Develops methods for GWAS of high dimensional phenotypes using summary statistics.
problem Lack of methods to model pleiotropy in multi-phenotype GWAS.
method Bayesian inference model using summary statistics, fast computation, and biologically informed priors.
result Demonstrates utility in metabolite GWAS with interpretable pathway-level inference.
BayesMR estimates causal effects and directionality from genetic data.
problem Challenges in finding good genetic instruments and estimating causal effects.
method Bayesian Mendelian randomization approach that accounts for pleiotropy and reverse causation.
result BayesMR provides a posterior distribution over causal effects and uncertainty.
Develops a new statistical framework for analyzing genetic pleiotropy in high-dimensional phenotypes.
problem Limited analysis of genetic pleiotropy for high-dimensional phenotypes and genotypes.
method Sparse structural equation models (SEMs) extended to sparse functional SEMs, incorporating both common and rare variants, and using functional data analysis and ADMM techniques.
result Higher power to detect true causal genetic pleiotropic structures compared to existing methods.
ENN method uses expectile regression for genetic data analysis of complex diseases.
problem Discover additional genetic variants contributing to complex diseases.
method Developed an expectile neural network (ENN) method integrating expectile regression and neural networks.
result ENN method outperforms existing expectile regression in discovering genetic variants predisposing to sub-populations.
Semi-supervised GAN creates synthetic genetic data for disease prediction.
problem Expensive and time-consuming to build large labeled genetic databases.
method Semi-supervised Genetic Generative Adversarial Network (gGAN).
result Model achieved satisfactory results with real genetic data.
Efficiently infers graph edges from genetic similarity data in landscape genetics.
problem Inferring unknown graph edges from genetic similarity data in a heterogeneous landscape.
method Developed an efficient first-order optimization method to solve the inverse landscape genetics problem.
result Our method provides fast and reliable convergence, significantly outperforming existing heuristics.
Method distinguishes genetic correlations from causation in GWAS.
problem Identifying causal relationships among genetically correlated traits.
method Mixed fourth moments to quantify causal relationships.
result Identified 30 putative genetically causal relationships across 52 traits.
Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. A non-parametric Bayesian approach in the form o…
New nonparametric HMM improves genetic sequence analysis.
problem Improving genetic sequence data analysis for hidden states and transitions.
method Developed a nonparametric hierarchical Dirichlet process HMM for genetic sequence data.
result Our model provides more parsimonious parameterization of genetic processes.
GPO uses genetic algorithms for deep policy optimization in reinforcement learning.
problem Catastrophic consequence of parameter crossovers in neural networks for deep reinforcement learning.
method GPO uses imitation learning for policy crossover in the state space and applies policy gradient methods for mutation.
result GPO achieves superior performance and comparable sample efficiency compared to state-of-the-art policy gradient methods.
Novel tests for genetic independence in high-dimensional data.
problem Testing independence in genetics studies with many variables.
method Defining premetric structures on genetic data support spaces.
result Solid theoretical framework and computationally-efficient implementations.
Introduces Deep Genetic Network for optimizing neural network hyperparameters.
problem Optimizing neural network hyperparameters is a tedious and time-consuming task.
method Uses genetic algorithms within a deep neural network architecture to optimize hyperparameters during training.
result Deep Genetic Network successfully optimizes hyperparameters in various types of neural network layers.
Deep models improve GWAS by identifying genetic interactions.
problem Missing non-linear interaction effects in GWAS.
method Gradient-based DeepLIFT technique to interpret deep models.
result Known and novel genetic risk factors identified.
GeNet classifies metagenomic sequences with less memory and comparable recall to state-of-the-art methods.
problem Classifying metagenomic sequences from raw DNA sequences.
method Exploits hierarchical structure between labels for training, using deep representations.
result GeNet achieves competitive precision and good recall with less memory requirements.
New method detects outliers in genetic and non-genetic data.
problem Identifying outliers in genetic and non-genetic data.
method Influence function of multiple kernel canonical correlation analysis.
result Visualization method effectively detects influential observations.
New method uses DNN for genetic variant identification, controlling randomness and improving interpretability.
problem Challenges in interpreting deep neural networks for genetic variant identification.
method Interpretable neural network model with controlled variable selection using ensembling, knockoffs, and de-randomization.
result The proposed method leads to more discoveries compared to conventional methods.
Novel method prioritizes genetic variants in nonlinear models.
problem Variable selection in nonlinear and nonparametric regression.
method Developed RATE measure for summarizing variable importance.
result RATE explains improved predictive accuracy of nonlinear models.
G2Ns combine genetic genes with neural networks for reinforcement learning.
problem Improving sample efficiency and performance in reinforcement learning.
method Genetic-Gated Networks (G2Ns) that integrate binary genetic genes with neural network hidden layers.
result G2Ns achieve significant improvements in sample efficiency and performance in reinforcement learning.
A distributed feature selection framework identifies genetic risk factors for Alzheimer's disease.
problem High dimensionality of GWAS data makes it hard to detect genetic risk factors for Alzheimer's disease.
method Distributed Feature Selection Framework (DFSF) with distributed group Lasso screening rules and stability selection.
result The method efficiently identifies relevant genetic risk factors for Alzheimer's disease across multiple institutions.
Paper uses AI to predict stock market volatility with neural networks and genetic algorithms.
problem Traditional methods for predicting stock market volatility have high errors.
method Back-propagation neural network and genetic algorithm integrated model.
result The model predicts future volatility with low errors and high accuracy.
A multilevel model combines genetic and imaging data for AD diagnosis.
problem Classification from multimodal genetic and brain imaging data with unbalanced contributions.
method Multilevel model with structured penalties for joint effects between modalities.
result The model reveals relationships between genes, brain regions, and disease status.
Predict genetic inheritance patterns using hypergraphs and latent models.
problem Diagnosing inherited diseases requires identifying family genetic patterns.
method Represent family trees as hypergraphs, use latent state space models for causal inference.
result Allows for explainable predictions of patient genotypes based on relatives' phenotypes.
In plant and animal breeding studies a distinction is made between the genetic value (additive + epistatic genetic effects) and the breeding value (additive genetic effects) of an individual since it is expected that some of the epistatic genetic effects will be lost due to recombination. In this paper, we argue that t…
Ensemble GP improves genetic programming by achieving better results with smaller models.
problem Improving genetic programming for binary classification problems.
method Ensemble GP uses an evolved population structure, fitness evaluation, and genetic operators inspired by ensemble learning methods.
result Ensemble GP outperformed standard GP on eight binary classification problems, achieving better results with smaller models.
Historical review of genetic algorithms for the TSP shows three distinct phases.
problem Optimizing routes for the Traveling Salesman Problem using genetic algorithms.
method Meta-data analysis of publications over time.
result Three distinct phases in the development of genetic algorithms for TSP identified.
Proposes new genetic algorithm rule for market competition.
problem Market competition genetic algorithm rules.
method Econophysics kinetic market model as an evolutionary algorithm.
result New replacement rule for genetic algorithms.
This paper compares Grid Search, Random Search, and Genetic Algorithm for NAS.
problem Hyperparameter optimization for neural architecture search.
method Comparison of Grid Search, Random Search, and Genetic Algorithm.
result Genetic Algorithm outperforms Grid Search and Random Search in terms of accuracy and execution time.
Deep learning detects genetic interactions in type 2 diabetes.
problem Detecting genetic interactions in complex diseases like type 2 diabetes.
method Stacked Autoencoder for non-linear epistatic interactions.
result Deep learning can uncover missing heritability in complex diseases.
A genetic algorithm improves multivariate kernel density estimation.
problem Efficiently estimating multivariate kernel density functions.
method Genetic algorithm applied to subsamples of the original data.
result The genetic algorithm-based estimator performs better than traditional methods.
This paper improves dynamic hedging accuracy using genetic programming to forecast implied volatilities.
problem Improving the accuracy of dynamic hedging using implied volatilities.
method The paper uses genetic programming to forecast implied volatilities and tests the performance of these forecasts in dynamic hedging strategies.
result Genetic programming-generated implied volatilities improve hedging accuracy compared to static training methods.
New method groups genetic data into coherent topics for disease insights.
problem Analyzing large, multi-dimensional genetic data sets.
method Conditional Hierarchical Bayesian Tucker Decomposition for genetic data analysis.
result Our models are more coherent than baseline models.
For every group genetic code with finite number of generating and at most with one defining relation we introduce the braid group of this genetic code. This construction includes the braid group of Euclidean plane, the braid groups of closed orientable surfaces, B type groups of Artin-Brieskorn, and allow us to study a…
sGLMM corrects genetic associations with complex relatedness and confounding.
problem Correcting spurious associations in complex genetic data with population stratification and relatedness.
method Sparse graph-structured linear mixed model (sGLMM) that incorporates relatedness information and confounding correction.
result sGLMM outperforms existing approaches in modeling correlation from population structure and shared signals.
GADAM uses genetic algorithm to improve Adam's performance in deep learning.
problem Deep learning optimization stuck in local optima.
method GADAM combines Adam and genetic algorithm to evolve unit models.
result GADAM effectively avoids local optima and achieves faster convergence.
Hybrid ML and GA system improves currency exchange prediction.
problem Improving currency exchange prediction accuracy.
method Combines machine learning, genetic algorithms, and technical analysis.
result Improves return on investment from 0.43% to 10.29%.
New models capture complex genetic causes of diseases.
problem Capturing causal relationships between genetic factors and diseases.
method Implicit causal models combining neural architectures and Bayesian inference.
result Significantly outperformed existing genetics methods.
Gradient GA uses gradient information to improve molecular design.
problem Random walk exploration limits genetic algorithms' quality and speed in molecular design.
method Gradient GA incorporates gradient information from the objective function into genetic algorithms, using a differentiable neural network and Discrete Langevin Proposal.
result Significantly improves convergence speed and solution quality over traditional genetic algorithms.
Proposes Genetic Thompson Sampling for multi-armed bandits, improving performance in nonstationary settings.
problem Improving sequential decision making tasks of online learning agents using multi-armed bandits.
method Integrates genetic principles into Thompson Sampling for multi-armed bandits.
result Significantly outperforms baselines in nonstationary settings.
EvoNUDGE uses graph neural networks to improve genetic programming performance.
problem Efficiency in evolutionary computation for problem solving.
method Graph neural network to elicit additional knowledge from symbolic regression problems.
result EvoNUDGE significantly outperforms conventional and neural genetic programming methods.
A distributed framework detects genetic risk factors for Alzheimer's disease across multiple institutions.
problem Privacy concerns in collaborative genetic studies.
method Local Query Model (LQM) and Distributed Enhanced Dual Polytope Projection (D-EDPP) screening rule.
result Successfully learned a consistent model across different institutions without compromising privacy.
As the amount and complexity of genetic information increases it is necessary that we explore some efficient ways of handling these data. This study takes the "divide and conquer" approach for analyzing high dimensional genomic data. Our aims include reducing the dimensionality of the problem that has to be dealt one a…
A new probabilistic LGP method improves symbolic regression performance.
problem Traditional LGP's random search limits its effectiveness.
method Integrates SCFG with LGP, updating grammar based on selected individuals.
result Statistically better results on symbolic regression benchmarks.
New method identifies rare genetic markers for Alzheimer's disease using MRI and genomics.
problem Identifying rare genetic markers for Alzheimer's disease.
method Combining MRI data with genome sequencing, using CNNs for brain traits, and kernel-based tests for rare variants.
result CNNs provide precise brain traits and novel kernels identify rare genetic markers.
Genetic algorithms optimize chess evaluation functions with mentor assistance.
problem Optimizing complex evaluation functions for superior performance.
method Genetic algorithms with mentor-assisted evolution.
result Programs evolved with mentors outperform top chess champions.
Paper proposes a new model to capture genetic interactions and improve prediction accuracy.
problem Capturing gene by gene or other forms of genetical interactions in complex traits.
method Hybrid modeling combining parametric mixed modeling and non-parametric rule ensembles.
result Capturing a part of the 'missing heritability' in complex traits through modeling local epistasis.
Genetic algorithms improve computer chess programs to grandmaster level.
problem Improving computer chess programs to match human grandmaster performance.
method Evolved a grandmaster-level evaluation function and search mechanism using genetic algorithms.
result The evolved program outperforms a world champion and matches other top programs.
GEGL uses genetic experts to improve deep learning for molecular design.
problem Designing molecules with desired properties using deep learning.
method Genetic expert-guided learning (GEGL) framework for training DNN.
result Significantly improves molecular design, achieving high scores on benchmarks.
Optimizes control of synchronization in networked oscillators using genetic programming.
problem Optimizing control of synchronization in complex networked systems.
method Multi-objective genetic programming-based symbolic regression.
result Learned interpretable control functions for driving systems from synchronized to non-synchronized states.