posted
On a youtube channel. Ish, XY, Winters, alTakruri, lioness, Mike, Nodnarb, Amun-Ra The Ultimate, rasol, Djehuti, IronLion, Mindovermatter, Zarahan, Mena... Come on down
beyoku Member # 14524
posted
I am not a regular poster but can you Provide more info on what you are looking to accomplish?. Maybe someone else will bite.
Mindovermatter Member # 22317
posted
I can't do a voice over interview but I could do an interview about certain topics online by writing.....
beyoku Member # 14524
posted
Details details details?
Fourty2Tribes Member # 21799
posted
Thanks for the response. I forgot about you beyoku but I definitely have some questions for you. I'll PM both of you this weekend.
I'm just a spectator here and haven't really posted, so I'm hoping the link works!
xyyman Member # 13597
posted
Nice work....so far....I have to finish look at it when have time.
Elmaestro Member # 22566
posted
Nice interview, ...but the vitamin D hypothesis was a stab to my heart & Millions of genes Beyoku, Millions?
the lioness, Member # 17353
posted
quote:Originally posted by NabtaPlayaPlaya: The interview with Beyoku can be found here... https://youtu.be/Clq7TTuynvE
I'm just a spectator here and haven't really posted, so I'm hoping the link works!
Mentioned nearer to the end of the video:
wikipedia
Olu Dara Jones (born Charles Jones III; January 12, 1941) is an American cornetist, guitarist and singer. He is the father of rapper Nas. Olu Dara was born Charles Jones in Natchez, Mississippi, United States, the son of Ella Mae and Charles (Rufus) Jones. Nas grandfather, Charles Jones II, was a singer touring with a quartet when Olu Dara was born, and everyone in his family was raised playing instruments.
______________________________
Interestingly they have a lot of background on Nas' maternal ancestry but little on his paternal line.
quote:
Haplogroup I1-M253 (M253, M307, P30, P40) displays a very clear frequency gradient, with a peak frequency of approximately 35% among the populations of southern Norway, southwestern Sweden, and Denmark, and rapidly decreasing frequencies toward the edges of the historically Germanic-influenced world. A notable exception is Finland, where frequency in West Finns is up to 40%, and in certain provinces like Satakunta more than 50%.
Ish Gebor Member # 18264
posted
This was interesting, and I did learn quite a few things.
What most stuck out was the algorithmic behavior in the software. Since the developer can be biased in how booleans need to behave, as directed by the developer.
The irony is also, that different methods in algorithms can lead to the same conclusion. In other words, different programmer can make software to have similar behavior, yet using different methods in algorithms. But eventually it is the bias method (math) by the developer which makes the decisions.
xyyman Member # 13597
posted
FYI. 8 STRs were released NOT two. And yes, normally 13 is needed to narrow down geographic locale. But 2-3 is enough to give preliminary assignment
Ish Gebor Member # 18264
posted
To elaborate on the topic addressed earlier on.
For those interested:
PROGRAMMING AND PREJUDICE UTAH COMPUTER SCIENTISTS DISCOVER HOW TO FIND BIAS IN ALGORITHMS
This software that helps predict criminal behavior is under fire for having a 'racist' algorithm
It seems likely that this is a result of the software taking into account factors like wealth and social marginalization that correlate heavily with race. It's not the first example we've seen lately of an calculation not designed explicitly for racist purposes producing racist results, though it is the one we've seen with the highest stakes.
quote: Gene name errors are widespread in the scientific literature
The spreadsheet software Microsoft Excel, when used with default settings, is known to convert gene names to dates and floating-point numbers. A programmatic scan of leading genomics journals reveals that approximately one-fifth of papers with supplementary Excel gene lists contain erroneous gene name conversions.
Table 1 Results of the systematic screen of supplementary Excel files for gene name conversion errors
—Ziemann et al. Genome Biology (2016) 17:177 DOI 10.1186/s13059-016-1044-7
quote: Validating TrueAllele® DNA Mixture Interpretation
Abstract: DNA mixtures with two or more contributors are a prevalent form of biological evidence. Mixture interpretation is complicated by the possibility of different genotype combinations that can explain the short tandem repeat (STR) data. Current human review simplifies this interpretation by applying thresholds to qualitatively treat STR data peaks as all-or-none events and assigning allele pairs equal likelihood. Computer review, however, can work instead with all the quantitative data to preserve more identification information. The present study examined the extent to which quantitative computer interpretation could elicit more identification information than human review from the same adjudicated two-person mixture data. The base 10 logarithm of a DNA match statistic is a standard information measure that permits such a comparison. On eight mixtures having two unknown contributors, we found that quantitative computer interpretation gave an average information increase of 6.24 log units (min = 2.32, max = 10.49) over qualitative human review. On eight other mixtures with a known victim reference and one unknown contributor, quantitative interpretation averaged a 4.67 log factor increase (min = 1.00, max = 11.31) over qualitative review. This study provides a general treatment of DNA interpretation methods (including mixtures) that encompasses both quantitative and qualitative review. Validation methods are introduced that can assess the efficacy and reproducibility of any DNA interpretation method. An in-depth case example highlights 10 reasons (at 10 different loci) why quantitative probability modeling preserves more identification information than qualitative threshold methods. The results validate TrueAllele® DNA mixture interpretation and establish a significant information improvement over human review.
quote: The problem addressed in this paper is to define a learning algorithm for the prediction of splice site locations in human DNA in the presence of sequence annotation errors in the training data. To this aim we generalize a previous machine learning algorithm. Experimental results on a common dataset including errors show the algorithm outperforms its previous version, in particular in the complexity of the produced hypothesis.
posted
This is not about genetics, obviously. But it shows how software can be manipulated in behavior.
This is probably the most well known bias software corruption:
quote: Volkswagen under investigation over illegal software that masks emissions
The US government has ordered Volkswagen to recall almost 500,000 cars after discovering that the company deployed sophisticated software to cheat emission tests allowing its cars to produce up to 40 times more pollution than allowed.
posted 
