Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. As such, architectures that fit the structure of genomics should …
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48 results for “Genomic Data”
Application of Markov Structure of Genomes to Outlier Identification and Read Classificationq-bio.GN
Paper uses genome Markov structure for outlier detection and read classification.
problem Identifying outliers and classifying reads in genome databases.
method Applying second-order Markov models to triplet base distributions.
result Improved accuracy in outlier identification and read classification.
Elastic co-clustering improves clustering of single-cell genomic data.
problem Improving clustering performance of single-cell genomic datasets.
method Elastic coupled co-clustering in an unsupervised transfer learning framework.
result Our algorithm significantly improves clustering performance over traditional methods.
Dr.S recommends cancer drugs based on genomic data.
problem Personalizing cancer treatments using genomic information.
method Machine learning to identify optimal drug-gene associations.
result Developed a Drug Recommendation System (Dr.S) for cancer cell lines.
New method combines ensembling and regularization for genomic disease prediction.
problem Genomic diseases require accurate prediction and biomarker identification.
method Integrates regularization with ensembling techniques for high-dimensional binary classification.
result Identifies critical biomarkers overlooked by competing methods.
The paper predicts diseases using both clinical and genomics data.
problem Clinical predictions using genomics data are not common.
method Integrated clinical and genomics datasets, machine learning, Principal Component Analysis for feature selection.
result 73% accuracy in predicting 75 disease classes.
PKB method uses pathway information for cancer sample classification.
problem Cancer genomic data's high dimensionality and limited sample sizes.
method Pathway-based Kernel Boosting (PKB) method integrating gene pathway information for sample classification.
result PKB method outperforms other methods and identifies relevant pathways.
Generates new human genomic sequences for LAI training.
problem Lack of accessible reference data sets for LAI.
method Class-conditional VAE-GAN to generate realistic sequences.
result Generated sequences improve LAI method performance.
Private cancer prediction model trained on federated genomic data.
problem Train a private cancer prediction model on federated genomic data.
method Differentially private federated learning (FL) for genomic cancer prediction.
result Ranked 3rd in a competition for private cancer prediction.
A Neural Network Model to Classify Liver Cancer Patients Using Data Expansion and Compressionstat.ML
Neural network classifies liver cancer patients based on genomic data.
problem Classifying liver cancer patients into high-risk and low-risk groups.
method Data expansion using wavelet analysis, compression of wavelet coefficients, training a neural network model.
result The neural network model accurately classifies patients without survival time information.
Random Fragments Classification of Microbial Marker Clades with Multi-class SVM and N-Best Algorithmq-bio.QM
SVM and N-best algorithm classify microbial marker clades from genome sequences.
problem Classifying microbial clades from genome sequences, especially new species.
method Support vector machine (SVM) with N-best algorithm, time series feature extraction, random fragment generation, k-mer size selection.
result Recognition accuracy rates above 28% in top-1 candidate, above 91% in top-10 candidate.
TF-MoDISco finds transcription factor motifs from genomic data.
problem Identifying transcription factor motifs from genomic sequence data.
method Algorithm for motif discovery from basepair-level importance scores.
result Improved version v0.5.6.5 of TF-MoDISco.
Paper proposes scalable method for analyzing multi-omic data.
problem Integrating high-dimensional multi-omic data for cancer subtyping.
method Mixed graphical model approach using Birth-Death MCMC algorithm.
result Our method outperforms LASSO and standard BDMCMC in computational efficiency and model selection accuracy.
Dilated convolutions model long-distance genomic dependencies effectively.
problem Detecting regulatory elements from raw DNA with long-distance dependencies.
method Developed and used a novel dataset for dilated convolutional neural networks.
result Dilated convolutions are effective at modeling regulatory elements in the human genome.
Measures DNA quality degradation effects.
problem Identifying degraded DNA sequence data.
method Novel quality quantification based on intentional degradation effects.
result Quantified measures of degradation can be used for multiple purposes.
New algorithm detects changes in genomic data faster and more accurately.
problem Detecting changes in genomic data with constraints.
method Adapting a functional pruning technique to solve constrained changepoint detection problems.
result Log-linear time complexity algorithm achieves state-of-the-art accuracy.
fiBAG integrates multiplatform genomic data to identify disease markers.
problem Understanding complex mechanisms underlying human diseases from multiplatform genomic data.
method fiBAG uses Gaussian process models and Bayes factors to identify functional evidence and guide variable selection.
result fiBAG improves detection of disease-related markers compared to non-integrative methods.
Proposes FDR-corrected sparse CCA for neuroimaging and genomics.
problem High-dimensional datasets in neuroimaging and genomics make false discoveries a concern.
method FDR-corrected sparse canonical correlation analysis (CCA) for high-dimensional settings.
result The proposed method controls the FDR of canonical vectors in high-dimensional settings.
With different genomes available, unsupervised learning algorithms are essential in learning genome-wide biological insights. Especially, the functional characterization of different genomes is essential for us to understand lives. In this book chapter, we review the state-of-the-art unsupervised learning algorithms fo…
Towards the Latent Transcriptomeq-bio.GN
Method computes embeddings for RNA-seq data without genome alignment.
problem No need for genome alignment for RNA-seq data analysis.
method RNN transforms kmers into 2D latent space for transcriptomic analysis.
result Captures DNA sequence similarity and abundance in latent space.
Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencodercs.LG
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.
PKB framework boosts genomic data analysis by integrating pathway knowledge.
problem Boosting discovery power and connecting new findings with biological mechanisms in genomic data.
method Pathway-based Kernel Boosting (PKB) framework integrating clinical and pathway information for prediction of various outcomes.
result PKB substantially outperforms other methods in predicting drug response and cancer survival.
Develops a faster soybean genome clustering method combining spectral and vector quantization.
problem Clustering soybean whole genome sequences efficiently.
method Combines Spectral Clustering and Vector Quantization for computational efficiency.
result Significantly outperforms existing methods in cluster quality and time complexity.
An approach for learning ancestral causal relationships in high dimensions, validated on human genome-wide data.
problem Learning ancestral causal relationships in high-dimensional biological data.
method Supervised learning approach with discrete indicators treated as labels, scalable to large problems.
result The approach is highly effective and scalable to the human genome-wide setting, robust to perturbations of input information.
Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by a…
Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selectionstat.ME
Guided adaptive shrinkage uses co-data to improve feature selection in genomic studies.
problem Feature selection challenges in high-dimensional genomics data, especially in clinical settings.
method Guided adaptive shrinkage methods that use co-data to adapt shrinkage parameters.
result Improves feature selection in genomic studies, demonstrated through comparisons and examples.
Machine learning accurately diagnoses cancer from whole genome sequencing data.
problem Accurate cancer diagnosis at all stages.
method Novel MLAC (Machine Learning Against Cancer) method using next-gen RNA sequencing.
result Perfect precision, sensitivity, and specificity achieved for most tumor types.
Locally epistatic genomic relationship matrices for genomic association, prediction and selectionstat.AP
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…
Semi-supervised deep learning detects problematic reads for genome assembly.
problem De novo genome assembly is hindered by specific types of reads.
method Analysis of coverage graphs converted to 1D-signals using semi-supervised deep learning models.
result Semi-supervised deep learning models can detect problematic reads with minimal labeled data.
SEISM tests neural network features for regulatory genomics.
problem Testing neural network features for regulatory genomics.
method Selective inference procedure for sequence motifs.
result Sampling under specific parameters characterizes composite null hypothesis.
Understanding functional organization of genetic information is a major challenge in modern biology. Following the initial publication of the human genome sequence in 2001, advances in high-throughput measurement technologies and efficient sharing of research material through community databases have opened up new view…
Method corrects deep generative model likelihood scores for OOD detection.
problem Challenges in deploying neural networks on out-of-distribution data.
method Likelihood ratio method for deep generative models.
result Proposed method achieves state-of-the-art OOD detection performance.
In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation reflect environmental risk factors, and are increasingly recognised for their fundamental role in diseases such as cancer. DNA methylation is a…
Genomic models learn DNA sequences to predict functions.
problem Understanding complex genetic interactions.
method Training LLMs on DNA sequences to predict functions.
result gLMs can predict functions of DNA elements.
Cluster Quilting clusters fragmented data sets for neuroscience and genomics.
problem Clustering fragmented data sets in neuroscience and genomics.
method Cluster Quilting method using patch ordering, patchwise SVD, sequential linear mapping, and k-means.
result Cluster Quilting discovers more accurate clusters than other methods.
SUQUAN optimizes quantile normalisation for better downstream analysis.
problem Optimizing target distribution for better downstream analysis.
method Optimizes target distribution jointly with other parameters in the analysis.
result SUQUAN outperforms standard quantile normalisation on various data types.
Spectral simplicial theory improves feature selection for complex data.
problem Complex data sets and high-dimensional feature spaces require efficient feature selection methods.
method Extends spectral techniques to abstract simplicial complexes, incorporating topological data analysis.
result Spectral simplicial methods provide a unified approach for feature selection in multi-modal genomic data.
Prototype Matching Network (PMN) improves genomic TFBS prediction.
problem Predicting Transcription Factor Binding Sites (TFBSs) with hundreds of TFs as labels.
method Prototype Matching Network (PMN) that learns motif-like features and TF-TF interactions.
result PMN significantly outperforms baselines on a large TFBS dataset.
EBIC is a biclustering tool for big genomic data, achieving significant speedup.
problem Mining genetic data for high-dimensional and big data challenges.
method EBIC is a biclustering algorithm enhanced for big data, including support for missing values and integration with R.
result EBIC achieves over 6.6 fold speedup on large datasets, demonstrating high scalability.
We present a novel method for extracting cancer signatures by applying statistical risk models (http://ssrn.com/abstract=2732453) from quantitative finance to cancer genome data. Using 1389 whole genome sequenced samples from 14 cancers, we identify an "overall" mode of somatic mutational noise. We give a prescription …
Graphs represent gene segment organization, revealing complex interrelationships in a scrambled genome.
problem Understanding gene segment organization and interrelationships in a scrambled genome.
method Directed graphs representing gene segments and their relationships, with graph properties mapped to higher-dimensional space for analysis.
result Emerging star-like structures indicate complex interrelationships, including segments from multiple genes interleaving or overlapping.
Fast and cheaper next generation sequencing technologies will generate unprecedentedly massive and highly-dimensional genomic and epigenomic variation data. In the near future, a routine part of medical record will include the sequenced genomes. A fundamental question is how to efficiently extract genomic and epigenomi…
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies. We address the problem of learning interpretable models for discrete phenotypes from whole genomes. We propose a general approach that relies on the Set Covering Machine and a k-mer representation of the genomes. We sho…
New algorithm classifies and generates genomic sequences using RG-flow categorifier.
problem Classifying and generating genomic sequences for disease prediction.
method RG-flow based categorifier combining quantum field theory, holographic duality, and neural ODEs.
result RG categorifier can classify and generate new sequences from genomic data.
iRF detects stable high-order interactions in genomics data.
problem Understanding high-order interactions in genomics data.
method Iterative Random Forest algorithm (iRF) for stable high-order interaction detection.
result iRF identifies stable high-order interactions with computational cost similar to Random Forest.
The paper develops a scalable method to infer GRNs from sparse data.
problem Inferring complex gene regulatory networks from limited and temporally sparse data.
method Bayesian optimization and kernel-based methods to construct a Gaussian Process (GP) model.
result The method efficiently searches for the topology with the highest likelihood value.
New algorithm clusters sparse data effectively.
problem Challenges in clustering sparse data.
method Deterministic Information Bottleneck framework for joint feature weighting and clustering.
result Demonstrated effectiveness on real-world genomics data.
A Bayesian Boolean Matrix Factorization for cancer genomics
problem Identifying coordinated feature changes in cancer
method Bayesian Boolean Matrix Factorization
result Captures widespread, near-simultaneous chromosome-number changes