SenMortalityAge#
Index#
Let’s first import some packages:
[1]:
import os
import inspect
import shutil
import json
import torch
import pandas as pd
import pyaging as pya
Instantiate model class#
[2]:
def print_entire_class(cls):
source = inspect.getsource(cls)
print(source)
print_entire_class(pya.models.SenMortalityAge)
class SenMortalityAge(LinearReferenceClock):
pass
[3]:
model = pya.models.SenMortalityAge()
Define clock metadata#
[4]:
model.metadata["clock_name"] = 'senmortalityage'
model.metadata["data_type"] = 'methylation'
model.metadata["species"] = 'Homo sapiens'
model.metadata["year"] = 2026
model.metadata["approved_by_author"] = '⌛'
model.metadata["citation"] = "Kasamoto, Kotaro, et al. \"DNA methylation clocks for estimating replicative senescence in human cells.\" Aging Cell (2026): e70430."
model.metadata["doi"] = "https://doi.org/10.1111/acel.70430"
model.metadata["research_only"] = None
model.metadata["notes"] = "Senescence-enriched DNA-methylation predictor of mortality risk, constructed from CpGs prioritized for their association with cellular senescence and with mortality. It belongs to a clock family that showed little CpG overlap between the senescence, chronological-age and mortality signals."
model.metadata["tissue"] = 'whole blood (Framingham Heart Study)'
model.metadata["predicts"] = 'mortality/time-to-death risk (senescence-enriched CpGs)'
model.metadata["unit"] = 'relative risk/hazard'
model.metadata["model_type"] = 'Cox regression'
model.metadata["platform"] = 'Illumina 450K/EPIC'
model.metadata["population"] = 'adults'
model.metadata["journal"] = 'Aging Cell'
model.metadata["last_author"] = 'Albert T Higgins-Chen'
model.metadata["n_features"] = 91
model.metadata["citations"] = 0
model.metadata["citations_date"] = '2026-07-05'
Download clock dependencies#
[5]:
os.system(f"curl -sL -o SenMortalityAge_CpGs.csv https://raw.githubusercontent.com/HigginsChenLab/methylCIPHER/19b12296b0d7eb7055a97d068064df635f44ce3e/data-raw/SenescenceAge/SenMortalityAge_CpGs.csv")
[5]:
0
Load features#
[6]:
coef_df = pd.read_csv('SenMortalityAge_CpGs.csv')
model.features = coef_df['CpG'].tolist()
Load weights into base model#
[7]:
weights = torch.tensor(coef_df['Coefficient'].tolist()).unsqueeze(0).float()
intercept = torch.tensor([0.0]).float()
[8]:
base_model = pya.models.LinearModel(input_dim=len(model.features))
base_model.linear.weight.data = weights.float()
base_model.linear.bias.data = intercept.float()
model.base_model = base_model
Load reference values#
[9]:
model.reference_values = None
Load preprocess and postprocess objects#
[10]:
model.preprocess_name = None
model.preprocess_dependencies = None
[11]:
model.postprocess_name = None
model.postprocess_dependencies = None
Check all clock parameters#
[12]:
pya.utils.print_model_details(model)
%==================================== Model Details ====================================%
Model Attributes:
training: True
metadata: {'approved_by_author': '⌛',
'citation': 'Kasamoto, Kotaro, et al. "DNA methylation clocks for estimating '
'replicative senescence in human cells." Aging Cell (2026): '
'e70430.',
'clock_name': 'senmortalityage',
'data_type': 'methylation',
'doi': 'https://doi.org/10.1111/acel.70430',
'notes': None,
'research_only': None,
'species': 'Homo sapiens',
'version': None,
'year': 2026}
reference_values: None
preprocess_name: None
preprocess_dependencies: None
postprocess_name: None
postprocess_dependencies: None
features: ['cg00006787', 'cg00054496', 'cg00458878', 'cg00459119', 'cg03175417', 'cg03366574', 'cg04094193', 'cg04234014', 'cg05396212', 'cg06015525', 'cg06507987', 'cg06542681', 'cg07458308', 'cg08332990', 'cg08726900', 'cg08770961', 'cg10682299', 'cg11974796', 'cg12132563', 'cg12506165', 'cg13029847', 'cg13315970', 'cg13714749', 'cg14205663', 'cg15163603', 'cg16086130', 'cg16181396', 'cg16374999', 'cg16552271', 'cg17340519']... [Total elements: 91]
base_model_features: None
%==================================== Model Details ====================================%
Model Structure:
base_model: LinearModel(
(linear): Linear(in_features=91, out_features=1, bias=True)
)
%==================================== Model Details ====================================%
Model Parameters and Weights:
base_model.linear.weight: [0.1905025690793991, 2.4848039150238037, 0.8850484490394592, 0.7116715908050537, 2.5755298137664795, 3.6517186164855957, 1.0995451211929321, 0.43131953477859497, 1.3369412422180176, 1.197675108909607, 0.7618207931518555, 0.18213650584220886, 3.231579303741455, 0.2401280403137207, 0.8895606994628906, 0.3462097942829132, 2.011119842529297, 0.7437288761138916, 0.5966653823852539, 0.6629067659378052, 0.5791985988616943, 1.4628826379776, 0.20292864739894867, 1.2046616077423096, -0.05603138357400894, 1.015661358833313, 0.6152376532554626, 0.8949704766273499, 0.13805358111858368, 0.4781338572502136]... [Tensor of shape torch.Size([1, 91])]
base_model.linear.bias: tensor([0.])
%==================================== Model Details ====================================%
Basic test#
[13]:
torch.manual_seed(42)
input = torch.randn(10, len(model.features), dtype=float)
model.eval()
model.to(float)
pred = model(input)
pred
[13]:
tensor([[-32.2390],
[ 10.5977],
[ 19.5190],
[ -3.3155],
[ 12.2869],
[ 26.4824],
[ 3.7673],
[-23.1682],
[-18.8041],
[ -3.3204]], dtype=torch.float64, grad_fn=<AddmmBackward0>)
Save torch model#
[14]:
torch.save(model, f"../weights/{model.metadata['clock_name']}.pt")
Clear directory#
[15]:
# Function to remove a folder and all its contents
def remove_folder(path):
try:
shutil.rmtree(path)
print(f"Deleted folder: {path}")
except Exception as e:
print(f"Error deleting folder {path}: {e}")
# Get a list of all files and folders in the current directory
all_items = os.listdir('.')
# Loop through the items
for item in all_items:
# Check if it's a file and does not end with .ipynb
if os.path.isfile(item) and not item.endswith('.ipynb'):
os.remove(item)
print(f"Deleted file: {item}")
# Check if it's a folder
elif os.path.isdir(item):
remove_folder(item)
Deleted file: SenMortalityAge_CpGs.csv