EpiRL learns to detect gene-gene interactions.
problem Computational challenges in epistasis detection.
method Modeling epistasis as a Markov Decision Process and using reinforcement learning.
result EpiRL discovers highly interacted genes.
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48 results for “gene detection”
Deep learning models improve cancer detection and typing classification from gene expression data.
problem Challenges in establishing specificity for cancer diagnosis using gene expression data.
method Developed deep learning models using mRNA datasets for cancer detection and typing classification.
result Achieved 98% accuracy in cancer detection and 18 out of 32 cancer-typing classifications over 90% accuracy.
New methods detect continuous variation in single-cell data.
problem Continuous variation within and between cell types not detected by discrete analyses.
method Three topologically motivated mathematical methods for unsupervised feature selection.
result Detect additional biologically meaningful genes with coherent expression patterns.
Proposes a method to identify relevant genes in autism-related diseases using auxiliary information.
problem Identifying relevant genes in autism-related diseases from diverse data sources.
method Uses logistic regression to filter irrelevant genes and clusters relevant genes into cohesive groups using adjacency matrix.
result Superior performance and robustness in finite samples observed in simulation studies.
Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysisstat.ML
In genome-wide interaction studies, to detect gene-gene interactions, most methods are divided into two folds: single nucleotide polymorphisms (SNP) based and gene-based methods. Basically, the methods based on the gene are more effective than the methods based on a single SNP. Recent years, while the kernel canonical …
Unsupervised two-view learning, or detection of dependencies between two paired data sets, is typically done by some variant of canonical correlation analysis (CCA). CCA searches for a linear projection for each view, such that the correlations between the projections are maximized. The solution is invariant to any lin…
Bayesian methods detect clusters in noisy data more reliably.
problem Noisy data distorts traditional clustering methods, leading to unreliable results.
method Bayesian community detection using Minimum Description Length principle.
result Bayesian methods identify more robust clusters in noisy data.
The linking genotype to phenotype is the fundamental aim of modern genetics. We focus on study of links between gene expression data and phenotype data through integrative analysis. We propose three approaches. 1) The inherent complexity of phenotypes makes high-throughput phenotype profiling a very difficult and labor…
Motivation: The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects--such as a gene and a disease--can be related in different ways, for example…
A hierarchical community detection method using recursive partitioning.
problem Finding interpretable and accurate community structures in networks.
method Top-down recursive partitioning starting with spectral clustering.
result The algorithm correctly recovers community trees under mild assumptions.
Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learningcs.SI
Paper proposes MLPCD for protein community detection in large PPI networks.
problem Identifying reliable protein communities from large-scale PPI networks.
method Integrates Gene Expression Data and uses Multi-source Learning with cloud computing.
result Demonstrates superior performance compared to existing methods.
SL2MF predicts synthetic lethality using logistic matrix factorization.
problem Predicting synthetic lethality in human cancers from limited experimental data.
method Logistic matrix factorization incorporating biological knowledge.
result SL2MF effectively predicts known and unknown SL interactions.
Test assesses if a linear classifier is random or significant.
problem Determining if a linear classifier captures meaningful differences between classes.
method Proposes a homogeneity test related to linear separability, establishes upper bounds for p-values.
result Upper bounds for p-values are highly accurate for normally distributed samples.
Estimates target GGM using auxiliary studies with false discovery rate control.
problem Estimating high-dimensional GGMs from related studies.
method Transfer learning with Trans-CLIME and debiased Trans-CLIME estimators.
result Debiased Trans-CLIME estimator provides element-wise asymptotic normality and false discovery rate control.
Deep learning detects bifurcations in dynamical systems.
problem Predicting catastrophic changes in dynamical systems across sciences.
method Data-driven, physically-informed deep-learning framework for classifying dynamical regimes and characterizing bifurcation boundaries.
result Extracts topologically invariant features to detect bifurcation boundaries in unseen systems.
Efficient algorithm for sparse PCA reduces data complexity.
problem Sparse PCA for high-dimensional data with non-convex optimization issues.
method Convex FPS formulation, gradient-based optimization, online learning extension.
result Explicit bounds on optimization error and statistical accuracy.
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent representation. Additional latent variables account for technical effects that may erroneously set some observations of gene expression leve…
In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data. Using a semiparametric method on a reproducing kernel Hilbert space (RKHS), we used a standard mixed-effects linear mo…
Motivation: Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments. These biclustering techniques have focused on one data source, often gene expres…
A new method uses gene interaction networks to predict gene functions.
problem Predicting gene functions from gene interactions.
method Context graph kernel approach in a machine learning framework.
result The proposed method outperforms linkage-assumption-based methods.
A New Statistical Framework for Genetic Pleiotropic Analysis of High Dimensional Phenotype Datastat.ML
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 …
The paper improves Fisher-Pitman tests for Poisson mixtures, detecting autism-related genes.
problem Detecting differentially expressed genes between autism and control subjects.
method Nonparametric Poisson mixtures and Fisher-Pitman permutation tests.
result The tests reveal genes missed by common methods, demonstrating rate optimality.
VEGN uses graph neural networks to predict disease-causing mutations from genetic variants.
problem Identifying disease-causing mutations from millions of genetic variants.
method VEGN employs a graph neural network on a heterogeneous graph of genes and variants, learning gene-gene interactions.
result VEGN outperforms existing state-of-the-art models in variant effect prediction.
Automated tests detect interactions in unstructured data.
problem Detecting interactions between latent variables in low-dimensional systems.
method Derive two interaction tests based on pairwise interventions and integrate them into an active learning pipeline.
result Tests can identify more known biological interactions than random search and standard active learning baselines.
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…
A new method for identifying significant gene subsets improves disease prediction.
problem Identifying significant subsets of genes for disease prediction.
method Kernel gene shaving using influence function of kernel CCA.
result The proposed method outperformed three popular gene selection methods.
Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into co…
New method estimates latent gene expression factors without overlap with known confounders.
problem Estimating latent variance components in gene expression data with known confounders.
method Restricted maximum-likelihood method maximizing likelihood on orthogonal subspace.
result Method reduces runtime and attains greater likelihood values than gradient-based optimizers.
Bayesian model learns cell types and gene networks from two data views.
problem Estimating cell types and their regulatory networks from single-cell gene expression and epigenetic data.
method Symphony Bayesian hierarchical multi-view mixture model with Variational EM inference.
result Symphony outperforms other methods in learning cell types and regulatory networks.
New method expands seed genes to functionally related clusters.
problem Discovering functionally related genes lacking GO terms.
method Semi-supervised learning with positive and unlabeled examples.
result LPU approaches significantly outperform existing methods.
Framework analyzes leaf vein architecture using deep learning and statistical methods.
problem Discards structural information in leaf venation studies.
method Integrates deep learning and statistical techniques to represent and analyze leaf vascular architecture.
result Identifies significant gene-environment interactions in leaf vascular architecture.
GLasso: Regularized Multi-task Graphical Lasso for Joint Estimation of eQTL Mapping and Gene Networkstat.ML
A new method for joint eQTL mapping and gene network estimation.
problem Discovering SNP-gene relationships and gene-gene relationships in gene expression regulation.
method L1-2 regularized multi-task graphical lasso (L1-2 GLasso).
result Competitive performance on capturing true sparse structures of eQTL mapping and gene network.
New method handles correlated genes for better genomic prediction.
problem Technical issues with highly correlated genes in prediction models.
method Grouping algorithm that treats correlated genes as a group and uses their common patterns.
result Significantly outperforms standard models in prediction and feature selection.
Popular online enrichment analysis tools from the field of molecular systems biology provide users with the ability to submit their experimental results as gene sets for individual analysis. Such queries are kept private, and have never before been considered as a resource for integrative analysis. By harnessing gene s…
Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Predictioncs.LG
VGAE learns gene-disease associations from networks, predicting disease-genes.
problem Predicting gene-disease associations from disease-gene networks.
method Introducing VGAE, a variational graph auto-encoder for disease-gene prediction.
result VGAE and C-VGAE outperform baseline methods in disease-gene prediction.
Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-con…
TWIST algorithm detects communities in multi-layer networks with tensor decomposition.
problem Community detection in multi-layer networks with multiple node-modality relationships.
method Tensor-based TWIST algorithm for global/local node and layer memberships.
result Accurate community detection with small misclassification error as network size increases.
We present the extention and application of a new unsupervised statistical learning technique--the Partition Decoupling Method--to gene expression data. Because it has the ability to reveal non-linear and non-convex geometries present in the data, the PDM is an improvement over typical gene expression analysis algorith…
Blind Source Separation (BSS) has proven to be a powerful tool for the analysis of composite patterns in engineering and science. We introduce Convex Analysis of Mixtures (CAM) for separating non-negative well-grounded sources, which learns the mixing matrix by identifying the lateral edges of the convex data scatter p…
Collaborative filtering predicts drug responses from gene expression data.
problem Predicting drug responses from large gene expression datasets with limited samples.
method Low-rank matrix factorization and latent linear regression.
result The proposed method outperforms state-of-the-art methods in predicting drug-gene associations.
A novel method selects genes for high-dimensional gene expression data with class imbalance.
problem Class imbalance in gene expression datasets.
method Synthetic data balancing, greedy search, weighted robust score.
result The proposed method outperforms existing feature selection procedures.
Proposes a novel network-based neighborhood regression for biological systems.
problem Lack of comprehensive analysis on biological modules using both global and local network data.
method Develops a community-wise least square optimization approach to analyze gene modules and their regulatory strength.
result Achieves exact minimax optimality and linear consistency in identifying gene module associations.
The problem of multilabel classification when the labels are related through a hierarchical categorization scheme occurs in many application domains such as computational biology. For example, this problem arises naturally when trying to automatically assign gene function using a controlled vocabularies like Gene Ontol…
Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we de…
Proposes a new signal model for high-dimensional, small-sample-size data.
problem Signal detection in high-dimensional, small-sample-size datasets.
method Intrinsic signal model based on dynamical system assumption.
result Taguchi method effectively detects signals in the proposed model.
The method integrates survival constraints into NMF for identifying survival-associated gene clusters.
problem Understanding and interpreting high-dimensional biological data for disease markers.
method Cox proportional hazards regression integrated with NMF via proportional hazards non-negative matrix factorization.
result The method can uncover survival-associated gene clusters in cancer gene expression data.
Biogeography-Based Informative Gene Selection and Cancer Classification Using SVM and Random Forestscs.NE
Microarray cancer gene expression data comprise of very high dimensions. Reducing the dimensions helps in improving the overall analysis and classification performance. We propose two hybrid techniques, Biogeography - based Optimization - Random Forests (BBO - RF) and BBO - SVM (Support Vector Machines) with gene ranki…
Bayesian optimization improves classifier selection for acute infection and mortality.
problem Improving accuracy of acute infection and mortality prediction.
method Comparison of hyperparameter optimization methods (grid search, random sampling, Bayesian optimization).
result Bayesian optimization outperforms grid search or random sampling for in-hospital mortality classifiers.