row_id
string | series_id
string | timepoint_h
int64 | organism
string | strain_id
string | drug_a
string | drug_b
string | stress_index
float64 | baseline_mic_a_mg_L
float64 | baseline_mic_b_mg_L
float64 | mic_a_mg_L
float64 | mic_b_mg_L
float64 | a_resistant_cutoff_mg_L
float64 | b_susceptible_floor_mg_L
float64 | b_sensitivity_fold_change
float64 | media
string | assay_method
string | source_type
string | collateral_sensitivity_signal
int64 | earliest_collateral_sensitivity
int64 | notes
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ABXCT003-TR-0001
|
S1
| 0
|
Escherichia coli
|
EC-CLIN101
|
ciprofloxacin
|
gentamicin
| 0.1
| 0.25
| 2
| 0.25
| 2
| 4
| 0.5
| 1
|
CAMHB
|
MIC_broth_microdilution
|
simulated
| 0
| 0
|
baseline
|
ABXCT003-TR-0002
|
S1
| 12
|
Escherichia coli
|
EC-CLIN101
|
ciprofloxacin
|
gentamicin
| 0.9
| 0.25
| 2
| 4
| 1
| 4
| 0.5
| 2
|
CAMHB
|
MIC_broth_microdilution
|
simulated
| 1
| 1
|
A crosses resistant B drops 2x
|
ABXCT003-TR-0003
|
S1
| 24
|
Escherichia coli
|
EC-CLIN101
|
ciprofloxacin
|
gentamicin
| 0.9
| 0.25
| 2
| 8
| 0.5
| 4
| 0.5
| 4
|
CAMHB
|
MIC_broth_microdilution
|
simulated
| 1
| 0
|
pattern persists
|
ABXCT003-TR-0004
|
S2
| 0
|
Klebsiella pneumoniae
|
KP-CLIN220
|
meropenem
|
amikacin
| 0.1
| 0.5
| 4
| 0.5
| 4
| 8
| 1
| 1
|
CAMHB
|
MIC_broth_microdilution
|
simulated
| 0
| 0
|
baseline
|
ABXCT003-TR-0005
|
S2
| 24
|
Klebsiella pneumoniae
|
KP-CLIN220
|
meropenem
|
amikacin
| 0.9
| 0.5
| 4
| 16
| 2
| 8
| 1
| 2
|
CAMHB
|
MIC_broth_microdilution
|
simulated
| 1
| 1
|
A crosses B drops 2x
|
ABXCT003-TR-0006
|
S2
| 48
|
Klebsiella pneumoniae
|
KP-CLIN220
|
meropenem
|
amikacin
| 0.9
| 0.5
| 4
| 32
| 1
| 8
| 1
| 4
|
CAMHB
|
MIC_broth_microdilution
|
simulated
| 1
| 0
|
persists
|
ABXCT003-TR-0007
|
S3
| 0
|
Pseudomonas aeruginosa
|
PA-CLIN330
|
ciprofloxacin
|
tobramycin
| 0.1
| 0.25
| 1
| 0.25
| 1
| 4
| 0.25
| 1
|
CAMHB
|
MIC_broth_microdilution
|
simulated
| 0
| 0
|
baseline
|
ABXCT003-TR-0008
|
S3
| 24
|
Pseudomonas aeruginosa
|
PA-CLIN330
|
ciprofloxacin
|
tobramycin
| 0.9
| 0.25
| 1
| 4
| 1
| 4
| 0.25
| 1
|
CAMHB
|
MIC_broth_microdilution
|
simulated
| 0
| 0
|
B did not drop
|
ABXCT003-TR-0009
|
S4
| 0
|
Escherichia coli
|
EC-CLIN600
|
ciprofloxacin
|
gentamicin
| 0.1
| 0.25
| 2
| 0.25
| 2
| 4
| 0.5
| 1
|
CAMHB
|
MIC_broth_microdilution
|
simulated
| 0
| 0
|
baseline
|
ABXCT003-TR-0010
|
S4
| 24
|
Escherichia coli
|
EC-CLIN600
|
ciprofloxacin
|
gentamicin
| 0.3
| 0.25
| 2
| 4
| 0.5
| 4
| 0.5
| 4
|
CAMHB
|
MIC_broth_microdilution
|
simulated
| 0
| 0
|
stress low even though pattern exists
|
license: mit language: - en pretty_name: ABX-CT-003 Collateral Sensitivity Pattern tags: - antibiotics - combination-therapy - collateral-sensitivity - mic - resistance - tabular task_categories: - tabular-classification size_categories: - n<1k
ABX-CT-003 Collateral Sensitivity Pattern
Purpose
Detect the paired pattern where resistance to Drug A coincides with increased sensitivity to Drug B.
Core pattern
- stress_index high
- Drug A MIC crosses a_resistant_cutoff_mg_L
- Drug B MIC drops at least 2x vs baseline and reaches a low floor
- Drug B drop occurs at the same or next timepoint in v1
Files
- data/train.csv
- data/test.csv
- scorer.py
Schema
Each row is one timepoint in a within strain series.
Required columns
- row_id
- series_id
- timepoint_h
- organism
- strain_id
- drug_a
- drug_b
- stress_index
- baseline_mic_a_mg_L
- baseline_mic_b_mg_L
- mic_a_mg_L
- mic_b_mg_L
- a_resistant_cutoff_mg_L
- b_susceptible_floor_mg_L
- b_sensitivity_fold_change
- media
- assay_method
- source_type
- collateral_sensitivity_signal
- earliest_collateral_sensitivity
Labels
collateral_sensitivity_signal
- 1 for rows at or after the first detected collateral sensitivity row
earliest_collateral_sensitivity
- 1 only for the first detected row in that series
Scorer logic in v1
- baseline Drug A must be below resistant cutoff
- find first time Drug A crosses resistant cutoff under stress
- collateral sensitivity triggers if Drug B at the same or next timepoint
- drops by at least 2x vs baseline
- reaches b_susceptible_floor_mg_L or lower
Evaluation
Run
- python scorer.py --path data/test.csv
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