Would this method for uncovering hidden objects, feature discovery, feature selection and supervised learning work? According to my understanding perfect prediction by computation optimization must be impossible as long as essential input...
moreWould this method for uncovering hidden objects, feature discovery, feature selection and supervised learning work? According to my understanding perfect prediction by computation optimization must be impossible as long as essential input training data features for supervised learning are still lacking. Therefore, I emphasize not to invest much effort into algorithmic computational optimization before the feature discovery process allows selecting all needed features through variation before realistic predictions can be achieved by enhancing computations and parameters. For me this simple progression from completed feature selection to optimized algorithm is essential for more rapid discoveries. That is why I am still wondering why I have seen so many papers focusing on improving computational predictions without any kind of prior considerations about having properly completed the absolutely essential exhaustive feature selection process without which no subsequent computations can lead to satisfactorily predictions, which are reasonably consistent with our experimental observations and measurements. I am afraid that I am still the only one, to whom the writing above can make sense. I had expected much more enthusiasm, excitement and optimism about very likely accelerating our discovery rate by first focusing on uncovering still hidden objects and features through diverse variations of conditions, procedures, methods, techniques and measurements, followed by the exhaustive selection for all relevant needed features, followed by designing, developing, combining and optimizing the computational steps of our machine learning algorithm until our predictions match our experimentally obtained observations. Once this major machine learning objective has been achieved we have reached its final status beyond which we cannot improve it unless we can generate conditions, which cause our previously perfectly predicting machine learning algorithm to obviously fails because it is an absolute prerequisite for discovering more relevant training reflected by more dimensions. According to my current understanding any newly discovered essential input data feature inevitably causes a rise in the dimensionality of input data components, which must be considered together but never in isolation. For example, if I train with 100 input features our input variable must consist of exactly 100 components or parts, which together form a new level of single measuring points, which tends to be much different in controlling its manipulations and their overall effects from anything, which could possibly get anticipated, when trying to add up the effects of its 100 parts to a new total. This new total tends to consist of much different dimensions and often refers to completely unrelated kind of data than when combining all 100 components consisting of exactly the same input values for every of its 100 variables but by considering all 100 components like a single indivisible unit of measurement points, which often results in completely different kinds of unrelated seeming properties/features, which are not even closely reflecting the results, which we'd obtain if we executed each dimension on its own in isolation and sequential order. For example, stopping translation prematurely by three consecutive tryptophans has a much different impact, i.e. stop, then when translating each of the tryptophan in isolation separated by other amino acids, since this causes the nascent polypeptide chain to grow. Each tRNA charged with tryptophan, which complements mRNA triplets, causes the peptide to grow by a single tryptophan, which gets added to it. So when you try 2 tryptophans the polypeptide grows by two amino acids. But when you try 3 consecutive tryptophans, then-counter-intuitively-instead of the expected growth by 3 amino acids-translation prematurely stops. Stopping translation prematurely is of a much different dimension, level, effect and data kind, then when keep adding more amino acids to the growing peptide chain. If we consider the effect of complementary binding of a tRNA to its mRNA codon our peptide grows by one amino acid any charged tRNA adds another amino acid of one of 20 categorical values. Normally no amino acid can cause the translation to step prematurely, not even two amino acids as a pair. But three amino acids as an indivisible triplet, which must be considered as a new single value requiring all three tryptophans to be sequentially to be present, like a single indivisible data block, which must not be divided only in triplets, because only triplets, but no pair or singlet, can stop translation prematurely. Another example is predicting overall cellular protein composition. It depends on how many mRNA strands coding for a particular protein are in the cytoplasm. There is proportionality between number of cytoplasmic mRNA strands and total protein. Therefore, if the cell needs to double protein abundance it could double transcription and keeps everything else the same. But a much better and less step intensive, more economic way of doubling protein concentration is to double the length of the poly-(A)-tail. Extending the length of the poly-(A)-mRNA-tail may require about 100 additional adenines whereas doubling transcription requires about at least 500-instead of only 100-new nucleotides in addition to all needed transcriptional modification steps with their elaborate synthesis machinery. If the dividing yeast must raise its lipid synthesis by more than 10-fold during the short M-phase, it could increase transcription by a factor of 10, it could make the poly-(A)-mRNA-tail 10 times longer, or it could synthesized 10 times more new ribosomes to increase the ribosomal translation by a factor of 10 simply by reducing the distance of free uncovered mRNA nucleotides between adjacent ribosomes translating running down the same mRNA strand. If more than one ribosome is translating the same mRNA strand simultaneously, it is called a poly-ribosome. Hence, having 10 times more ribosomes binding to the same mRNA strand at the same time increases translation by a factor of 10 without needing any additional transcription. Above I have given three easy examples to get 10 times more proteins. Although all 3 methods have the same final result, i.e. 10 times more proteins, their mode of action, their required essential features, their dimensions and their minimally required parts, which must be considered like a single value, are totally different.
