Benchmarking Secondary Structure Predictions

Typedefs

typedef struct vrna_score_s vrna_score_t
#include <ViennaRNA/structures/benchmark.h>

Typename for the score data structure vrna_score_s.

Functions

vrna_score_t vrna_score_from_confusion_matrix(int TP, int TN, int FP, int FN)
#include <ViennaRNA/structures/benchmark.h>

Construct score data structure from given confusion matrix.

Parameters:

  • TP – True positive count

  • TN – True negative count

  • FP – False positive count

  • FN – False negative count

Returns:

The score data structure to write

vrna_score_t vrna_compare_structure_pt(const short *pt_gold, const short *pt_other, int fuzzy)
#include <ViennaRNA/structures/benchmark.h>

Return statistic of two structure (in pair table) comparaison.

See also

vrna_compare_structure

Parameters:

  • pt_gold – Gold standard structure in pair table

  • pt_other – Structure to compare in pair table

  • fuzzy – Allows for base pair slippage. Hence, for any base pair (i,j) in the gold standard, a base pair (p, q) in the second structure is considered a true positive, if i - fuzzy <= p <= i + fuzzy, and j - fuzzy <= q <= j + fuzzy.

Returns:

The vrna_score_s data structure

vrna_score_t vrna_compare_structure(const char *structure_gold, const char *structure_other, int fuzzy, unsigned int options)
#include <ViennaRNA/structures/benchmark.h>

Return statistic of two structure (in dbn) comparaison.

Parameters:

  • pt_gold – Gold standard structure

  • pt_other – Structure to compare

  • fuzzy – Allows for base pair slippage. Hence, for any base pair (i,j) in the gold standard, a base pair (p, q) in the second structure is considered a true positive, if i - fuzzy <= p <= i + fuzzy, and j - fuzzy <= q <= j + fuzzy.

  • options – A bitmask to specify which brackets are recognized during conversion to pair table

Returns:

The vrna_score_s data structure

struct vrna_score_s
#include <ViennaRNA/structures/benchmark.h>

The data structure that contains statistic result of two structures comparaison.

Public Members

int TP

True Positive count.

int TN

True Negative count.

int FP

False Positive count.

int FN

False Negative count.

float TPR

True Positive Rate.

float PPV

Positive Predictive Value.

float FPR

False Positive Rate.

float FOR

False Omission Rate.

float TNR

True Negative Rate.

float FDR

False Discovery Rate.

float FNR

False Negative Rate

float NPV

Negative Predictive Value.

float F1

F1 Score.

float MCC

Matthews Correlation Coefficient.