Artificial intelligence involves multiple fields, including deep learning, neural networks, Bayesian networks, and evolutionary algorithms. Here's how the current artificial intelligence is applied in life science and metabolic disease research.

1. AI Application In Life Sciences
Artificial intelligence involves multiple fields, including deep learning, neural
networks, Bayesian networks, and evolutionary algorithms. Here's how the current
artificial intelligence is applied in life science and metabolic disease research.
Artificial Intelligence In Drug Development
To develop new therapies, pharmaceutical companies have adopted artificial
intelligence in three main areas:
 Finding new drug targets
 Drug development
 Improve clinical trials
Finding New Drug Targets
Artificial intelligence can help identify new drug targets in a variety of ways. RNA
sequencing is used to analyze gene expression. Then use the gene expression data to
measure the effect of treatment, dig new pathways, or identify new biomarkers of
disease.
There are two main types of DNA sequencing applications: germline (genetic) and
somatic mutation analysis.
Germline mutation data can be used in assessing a patient's genetic risk for a
particular disease or to diagnose genetic diseases.
Somatic mutations (which builds up in patients as they age) can be used in a variety of
ways, including:
 Remission / Relapse Tracking
 Cancer diagnostic procedures
 Clinical Trials as a non-lethal endpoint
 Detection of resistance in a treatment
 Guidance for clinicians to pick the best treatment for a specific patient

2. Drug Development
Artificial intelligence has played a major role in drug development. Most large
pharmaceutical companies already have AI projects or collaborations. They have been
using AIs to promote immuno-oncology drug searches and to analyze and test
research hypotheses faster.
AI is also used in strategic research to find new treatments for metabolic diseases and
advancing cancer drug development through causal inference machine learning.
Currently, several artificial intelligence companies also provide platforms for drug
development, design, and reuse.
Improvements In Clinical Trials
Artificial intelligence improves clinical trials through targeted recruitment, which
makes it easier for index patients to find the right trial. If artificial intelligence can
extract medical record data, the condition of a particular patient can be compared
with all the criteria applicable to open trials.
Artificial intelligence also plays a big role in adhering to medications and treatments,
which is a major issue in clinical trials. Mobile technology and mobile apps can remind
patients when to take their medications, but they also invest in "absorbable sensor"
that track medication intake and wirelessly record the number of vials, allowing
pharmaceutical companies to report patient-reported data more accurately.
Artificial intelligence also helps to apply drug prediction algorithms in clinical trials.
Application Of Artificial Intelligence In Pathology
The application of artificial intelligence in pathology makes data analysis more
objective. For example, immunofluorescent/chemically stained cells and tissues are
more objective than the traditional subjective process analysis. Some artificial
intelligence companies focus on improving the work of doctors around this kind of
image interpretation and prognosis prediction.

3. Diagnostic Imaging
The diagnostic imaging market is one of the most advanced functions of artificial
intelligence. Emerging applications are extending the benefits of diagnostic image
analysis to other image analysis tasks, such as identifying cancer cells in stained
pathological sections.
Immunostaining includes techniques widely used in histology, cell biology, and
molecular biology, all of which use antibody-based staining. Using artificial
intelligence to explain chemical staining (such as H & E) can be used to mine historical
pathology data, automate repetitive tasks, or add other data to clinical trials.
Data Integration With Artificial Intelligence
The integration of data supported by artificial intelligence is the latest method in
the life sciences industry. History has proven that data integration is challenging
because of the large data sets, often incompatible formats, and the data set
continues to grow.
Traditional methods include standardizing the data format and then manually writing
scripts to query the data to generate useful data sets. In contrast, artificial
intelligence uses machine learning and natural language processing to fully integrate
datasets and mine datasets to gain valuable insights. This approach supports scalable
integration and analysis.