Developed deep learning models to predict crop yields across diverse environments.
problem Precise crop yield prediction for improved agricultural practices and climate resilience.
method Integrated weather data, used CNN-DNN and CNN-LSTM-DNN models, applied GEM method.
result Achieved superior performance in crop yield prediction with reduced RMSE and MAE.
Deep learning predicts crop yield integrating genotype and weather data.
problem Improving crop yield prediction for diverse climates.
method Long Short Term Memory - Recurrent Neural Network model with temporal attention mechanism.
result Deep learning models outperform traditional methods for yield prediction.
New algorithm improves plant breeding by clustering soybean genotypes more accurately and efficiently.
problem Low accuracy and high computational complexity in clustering plant genotypes.
method Spectral Clustering with Pivotal Sampling for phenotypic data.
result Our algorithm achieves substantially more accuracy than existing methods.
Autoencoders identify brain networks linked to stress and genotype.
problem Designing effective brain stimulation protocols for mental illnesses.
method Supervised autoencoders to model multi-region brain activity.
result Autoencoders reveal a stress-related brain network associated with a bipolar disorder genotype.
Algorithm predicts zoonotic virus emergence with high accuracy.
problem Precise spatio-temporal prediction of zoonotic virus emergence.
method Machine inference using protein sequence databases.
result Quantitative indicators of jump risk from genotypic changes.
Paper models Alzheimer's disease using genotypic, phenotypic, and cognitive data.
problem Early detection and risk factor identification for Alzheimer's disease.
method Probabilistic generative subspace learning from multi-view medical data.
result Proposes a method to model Alzheimer's disease that combines genotypic, phenotypic, and cognitive data.
CNNs classify cancer types based on DNA methylation patterns.
problem Classifying cancer types using DNA methylation data.
method Convolutional Neural Networks (CNNs) for classification.
result CNNs can classify cancer types from DNA methylation profiles.
Study assesses linear classifiers for virus genotyping and subtyping.
problem Challenges in classifying viral sequences, especially in alignment-free methods.
method Comprehensive evaluation of linear classifiers on HCV genomes, varying parameters and sequence lengths.
result Several classifiers perform well under specific conditions, providing robust assessment.
New method learns parameter groups and structures in multi-response models.
problem Discovering unknown grouping structures in multi-response models.
method Proposes two convex regularization formulations and optimization approaches.
result Validated on simulations and real datasets, providing more accurate parameter estimation.
Deep neural network predicts crop yield with high accuracy.
problem Predicting crop yield from complex factors.
method Deep neural network approach using comprehensive datasets.
result Deep neural network achieved superior prediction accuracy.
New model clusters cells and individuals, revealing genetic influences on cell types.
problem Clustering nested data with group-level and observation-level variables.
method Nested Atoms Model (NAM), Bayesian nonparametric approach.
result Identifies clusters of genetically similar individuals with homogeneous cell-type profiles.
A simple algorithm for GWAS estimating SNP effects.
problem Estimating main and epistatic effects of SNPs.
method Pairs of individuals considered instead of SNPs or pairs of SNPs, based on intuitive allele changes leading to phenotype differences.
result Weak dependence on the number of SNPs, strong on the number of individuals.
In statistical genetics an important task involves building predictive models for the genotype-phenotype relationships and thus attribute a proportion of the total phenotypic variance to the variation in genotypes. Numerous models have been proposed to incorporate additive genetic effects into models for prediction or …
GeneCAI optimizes DNN compression hyper-parameters for mobile devices.
problem Efficient deployment of complex DNNs on resource-limited devices.
method GeneCAI uses genetic algorithm to learn optimal hyper-parameters.
result GeneCAI finds models with better accuracy-complexity trade-off.
A genome-wide association study (GWAS) correlates marker variation with trait variation in a sample of individuals. Each study subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here we assume that subjects are unrelated and collected at random and that trait values are n…
Study improves maize yield prediction using BNs with mixed-effects models.
problem Limited causal inference in agronomic data models.
method Integrates random effects into Bayesian networks, leveraging hierarchical data structure.
result Significantly reduces maize yield prediction error from 28% to 17%.
GSU tests association between complex genotypes and phenotypes.
problem Testing association between complex genotypes and phenotypes.
method GSU is a similarity-based test using Laplacian kernel for complex objects.
result GSU identified three genes associated with Alzheimer's Disease.
The widely used genetic pleiotropic analysis of multiple phenotypes are often designed for examining the relationship between common variants and a few phenotypes. They are not suited for both high dimensional phenotypes and high dimensional genotype (next-generation sequencing) data. To overcome these limitations, we …
Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, that can be applied to WGS data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutation…
GWHD dataset offers 4,700 high-res images of wheat heads.
problem Challenges in wheat head detection from high-resolution imagery.
method Large, diverse dataset with detailed metadata.
result Benchmark for wheat head detection methods.
Novel MCA-based rule mining speeds up psychiatric disorder screening.
problem High dimensionality and heterogeneity in clinical data.
method Developed a novel categorical rule mining method based on MCA.
result 100x faster than state-of-the-art methods for large datasets.
Automates neural network model selection for efficiency.
problem Efficiently choosing neural network architectures and hyper-parameters.
method Modified micro-genetic algorithm for automated model selection.
result AMS finds efficient neural network models for classification and regression.
AI improves healthcare diagnostics and predictions.
problem Data heterogeneity and model limitations in AI for health.
method Review of AI applications in health informatics.
result AI enhances disease diagnosis and prediction.
Deep learning model predicts crop yields using CNN-RNN.
problem Challenges in predicting crop yields due to multiple factors.
method CNN-RNN framework using environmental and management data.
result CNN-RNN model outperformed other methods by 9-8% RMSE.
The perennial problem of "how many clusters?" remains an issue of substantial interest in data mining and machine learning communities, and becomes particularly salient in large data sets such as populational genomic data where the number of clusters needs to be relatively large and open-ended. This problem gets furthe…
VAEs struggle with surjective multimodal data, especially class labels describing images.
problem VAEs struggle to capture variability in surjective multimodal data.
method Theoretical and empirical demonstration of VAEs with a mixture of experts posterior.
result VAEs with a mixture of experts posterior can disregard variation in surjective multimodal data.
Methods for analysis of principal components in discrete data have existed for some time under various names such as grade of membership modelling, probabilistic latent semantic analysis, and genotype inference with admixture. In this paper we explore a number of extensions to the common theory, and present some applic…
Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the …
New KNN test improves association analysis of high-dimensional sequencing data.
problem Challenges in using neural networks for high-dimensional sequencing data analysis.
method Kernel-based neural network (KNN) test for complex association analysis.
result KNN test outperforms SKAT in detecting non-linear and interaction effects.
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.
The paper introduces a new method to infer phylogenetic trees without bifurcations.
problem Inferring phylogenetic trees with zero-length branches and polytomies.
method Adaptive LASSO-type regularization estimators for phylogenetics.
result Regularization is a practical approach for phylogenetics, revealing zero-length branches.
MRPC improves causal graph inference in R with better accuracy.
problem Accurate inference of causal graphs from data.
method Improves the PC algorithm with online FDR control, v-structure identification, and robust correlation estimation.
result Significantly reduces false positive edges and accurately identifies v-structures.
Federated learning improves bioinformatics by sharing data legally.
problem Lack of access to diverse data in bioinformatics.
method Combines data from multiple institutions legally.
result Federated learning accelerates clinical discovery and robust exploration.
A new variational inference method speeds up AMMI model estimation.
problem High-dimensional GxE interaction data in plant breeding.
method Variational inference for AMMI model.
result The new method is 2x faster with similar predictive performance.
Evolutionary methods improve understanding of LLMs and their relationships.
problem Improving understanding of LLMs and their relationships.
method Relating weights to genotypes and output text to phenotypes using evolutionary methods.
result Estimated evolutionary trees reliably recover the topology of the ground-truth training tree.
DeepSequence model predicts mutation effects better than existing methods.
problem Quantifying complex interactions in biomolecules.
method Latent variable models with nonlinear dependencies.
result DeepSequence predicts mutation effects significantly better than site-independent or pairwise models.
The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications. Realizing this potential, however, requires novel statistical analysis methods that are both interpretable and predictive. We introduce Union of Intersections (UoI), a flexible, m…
Quality-Diversity algorithms explore multiple high-performing solutions in a search space.
problem Finding multiple high-performing solutions in complex optimization problems.
method Evolutionary computation approach focusing on behavioral space and holistic solution distribution.
result Quality-Diversity algorithms provide a comprehensive view of high-performing solutions in a search space.
Volterra and polynomial regression models play a major role in nonlinear system identification and inference tasks. Exciting applications ranging from neuroscience to genome-wide association analysis build on these models with the additional requirement of parsimony. This requirement has high interpretative value, but …
Genomic predictors explain 40% of human height variance.
problem Missing heritability in human height prediction.
method High-dimensional statistics and machine learning.
result Predicted heights correlate 0.65 with actual height.
Paper compares Bayesian and de-biased estimators for low-rank matrix completion.
problem Predict missing entries in partially observed matrices.
method Bayesian and de-biased estimators comparison.
result De-biased estimator performs similarly to Bayesian estimators but is more stable and can outperform in small samples.
As an increasing number of genome-wide association studies reveal the limitations of attempting to explain phenotypic heritability by single genetic loci, there is growing interest for associating complex phenotypes with sets of genetic loci. While several methods for multi-locus mapping have been proposed, it is often…
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.
A model predicts which patients can safely use a warfarin dosing algorithm.
problem Determining the optimal initial dose for warfarin patients.
method Support Vector Machines with a polynomial kernel function.
result The model increases dosing accuracy by 15% in RMSE and 17% in MAE.
Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called grap…
High-throughput sequencing allows the detection and quantification of frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor cell populations. In some cases, the evolutionary history and population frequency of the subclonal lineages of tumor cells present in the sample can be reconstructed from…
Motivation: Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. Wang et al. (Bioinformatics, 2012) have developed an approach for the analysis of imaging genomic studies using penalized multi-task regressi…
New model detects gradual changes in processes more accurately.
problem Traditional change-point models fail to identify gradual changes effectively.
method Introduces a Bayesian change-dynamic model using hierarchical models for gradual change detection.
result The model identifies gradual changes faster and more accurately than traditional models.