Press Releases
Data Analysis at AI & Medicine: PrediXcan Based Analysis and BSLMM Prediction
Dec 16, 2020, 14:09 PM
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On November 19, 2020, AI & Medicine, an expert of AI-assisted medicine, announces that its artificial intelligence-powered systems are capable of analyzing data from hundreds of sources and accordingly offer predictions about what works and what doesn't. This would greatly help scientists and researchers more efficiently and wisely make choices in drug discovery and development attempts. Ever since its founding, AI & Medicine has been known for its expertise in applying AI into drug discovery, personalized healthcare and various other medical applications.
As a very critical step in drug discovery, data analysis used to be time-consuming, labor-intensive. But now with the help pf AI systems, it has been streamlined and can be accomplished within a much shorter time frame. AI systems are able to process various data types, either structured data or unstructured data, in a fast manner and return with reliable results or predictions.
“Nowadays, big data analysis using AI systems definitely brings much more hope than hype to drug R&D. More importantly, this attempt is feasible as what is needed in data analysis, such as the public cloud to store petabytes of data and the servers to accomplish big data projects, can be obtained at a reasonable price,” commented the representative speaker from AI & Medicine.
Below are some data analysis methods that AI & Medicine is specialized in:
PrediXcan Based Analysis
PrediXcan integrates transcriptome data and GWAS data into a single computing framework. It is developed to detect the relationship between genes and traits and to determine the correlation between gene expression levels and disease states or target characteristics.
BSLMM Prediction
Bayesian Sparse Linear Mixed Model (BSLMM) is a mixed model of linear mixed model (LMM) and sparse regression model. The BSLMM phenotype prediction model method can improve the accuracy of genome-wide genetic locus information. It can be used in human beings to promote the development of personalized medicines, and also used in animals and plants to help select ideal breeding individuals so as to improve the effectiveness of breeding programs.
For information related data analysis or other AI-assisted capabilities at AI & Medicine, please visit https://aimed.protheragen.com/data-analysis.html.
About AI & Medicine
Fully aware of the difficulties and challenges faced by the research and healthcare industry in drug discovery, AI & Medicine has newly developed an AI-powered drug discovery platform to accelerate the R & D attempt process. Meanwhile, AI & Medicine’s medical and scientific solution capabilities are widely ranged, covering ADMET prediction, drug repositioning, clinical trials, patient recruitment, pharmacovigilance system, AI diagnosis and many more.
As a very critical step in drug discovery, data analysis used to be time-consuming, labor-intensive. But now with the help pf AI systems, it has been streamlined and can be accomplished within a much shorter time frame. AI systems are able to process various data types, either structured data or unstructured data, in a fast manner and return with reliable results or predictions.
“Nowadays, big data analysis using AI systems definitely brings much more hope than hype to drug R&D. More importantly, this attempt is feasible as what is needed in data analysis, such as the public cloud to store petabytes of data and the servers to accomplish big data projects, can be obtained at a reasonable price,” commented the representative speaker from AI & Medicine.
Below are some data analysis methods that AI & Medicine is specialized in:
PrediXcan Based Analysis
PrediXcan integrates transcriptome data and GWAS data into a single computing framework. It is developed to detect the relationship between genes and traits and to determine the correlation between gene expression levels and disease states or target characteristics.
BSLMM Prediction
Bayesian Sparse Linear Mixed Model (BSLMM) is a mixed model of linear mixed model (LMM) and sparse regression model. The BSLMM phenotype prediction model method can improve the accuracy of genome-wide genetic locus information. It can be used in human beings to promote the development of personalized medicines, and also used in animals and plants to help select ideal breeding individuals so as to improve the effectiveness of breeding programs.
For information related data analysis or other AI-assisted capabilities at AI & Medicine, please visit https://aimed.protheragen.com/data-analysis.html.
About AI & Medicine
Fully aware of the difficulties and challenges faced by the research and healthcare industry in drug discovery, AI & Medicine has newly developed an AI-powered drug discovery platform to accelerate the R & D attempt process. Meanwhile, AI & Medicine’s medical and scientific solution capabilities are widely ranged, covering ADMET prediction, drug repositioning, clinical trials, patient recruitment, pharmacovigilance system, AI diagnosis and many more.
