Self-learning linear regression on a dna-computing platform

A computer program product, comprising a computer-readable hardware storage device having a computer-readable program code stored therein, said program code configured to be executed by a processor of a computer system to implement a method for using machine-learning on a DNA-computing platform to perform linear regression, the method comprising:

• the processor identifying a first set of equal-length nucleobase sequences,
• wherein the identifying each sequence comprises selecting an order of nucleobases that each encode two or more numeric values of a set of numeric values,
• wherein each value encoded into a first sequence of the first set of sequences represents an estimated value of a beta coefficient comprised by a corresponding first equation of a system of linear equations;
• and
• wherein each sequence of the first set has a length equal to a shortest length capable of encoding all values of beta coefficients of any single equation comprised by the system of linear equations;
• the processor further identifying a second set of equal-length nucleobase sequences that are each capable of bonding through a Watson Crick reaction to a corresponding sequence of the first set of equal-length nucleobase sequences;
• the processor adjusting each value of the set of numeric values as a function of a result of a Watson Crick reaction between a first set of nucleobase chains and a second set of nucleobase chains,
• wherein each chain of the first set of nucleobase chains comprises nucleobases ordered in a sequence of the first set of nucleobase sequences, and
• wherein each chain of the second set of nucleobase chains comprises nucleobases ordered in a sequence of the second set of nucleobase sequences;
• the processor replacing each value of the set of numeric values with its corresponding adjusted value;
• and
• the processor repeating the identifying, further identifying, adjusting, and replacing until the adjusted values converge over two or more successive adjustings to a constant set of values.

A computer system comprising a processor, a memory coupled to said processor, and a computer-readable hardware storage device coupled to said processor, said storage device containing program code configured to be run by said processor via the memory to implement a method for using machine-learning on a DNA-computing platform to perform linear regression, the method comprising:

• the processor identifying a first set of equal-length nucleobase sequences,
• wherein the identifying each sequence comprises selecting an order of nucleobases that each encode two or more numeric values of a set of numeric values,
• wherein each value encoded into a first sequence of the first set of sequences represents an estimated value of a beta coefficient comprised by a corresponding first equation of a system of linear equations;
• and
• wherein each sequence of the first set has a length equal to a shortest length capable of encoding all values of beta coefficients of any single equation comprised by the system of linear equations;
• the processor further identifying a second set of equal-length nucleobase sequences that are each capable of bonding through a Watson Crick reaction to a corresponding sequence of the first set of equal-length nucleobase sequences;
• the processor adjusting each value of the set of numeric values as a function of a result of a Watson Crick reaction between a first set of nucleobase chains and a second set of nucleobase chains,
• wherein each chain of the first set of nucleobase chains comprises nucleobases ordered in a sequence of the first set of nucleobase sequences, and
• wherein each chain of the second set of nucleobase chains comprises nucleobases ordered in a sequence of the second set of nucleobase sequences;
• the processor replacing each value of the set of numeric values with its corresponding adjusted value;
• and
• the processor repeating the identifying, further identifying, adjusting, and replacing until the adjusted values converge over two or more successive adjustings to a constant set of values.