String alignment with translocation insensitivity

A computer system comprising:

• a hardware processor;
• and
• a sequence alignment, in communication with the hardware processor, wherein the sequence alignment is configured:
• to arrange a pair of strings in a similarity matrix;
• to determine a match score for an optimal local alignment of whole-word sequences between the pair of strings;
• to mask the whole-word sequences of the optimal local alignment to generate word-masked strings;
• using the word-masked strings, to repeat the arranging, determining, and masking steps a number of times to generate a number of match scores;
• to combine the number of match scores into a combined score that represents similarities between the pair of strings, wherein the combined score is insensitive to translocation and word truncations;
• to determine alignment between the pair of strings based on the combined score;
• and
• to train a number of machine learning models for sequence alignment using a training dataset that comprises determined alignment between the pair of strings based on the combined score.

A computer program product comprising:

• a computer readable storage media;
• and
• program code, stored on the computer readable storage media, for determining string alignment with insensitivity to translocation, the program code comprising:
• code for arranging a pair of strings in a similarity matrix;
• code for determining a match score for an optimal local alignment of whole-word sequences between the pair of strings;
• code for masking the whole-word sequences of the optimal local alignment to generate word-masked strings;
• code for repeating, using the word-masked strings, the arranging, determining, and masking steps a number of times to generate a number of match scores;
• code for combining the number of match scores into a combined score that represents similarities between the pair of strings, wherein the combined score is insensitive to translocation and word truncations;
• and
• code for determining alignment between the pair of strings based on the combined score;
• and
• code for training a number of machine learning models for sequence alignment using a training dataset that comprises determined alignment between the pair of strings based on the combined score.