GitHub - okayode/NeurIPS_TS4H_25: We propose to learn time-varying interactions between cells, such as those of bladder cancer tumors and immune cells, and their response to a combination of anticancer treatments in a limited data scenario

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
.ipynb_checkpoints		.ipynb_checkpoints
data		data
figures		figures
.DS_Store		.DS_Store
CTM_vol.txt		CTM_vol.txt
C_vol.txt		C_vol.txt
M_vol.txt		M_vol.txt
T_vol.txt		T_vol.txt
Untitled.ipynb		Untitled.ipynb
dM.txt		dM.txt
kGC.txt		kGC.txt
kGM.txt		kGM.txt
kGT.txt		kGT.txt
kTC.txt		kTC.txt
loss.txt		loss.txt
output_1.txt		output_1.txt
output_10.txt		output_10.txt
output_2.txt		output_2.txt
output_3.txt		output_3.txt
output_4.txt		output_4.txt
output_5.txt		output_5.txt
output_6.txt		output_6.txt
output_7.txt		output_7.txt
output_8.txt		output_8.txt
output_9.txt		output_9.txt
pC.txt		pC.txt
pTdT.txt		pTdT.txt
rM.txt		rM.txt
readme.txt		readme.txt
sCT.txt		sCT.txt
sMT.txt		sMT.txt
sMT_Robust.py		sMT_Robust.py
time.txt		time.txt

Repository files navigation

#############################################################################################
# rm .txt files in the directory except the readme.txt

find . -maxdepth 1 -type f -name "*.txt" ! -name "readme.txt" -exec rm {} \;

#############################################################################################
#  How to run the code																		#
#############################################################################################

for job in 1 2 3 4 5 6 7 8 9 10; do python3 sMT_Robust.py > output_${job}.txt; done

#############################################################################################
#  How to generate the txt files															#
#############################################################################################

for i in {1..10}; do grep "ctmvol:" output_$i.txt | sed 's/ctmvol: //' >> CTM_vol.txt; echo >> CTM_vol.txt; done

# sub populations
for i in {1..10}; do grep "cancercells:" output_$i.txt | sed 's/cancercells: //' >> C_vol.txt; echo >> C_vol.txt; done
for i in {1..10}; do grep "tcells:" output_$i.txt | sed 's/tcells: //' >> T_vol.txt; echo >> T_vol.txt; done
for i in {1..10}; do grep "mdscs:" output_$i.txt | sed 's/mdscs: //' >> M_vol.txt; echo >> M_vol.txt; done

# time varying parameter
for i in {1..10}; do grep "supp:" output_$i.txt | sed 's/supp: //' >> sMT.txt; echo >> sMT.txt; done

for i in {1..10}; do grep "timepoints:" output_$i.txt | sed 's/timepoints: //' >> time.txt; echo >> time.txt; done
for i in {1..10}; do grep "loss:" output_$i.txt | sed 's/loss: //' >> loss.txt; echo >> loss.txt; done

# constant parameters
for i in {1..10}; do grep "kTC:" output_$i.txt | sed 's/kTC: //' >> kTC.txt; echo >> kTC.txt; done
for i in {1..10}; do grep "kGT:" output_$i.txt | sed 's/kGT: //' >> kGT.txt; echo >> kGT.txt; done
for i in {1..10}; do grep "kGM:" output_$i.txt | sed 's/kGM: //' >> kGM.txt; echo >> kGM.txt; done
for i in {1..10}; do grep "kGC:" output_$i.txt | sed 's/kGC: //' >> kGC.txt; echo >> kGC.txt; done
for i in {1..10}; do grep "sCT:" output_$i.txt | sed 's/sCT: //' >> sCT.txt; echo >> sCT.txt; done
for i in {1..10}; do grep "pTdT:" output_$i.txt | sed 's/pTdT: //' >> pTdT.txt; echo >> pTdT.txt; done
for i in {1..10}; do grep "pC:" output_$i.txt | sed 's/pC: //' >> pC.txt; echo >> pC.txt; done
for i in {1..10}; do grep "dM:" output_$i.txt | sed 's/dM: //' >> dM.txt; echo >> dM.txt; done
for i in {1..10}; do grep "rM:" output_$i.txt | sed 's/rM: //' >> rM.txt; echo >> rM.txt; done

About

We propose to learn time-varying interactions between cells, such as those of bladder cancer tumors and immune cells, and their response to a combination of anticancer treatments in a limited data scenario

openreview.net/forum?id=8j7PSxDB2X

physics-informed-neural-networks time-varying-parameters anticancer-treatments sparse-biological-data

Readme