I
am reposting this article because this is the new era of AI - that will
replace doctors, hospitals, health care providers. The technocratic
future vision is that AI can diagnose, treat, investigate every disease
of humanity. AI will alter us without out knowing by monitoring through
nanosensors every aspect of cellular function and by augmenting it
automatically - modifying the organism without our knowledge. While you
have so many people who are still clueless about Transhumanism,
Technocracy, the Singularity, self assembly nanotechnology, the
militaries plan to use healthcare to have the public accept Cyborgs and
augmented humans, they deny clearly visible nano and microrobots
swimming in human blood - while this irreversible step in human
evolution is happening now, science is progressing at lightening speed.
Scientific advancement is not waiting for those who are still in deep
sleep.
We
are well on our way to reach the transhumanst technocratic goal, in
fact, some experts understand that we are in the process of moving into
the post human era. As people celebrate the explosive rise of AI in
healthcare, I offer just a few of my many previous articles as as a
warning and call for caution:
Artificial
Intelligence Hacking Of Human Brains - Nanotechnology, Artificial
Intelligence Merging With Quantum Computing And The Warnings Of
Extinction
Review Article: Artificial Intelligence Enhanced Biomedical Micro/Nanorobots In Microfluidics
Neurohacking and Artificial Intelligence - How Easy Is It For AI To Hack Humans Without Their Knowledge? It Is Happening Now
Synthetic Biology and Artificial Intelligence - What Do We Know And What Are The Spiritual Implications?
SUPER
INTELLIGENCE - Analysis of Nick Bostoms Book - Director Of "Future of
Humanity" Institute and Strategic Artificial Intelligence Research
Centre
Artificial
Intelligence Designed Xenobots 3.0: The World's Self Replicating Living
Robots And The Next Phase: Self Assembly HUMAN ANTHROBOTS
Should
We Be Concerned? "Dual Use Of Artificial-Intelligence-Powered Drug
Discovery" - Shocking AI Ability To Create Bioweapons Inventory Proven -
40.000 Lethal Molecules Discovered In 6 Hours
The
Rise Of The BioCyborg: Synthetic Biology, Artificial Chimerism And
Human Enhancement The Rise Of The BioCyborg: Synthetic Biology,
Artificial Chimerism And Human Enhancement
AI
can now create a replica of your personality: A two-hour interview is
enough to accurately capture your values and preferences, according to
new research from Stanford and Google DeepMind.
The Technocratic Agenda Of Digitizing All Genetic Sequences On Earth For Profit And The AI Existential Threat
Please
read for your self and ask yourself the question: If AI can predict the
inner workings of cells, can it also alter the inner workings of cells
but the use of nanotechnology? The answer in science is an inequivocal
yes, and if you do not know that, start reading the bio nanotechnology
literature.
Computational biologists develop AI that predicts inner workings of cells
Using
a new artificial intelligence method, researchers at Columbia
University Vagelos College of Physicians and Surgeons can accurately
predict the activity of genes within any human cell, essentially
revealing the cell's inner mechanisms. The system, described in Nature, could transform the way scientists work to understand everything from cancer to genetic diseases.
"Predictive
generalizable computational models allow to uncover biological
processes in a fast and accurate way. These methods can effectively
conduct large-scale computational experiments, boosting and guiding
traditional experimental approaches," says Raul Rabadan, professor of
systems biology and senior author of the new paper.
Traditional
research methods in biology are good at revealing how cells perform
their jobs or react to disturbances. But they cannot make predictions
about how cells work or how cells will react to change, like a
cancer-causing mutation.
"Having the ability to accurately predict a cell's activities would transform our understanding of fundamental biological processes,"
Rabadan says. "It would turn biology from a science that describes
seemingly random processes into one that can predict the underlying
systems that govern cell behavior."
In
recent years, the accumulation of massive amounts of data from cells
and more powerful AI models are starting to transform biology into a
more predictive science. The 2024 Nobel Prize in
Chemistry was awarded to researchers for their groundbreaking work in
using AI to predict protein structures. But the use of AI methods to predict the activities of genes and proteins inside cells has proven more difficult.
In
the new study, Rabadan and his colleagues tried to use AI to predict
which genes are active within specific cells. Such information about
gene expression can tell researchers the identity of the cell and how
the cell performs its functions.
"Previous models have been trained on data in particular cell types, usually cancer cell lines
or something else that has little resemblance to normal cells," Rabadan
says. Xi Fu, a graduate student in Rabadan's lab, decided to take a
different approach, training a machine learning model on gene expression
data from millions of cells obtained from normal human tissues. The
inputs consisted of genome sequences and data showing which parts of the
genome are accessible and expressed.
The
overall approach resembles the way ChatGPT and other popular
"foundation" models work. These systems use a set of training data to
identify underlying rules, the grammar of language, and then apply those
inferred rules to new situations.
"Here
it's exactly the same thing: we learn the grammar in many different
cellular states, and then we go into a particular condition—it can be a
diseased [cell type] or it can be a normal cell type—and we can try to
see how well we predict patterns from this information," says Rabadan.
Fu
and Rabadan soon enlisted a team of collaborators, including co-first
authors Alejandro Buendia, now a Stanford Ph.D. student formerly in the
Rabadan lab, and Shentong Mo of Carnegie Mellon, to train and test the
new model.
After training on data from more than 1.3 million human cells, the system became accurate enough to predict gene expression in cell types it had never seen, yielding results that agreed closely with experimental data.
Next,
the investigators showed the power of their AI system when they asked
it to uncover still-hidden biology of diseased cells, in this case, an
inherited form of pediatric leukemia.
"These
kids inherit a gene that is mutated, and it was unclear exactly what it
is these mutations are doing," says Rabadan, who also co-directs the
cancer genomics and epigenomics research program at Columbia's Herbert
Irving Comprehensive Cancer Center.
With
AI, the researchers predicted that the mutations disrupt the
interaction between two different transcription factors that determine
the fate of leukemic cells. Laboratory experiments confirmed AI's
prediction. Understanding the effect of these mutations uncovers
specific mechanisms that drive this disease.
The new computational methods should also allow researchers to start exploring the role of genome's "dark matter"—a
term borrowed from cosmology that refers to the vast majority of the
genome, which does not encode known genes—in cancer and other diseases.
"The
vast majority of mutations found in cancer patients are in so-called
dark regions of the genome. These mutations do not affect the function
of a protein and have remained mostly unexplored," says Rabadan. "The
idea is that using these models, we can look at mutations and illuminate
that part of the genome."
Rabadan
is working with researchers at Columbia and other universities,
exploring different cancers, from brain to blood cancers, learning the
grammar of regulation in normal cells, and how cells change in the
process of cancer development.
The work also opens new avenues for understanding many diseases beyond cancer
and potentially identifying targets for new treatments. By presenting
novel mutations to the computer model, researchers can now gain deep
insights and predictions about exactly how those mutations affect a
cell.
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