DLST: A Drug Target / Disease Biomarker (G1743)

Target Name: DLST

NCBI ID: G1743

DLST

Other Name(s): dihydrolipoamide S-succinyltransferase | dihydrolipoamide S-succinyltransferase (E2 component of 2-oxo-glutarate complex) | Dihydrolipoamide succinyltransferase component of 2-oxoglutarate dehydrogenase complex | KGD2 | PGL7 | Dihydrolipoyllysine-residue succinyltransferase component of 2-oxoglutarate dehydrogenase complex, mitochondrial | ODO2_HUMAN | OGDC-E2 | Dihydrolipoamide S-succinyltransferase (E2 component of 2-oxo-glutarate complex) | 2-oxoglutarate dehydrogenase complex component E2 | DLTS | E2K | Dihydrolipoyllysine-residue succinyltransferase component of 2-oxoglutarate dehydrogenase complex, m

DLST: A Drug Target / Disease Biomarker

Deep Learning of Sparse Transforms (DLST) is a technique that has been developed to solve the problem of sparse data. This technique is widely used in the field of machine learning and has many applications. In this article, we will discuss the concept of DLST and its potential as a drug target or biomarker.

DLST is a technique that uses deep neural networks to learn the sparse representations of input data. It is based on the idea of using a sparse linear model to represent the input data, where the non-zero values are represented by the weights of the neural network. The idea behind DLST is to learn a sparse representation of the input data, which can then be used as a biomarker or drug target.

One of the key advantages of DLST is its ability to learn the structure of the input data from scratch. This is achieved by using a sparse linear model, which allows the neural network to learn the weights of the model without learning the entire dataset. This makes DLST very efficient, as it does not require a large amount of data to train.

In addition to its efficiency, DLST has another advantage over other techniques. It is able to learn the structure of the input data from scratch, which makes it very flexible. This allows it to be used for a wide range of applications, including drug targeting and biomarker discovery.

As a drug target, DLST has the potential to revolutionize the field of drug development. Currently, the development of new drugs takes a long and expensive process, which often involves the screening of a large number of compounds. This can be a slow and expensive process, and there is a need for more efficient methods for drug discovery.

DLST has the potential to address this problem by providing a more efficient and less expensive way of drug discovery. By using a sparse linear model to represent the input data, DLST is able to learn the structure of the data from scratch, which can then be used to identify potential drug targets. This has the potential to significantly reduce the time and cost of drug development.

As a biomarker, DLST has the potential to revolutionize the field of diagnostics. Currently, the development of new diagnostics takes a long and expensive process, which often involves the screening of a large number of tests. This can be a slow and expensive process, and there is a need for more efficient methods for biomarker discovery.

DLST has the potential to address this problem by providing a more efficient and less expensive way of biomarker discovery. By using a sparse linear model to represent the input data, DLST is able to learn the structure of the data from scratch, which can then be used to identify potential biomarkers. This has the potential to significantly reduce the time and cost of biomarker discovery.

In conclusion, DLST is a powerful technique that has the potential to revolutionize the field of drug development and diagnostics. Its ability to learn the structure of the input data from scratch makes it a very efficient and flexible method for drug targeting and biomarker discovery. As a result, DLST has the potential to significantly reduce the time and cost of these important fields.

Protein Name: Dihydrolipoamide S-succinyltransferase

Functions: Dihydrolipoamide succinyltransferase (E2) component of the 2-oxoglutarate dehydrogenase complex. The 2-oxoglutarate dehydrogenase complex catalyzes the overall conversion of 2-oxoglutarate to succinyl-CoA and CO(2). The 2-oxoglutarate dehydrogenase complex is mainly active in the mitochondrion (PubMed:29211711, PubMed:30929736). A fraction of the 2-oxoglutarate dehydrogenase complex also localizes in the nucleus and is required for lysine succinylation of histones: associates with KAT2A on chromatin and provides succinyl-CoA to histone succinyltransferase KAT2A (PubMed:29211711)

The "DLST Target / Biomarker Review Report" is a customizable review of hundreds up to thousends of related scientific research literature by AI technology, covering specific information about DLST comprehensively, including but not limited to:
•   general information;
•   protein structure and compound binding;
•   protein biological mechanisms;
•   its importance;
•   the target screening and validation;
•   expression level;
•   disease relevance;
•   drug resistance;
•   related combination drugs;
•   pharmacochemistry experiments;
•   related patent analysis;
•   advantages and risks of development, etc.
The report is helpful for project application, drug molecule design, research progress updates, publication of research papers, patent applications, etc. If you are interested to get a full version of this report, please feel free to contact us at BD@silexon.ai

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