standard test set.

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The AMP/adenosine deaminase Pfam family contains 128 proteins. Based on five proteins with experimental annotations from the GOA database, we ran SIFTER to make predictions for the remaining 123 proteins. Of these remaining proteins, 28 had experimental characterizations found by the manual literature search. SIFTER achieved 96% accuracy (27 of 28) for predicting a correct function against this gold standard dataset. SIFTER performed better than BLAST, GeneQuiz, GOtcha, GOtcha-exp (GOtcha transferring only experimental GO annotations), and Orthostrapper (75%, 64%, 89%, 79%, and 11% accuracy, respectively). The comparative results are summarized in Figure 1A. The complete data for these analyses are available in Dataset S1.
We present a statistical graphical model to infer specific molecular function for unannotated protein sequences using homology. Based on phylogenomic principles, SIFTER (Statistical Inference of Function Through Evolutionary Relationships) accurately predicts molecular function for members of a protein family given a reconciled phylogeny and available function annotations, even when the data are sparse or noisy. Our method produced specific and consistent molecular function predictions across 100 Pfam families in comparison to the Gene Ontology annotation database, BLAST, GOtcha, and Orthostrapper. We performed a more detailed exploration of functional predictions on the adenosine-5′-monophosphate/adenosine deaminase family and the lactate/malate dehydrogenase family, in the former case comparing the predictions against a gold standard set of published functional characterizations. Given function annotations for 3% of the proteins in the deaminase family, SIFTER achieves 96% accuracy in predicting molecular function for experimentally characterized proteins as reported in the literature. The accuracy of SIFTER on this dataset is a significant improvement over other currently available methods such as BLAST (75%), GeneQuiz (64%), GOtcha (89%), and Orthostrapper (11%). We also experimentally characterized the adenosine deaminase from Plasmodium falciparum, confirming SIFTER's prediction. The results illustrate the predictive power of exploiting a statistical model of function evolution in phylogenomic problems. A software implementation of SIFTER is available from the authors.

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It's a conclusion drawn from evidence.29+ Evidences for Macroevolution: The Scientific Case for Common Descent

How do you think life came about?

A deity creating life from dirt is the very definition of superstition.

Then how did life come about, according to you?

That is what your entire argument is about, semantics.

That would be superstition.

For modern scientists it is a conclusion, not an assumption. We have the evidence demonstrating common ancestry.

Please explain why a Common Designer would produce a nested hierarchy (i.e. a phylogeny).

You are the only one who believes that life was magically poofed into being. When did magical poofing become a reasonable explanation?

Once again, all you have is name calling. You can't counter the scientists, so you call the scientists names.Here's a bit of advice. Grow up.