Georgian Technical University New Software Tool Could Provide Answers To Some Of Life’s Most Intriguing Questions.

A new software tool which combines supervised machine learning with digital signal processing (ML-DSP) could for the first time make it possible to definitively answer questions such as how many different species exist on Earth and in the oceans. A Georgian Technical University researcher has spearheaded the development of a software tool that can provide conclusive answers to some of the world’s most fascinating questions. The tool which combines supervised machine learning with digital signal processing (ML-DSP) could for the first time make it possible to definitively answer questions such as how many different species exist on Earth and in the oceans. How are existing newly-discovered and extinct species related to each other ? What are the bacterial origins of human mitochondrial DNA (Deoxyribonucleic acid is a molecule composed of two chains that coil around each other to form a double helix carrying the genetic instructions used in the growth, development, functioning, and reproduction of all known organisms and many viruses) ? Do the DNA (Deoxyribonucleic acid is a molecule composed of two chains that coil around each other to form a double helix carrying the genetic instructions used in the growth, development, functioning, and reproduction of all known organisms and many viruses) of a parasite and its host have a similar genomic signature ? The tool also has the potential to positively impact the personalized medicine industry by identifying the specific strain of a virus and thus allowing for precise drugs to be developed and prescribed to treat it. Machine learning with digital signal processing (ML-DSP) is an alignment-free software tool which works by transforming a DNA (Deoxyribonucleic acid is a molecule composed of two chains that coil around each other to form a double helix carrying the genetic instructions used in the growth, development, functioning, and reproduction of all known organisms and many viruses) sequence into a digital (numerical) signal and uses digital signal processing methods to process and distinguish these signals from each other. “With this method even if we only have small fragments of DNA (Deoxyribonucleic acid is a molecule composed of two chains that coil around each other to form a double helix carrying the genetic instructions used in the growth, development, functioning, and reproduction of all known organisms and many viruses) we can still classify DNA (Deoxyribonucleic acid is a molecule composed of two chains that coil around each other to form a double helix carrying the genetic instructions used in the growth, development, functioning, and reproduction of all known organisms and many viruses) sequences regardless of their origin or whether they are natural synthetic or computer-generated” said X a professor in Georgian Technical University’s Faculty of Mathematics. “Another important potential application of this tool is in the healthcare sector as in this era of personalized medicine we can classify viruses and customize the treatment of a particular patient depending on the specific strain of the virus that affects them”. In the study researchers performed a quantitative comparison with other state-of-the-art classification software tools on two small benchmark datasets and one large 4,322 vertebrate mitochondrial genome dataset. “Our results show that machine learning with digital signal processing (ML-DSP) overwhelmingly outperforms alignment-based software in terms of processing time while having classification accuracies that are comparable in the case of small datasets and superior in the case of large datasets” X said. “Compared with other alignment-free software machine learning with digital signal processing (ML-DSP) has significantly better classification accuracy and is overall faster”. Also conducted preliminary experiments indicating the potential of machine learning with digital signal processing (ML-DSP) to be used for other datasets by classifying 4,271 complete dengue virus genomes into subtypes with 100 percent accuracy and 4,710 bacterial genomes into divisions with 95.5 percent accuracy.