Is proper feature selection possible before all imperatively hidden objects required for conceptualizing aging adequately are fully discovered?

Is proper feature selection for machine learning training data even possible before all imperatively hidden objects, factors, dimensions and parameters required for conceptualizing aging adequately are fully discovered?

About imperatively hidden objects and the need for new concept discoveries to select all necessary features required to fully understanding aging, immigration and other phenomena.

Humans are very bias in choosing their method of conducting experimental measurements or make observations without being aware of it. What percentage of the entire electromagnetic wave spectrum can we perceive? No more than 5% for sure. But the changes, of which we must be aware, before we can understand aging, are most likely much more distinct outside our narrow sensory window because our sensory limitations did not affect the evolution of aging in any way.

For example, humans can only hear part of the sound an elephant makes because humans cannot hear such low frequencies as the elephant can. This tends to prevent the full understanding of the elephant’s communication options. Humans cannot distinguish such low sound frequencies from background noise, i.e. environment, because they cannot perceive the low elephant sound frequencies from being different from the background environment. But without considering those imperatively hidden factors we cannot fully understand elephant communication. Therefore, humans tend to miss cellular processes, which can only be distinguished from background noise outside the electromagnetic wavelength interval, for which humans have evolved sensory organs, i.e. eyes, ears and skin. The mechanism by which the tongue and nose operate is of an entirely different dimension because they cannot sense any wavelength.

For example, before magnets were discovered, they remained for us an imperatively hidden object because we could not even suspect them in any way. But still, just because we lack any senses for perceiving any kind of magnetism does not stop it from affecting our lives. Only after we discovered the consequences of the forces, which the magnetic field has on some metals, prompted us to search outside the limited window, within which we can sense differences in waive length. Magnetic fields could affect life in many positive ways because they are used to treat major depressive disorder and cause involuntary muscle contraction. But has anybody even thought of measuring the magnetic field of a cell or brain, which I expect to be strong enough for us to measure with sensitive devices? Since any electric current causes a perpendicular radiating magnetic field, it can be hypothesized that the weak magnetic field is pulse-like and depends on the temporal pattern by which neurons fire action potentials. The changes in the magnetic field of a cell is expected to be enriched for the cellular component membrane because they have proton pumps and maintain an electric gradient to produce ATP. But what if changes in this magnetic field are causing us to age? Then we could stop the aging process by any intervention, which sets our cellular magnetic field pattern back to its youthful benchmark.

I suspect that the reason for our only rudimentary understanding of the aging process is caused by us missing such kind of imperatively hidden objects, which are required for making the essential key observations without which aging cannot be fully explained. I view a magnetic field as a concept, which exists regardless weather we are aware of it. There may be many more other hidden concepts, which we must develop, before we can reverse aging.

I use an analogy to explain this concept, which came to me in a dream, and which does not appear to concern anybody, except for me.

Let’s say that an immortal interstellar alien highly intelligent out-of-space critter has landed on Earth. Lets imagine that he can only perceive waive lengths within the limits of the magnetic field. Then we humans would not even notice this out of space interstellar visitor because he/she remains an imperatively hidden object that we cannot even suspect. Let’s say this interstellar species has not evolved a body or anything to which our senses are sensitive. Let’s say that this life can be fully defined by irregularities within the magnetic field. But this interstellar critter can perceive us humans because our magnetic field disrupt the homogeneity of the background environment and must therefore be something other than background noise. Let’s say that this immortal interstellar critter can perceive and process all the magnetic fields on Earth. Could he maybe develop the concept of siblings or parents on its own? Is the magnetic field of relatives more similar to each other than expected by chance? It is very likely because humans vary a lot in their neuronal wiring architecture. Hence, each human could be defined by the pattern of his/her action potentials. This inevitably causes a very weak unique perpendicularly acting electromagnetic field that cannot be detected by our instruments. Therefore, instead of humans, we should use the giant squid as model organism to understand the relationships between life, aging and changes in magnetic field because it has the thickest neuron. Therefore, it must fire stronger action potentials than our human neurons. This will inevitably cause a stronger perpendicularly acting electromagnetic field, which may be strong enough to be detected by our instruments.

Lets say that this interstellar critter wants to use machine learning to predict the risk of any particular university student in the USA for having to return home after graduation because they lost their immigration status and could not find a job, which would have made them eligible for one year OPT (Optional Practical Training). Lets say that this interstellar critter has no concept of aging and that his most important goal is to develop a classifier by developing a new machine learning algorithm, which can predict in advance the risk that any particular student is facing to no longer been allowed to reside in the United States. Let’s say that accomplishing this objective has the same meaning and importance to this critter as for us the cure from aging.

What should he do? He cannot talk. No human even suspects him. He could start using supervised machine learning by observing thousands of students to find out what those students share, who are forced to leave, or what they lack compared to citizens, who are always welcome here.

I hypothesize that no matter how clever and sensitive to irregular interruption of the homogenous electromagnetic field, which is the only dimension in which he can sense the presence of humans and any other form of life, he has no chance to understand the risk factors for being forced to leave because they are an imperatively hidden concept to the critter, which he cannot even suspect in any way. But without developing the right concepts in advance, this critter can never the discover risk factors for having to leave the USA after graduation.

The same applies to aging. We are still missing essential concepts without which we cannot fully understand it. But even if somebody by chance could detect the magnetic irregularities caused by this foreign interstellar critter, he/she could never suspect that it is highly intelligent.

This means that even if we measured a cell across the entire wavelength spectrum and could clearly detect its presence we would never suspect it to have any kind of intelligence because we would consider the anomalies in the magnetic field as background noise. Our visiting interstellar critter has a similar problem. He cannot develop the essential concepts without which he could never develop a machine learning algorithm to predict all the correct risk factors, which impair the chances for somebody to be allowed to keep residing in the US while not full time enrolled. As long as this critter has no concept of “country”, e.g. the USA, he has absolutely no chance to discover nationalities because even if he could figure out the nationality of everyone, it would make no sense to him. But words like “American” “German”, “French” or “Indian” cannot make any sense to this critter as long as the concept of “country” remains an imperatively hidden object for him. How can somebody be considered “German” or “American” as long as the concept of Germany or USA are still lacking? One can only be German if Germany exists. Without at least suspecting the concept of a country, e.g. Germany, there is absolutely no way to discover the required concept of citizenship. But without determining the feature “citizenship” no machine learning algorithm could learn to make correct predictions. .The same applies to aging. We are still lacking so many essential concepts without which aging can never be understood

For example, as long as the concept of a ribosome is lacking we have no way of understanding the changes in the relative abundance ratio of mRNA and proteins. We may have some initially success with building a model to predict protein abundance and concentration because it is about 70% similar of the transcriptome. But no matter how many training samples we use to train our predictor, it must fail, unless we have developed a mental concept of a ribosome. I believe we face a similar predicament with understanding the causes and regulation of epigenetic changes over time with advancing age, despite being able to measuring them so clearly that we can use them to determine the biological age. But unfortunately, as long as we lack any concept, by which epigenetic changes could be connected to other cellular processes, we cannot understand how epigenetic changes are regulated.

Before we could correctly conceptualize the role and scope of the ribosome we had no way to explain the mechanism by which mRNA and protein abundance are linked. But even after we conceptualized the role of the ribosome correctly any machine learning algorithm to predict protein concentration would inevitably fail as long as we lack the correct concept of the poly-AAA-tail. Similarly, there are still lots of imperatively hidden concepts, factors, dimensions or objects, which we cannot suspect because we cannot perceive them, which prevent us from fully understanding aging. However, the fact that our current observations fail to fully explain aging, indicate the presence of imperatively hidden factors of which we can see the consequences without being able to detect them. But since every consequence must have a cause, any unexplained consequence indicates the presence of imperatively hidden inperceptable factors without which we cannot succeed to improve our feature selection.

As I tried to explain in my immigration example, only when selecting the correct feature, e.g. citizenship, the risk for being asked to leave by the federal government can be predicted. Could I convince anybody of the high likelihood of the presence of imperatively hidden factors, which we cannot perceive yet as being distinctly different from their environment?

The current bottleneck to defeat aging is not to focus on improving our machine learning algorithms and increase the training samples, but instead, we must focus on proper feature selection. My main contribution towards defeating aging is to predict features, measurement types and intervals between measurements, which could show the actions of aging much clearer than the features, we have currently selected, to stop aging and defeat death. That is why I keep dreaming about all my crazy hypotheses, which come to me while sleeping, showering, eating or biking. While still working on my master’s thesis I had similar dreams but I pushed them away and ignored them because I had no data to support the concepts I kept dreaming about. Then I found an article last spring break supporting one of my very intensively hypothesis according to which there is a life cycle, which splits homogenous long intervals by inhomogeneous short intervals similar to the cell cycle implying that cyclical (i.e. cell cycle) and linear biological clocks (i.e. lifespan) must have evolved from a single mother clock in the very distant past and diverged a lot ever since. I came up with all my crazy aging hypothesis, which could theoretically be true, in my dreams of being an aged dying yeast cell, which is desperately trying to test any intervention to save its life. Now it is up to wet-lab scientists to test my hypotheses. But even if all of them can be ruled out, the possibilities, by which the mechanism of aging could function, would be reduced. This would leave us with fewer hypotheses left to test. Since the options we have for fully understanding the aging process are large but yet finite, any crazy hypothesis, which can be ruled out, brings us a tiny step closer to immortality.

The reason why I claim that correct feature selection, but not the machine learning algorithms, are the current bottleneck to progress in our understanding of the aging process is that our machine learning algorithms have improve over time but our feature selection has not.

I am afraid my writings are too childish for being taken seriously by experts but I find them important nevertheless. Almost everyone can learn the currently accepted methods and steps to analyze datasets, but I don’t know anybody, who can imagine being in the shoes of a yeast or a new still naïve machine learning algorithm. While sleeping I am trying to envision our world from their very different perspective. Scientists only tend to dig deeper if there is an indication by some data or discoveries that certain new features selection properties could improve our predictor. But it may take years until we have the data, which point in the same direction and indicate the same features as I have dreamed while still a student.

My eyesight is not good enough to complete all the reading assignments. That is why I often tend to make a story, what results I would suspect by imagining how I’d respond, if I were a ribosome, a vacuolar membrane, a transcription factor, a promoter, a proton pump, an ATPase, a kinase, a gene or protein, to come up with the right keywords. Surprisingly, I am right about 75% of the time, which is higher than expected by chance. Since this method has partially worked in the past, it can be expected to also partially work in the future.

The fact that I cannot find any data for measuring the transcriptome in five-minute intervals for more than 3 out of the average 25 replications, which is considered the average WT yeast replicative lifespan, indicates that nobody has seriously suspected that we could at least observe the effects of the aging mechanism by selecting new periodic features, such as period length or amplitude, which only make sense if we replace our linear with a periodic concept of life. However, this requires us to change our concepts about life to be driven by linearly acting trends to cyclical periodically acting trends in order to expand our feature selection options to periodic quantities, such as period length, amplitude or oscillation pattern, which would have been impossible to imagine when holding on to the old linear concept. In this case – although we could clearly measure the period length - we could not detect it as a feature affected by aging until we explicitly define, select and measure this new feature, e.g. the period length.

Please let me know if this writing makes sense to you because so for nobody, except for me, seems to worry about this problem.