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+ DKFZ (The German Cancer Research Center), March 15, 2024, Heidelberg
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+ ![]()
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+ Finding important genes
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+ ![]()
+ $
+ f(x_1,\, \ldots,\, x_p) = \beta_0 + \sum_{i} x_i \beta_i
+ + \sum_{i \le j} x_i x_j \beta_{i,j}
+ $
+ $
+ + \sum_{i \le j \le k} x_i x_j x_k \beta_{i,j,k}
+ + \ldots
+ $
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+ ![]()
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+ Kieran Elmes et al. Learning epistatic gene interactions from perturbation screens. 2021
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+ ![]()
+ + + Sarah Howles et al. Genetic variants of calcium and vitamin D metabolism in kidney stone disease. 2019 + +
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+ ![]()
+ + + Kieran Elmes et al. Learning epistatic gene interactions from perturbation screens. 2021 + + +
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+ Hierarchy and scalability are largely solved (until you sequence more)
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+ ![]()
+ + + Kieran Elmes et al. A fast lasso-based method for inferring higher-order interactions + + +
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+ + + Kieran Elmes et al. A fast lasso-based method for inferring higher-order interactions + + +
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+ ![]()
+ + + Kieran Elmes et al. SNVformer: an attention-based deep neural network for GWAS data + + +
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+ ![]()
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+ ![]()
+ What AI? +
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+ ![]()
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++ Algorithms for reconstructing within-patient tumor histories to support precision oncology +
++ Alex Gavryushkin +
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+ ![]()
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+ DKFZ (The German Cancer Research Center), March 15, 2024, Heidelberg
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+ Disclaimers
+Precision oncology
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+Phylogenetic trees on tumor cells
+
+Problem
+This result is not consistent across datasets — why?
+Model-based approaches to genotype-phenotype data
+
+ $
+ f(x_1,\, \ldots,\, x_p) = \beta_0 + \sum_{i} x_i \beta_i
+ + \sum_{i \le j} x_i x_j \beta_{i,j}
+ $
+ $
+ + \sum_{i \le j \le k} x_i x_j x_k \beta_{i,j,k}
+ + \ldots
+ $+
+State of the art in 2018
+
+
+
+ Kieran Elmes et al. Learning epistatic gene interactions from perturbation screens. 2021
+
+
+Problem #1: The hierarchy assumptions
+
+ + + Sarah Howles et al. Genetic variants of calcium and vitamin D metabolism in kidney stone disease. 2019 + +
Problem #2: Scalability
+
+ + + Kieran Elmes et al. Learning epistatic gene interactions from perturbation screens. 2021 + + +
+ + + Kieran Elmes et al. A fast lasso-based method for inferring higher-order interactions + + +
Problem #3 (unsolved!):
+Model assumptions
+ $ + f(x_1,\, \ldots,\, x_p) = \beta_0 + \sum_{i} x_i \beta_i + + \sum_{i \le j} x_i x_j \beta_{i,j} + $ + $ + + \sum_{i \le j \le k} x_i x_j x_k \beta_{i,j,k} + + \ldots + $+ + + Kieran Elmes et al. A fast lasso-based method for inferring higher-order interactions + + +
SNVformer
+
+ + + Kieran Elmes et al. SNVformer: an attention-based deep neural network for GWAS data + + +
"eQTL" simulations (work in progress)
+
+
+ -
+
- Multi-Layer Perceptron [ AI ] +
- Logistic Regression [ :) ] +
- Transformer-encoder [ AI ] +
- LightGBM (Random Forests) [ AI? ] +
- Differentiable Logic [ AI ] +
- Support Vector Machines [ AI? ] +
Why don't you just do AI?
+
+ What AI? +
bioDS lab @UCNZ
+
+Thank you!
+
+