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.
Bayesian model identifies cancer pathways using genomic data.
problem Identifying altered pathways associated with specific cancer types.
method Bayesian semi-nonnegative tri-matrix factorization incorporating biological prior knowledge.
result Pathways identified can be used as prognostic biomarkers.
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.
NetBiTE predicts drug sensitivity and identifies biomarkers in cancer.
problem Predicting drug sensitivity and identifying biomarkers in cancer.
method NetBiTE combines prior knowledge and gene expression data using a biased tree ensemble approach.
result NetBiTE outperforms RF in predicting IC50 drug sensitivity for drugs targeting membrane receptor pathways.
A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially va…
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.
Deep learning model explains breast cancer subtypes using logistic regression.
problem Clarifying the mechanisms of breast cancer subtypes for better treatment.
method Developed a PWL model that generates custom-made logistic regression for each patient.
result The PWL model reveals genes relevant to cell cycle-related pathways.
Deep Learning identifies 20 critical proteins linked to FLT3-ITD mutation in leukemia.
problem Identifying critical proteins associated with FLT3-ITD mutation in leukemia.
method Hierarchical Deep Learning network using autoencoders for feature extraction.
result Deep Learning accurately correlates 20 critical proteins with FLT3-ITD mutation (97% accuracy).
Paper proposes inference method for high-dimensional censored quantile regression.
problem Identifying heterogeneous effects of high-dimensional genetic biomarkers on survival outcomes.
method Combines low-dimensional model estimates based on multi-sample splittings and variable selection.
result Proposed estimator is consistent and asymptotically follows a Gaussian process.
Develops platforms to analyze social media data for human behavior and emotions.
problem Understanding human behavior and emotions from social media data.
method Self-structuring incremental machine learning, event detection, natural language processing.
result Captured salient topics and events from social media data, validated against news.
A new method speeds up overlapping group lasso computations.
problem Time-consuming optimization of overlapping group lasso on large-scale problems.
method Non-overlapping statistical approximation to overlapping group lasso.
result The proposed penalty is statistically equivalent to overlapping group lasso.
Paper classifies lung nodules in CT scans, improving diagnostic accuracy.
problem Early detection of lung nodules for better treatment planning.
method Proposes four 3D neural networks for direct mapping from 3D images to class labels.
result 3D multi-output DenseNet achieves state-of-the-art classification accuracy.
Paper uses machine learning to identify key pathways for c-di-GMP in bacterial genomes.
problem Understanding pathways essential for c-di-GMP in bacterial cellulose production.
method Applied Lasso and Random Forests for feature selection and modeling gene count data.
result Bacterial chemotaxis is identified as the most essential pathway for c-di-GMP encoding domains.
Deep learning improves radiotherapy by automating head and neck cancer organ segmentation.
problem Manual delineation of radio-sensitive organs in head and neck cancer radiotherapy is time-consuming and variably performed.
method A 3D U-Net deep learning architecture trained on clinical CT scans and expert segmentations.
result Deep learning model achieves expert-level performance in delineating 21 distinct OARs.
Bayesian approach infers signaling pathways from data.
problem Accurately understand cellular regulation processes.
method Dynamic Bayesian Network structure estimation using Markov Chain Monte Carlo.
result Efficient sampling of sparse graphs improves inference.
PUMA interprets metabolomics data to predict pathway activity and assign chemical identities.
problem Interpreting metabolomics data to determine biochemical pathway activities.
method Generative probabilistic modeling using stochastic sampling.
result PUMA predicts pathway activity and assigns chemical identities to metabolites.
Machine learning identifies key metabolic control circuits in bacterial pathways.
problem Identifying regulated metabolic pathways in bacteria.
method Machine learning approach analyzing multi-omics data.
result Identification of E. coli Glycolysis regulatory circuits.
Crowdsourced reinforcement learning optimizes knee replacement pathway, reducing costs.
problem Optimizing the sequential decision process for knee replacement surgery.
method Reinforcement learning, value iteration, state compression, kernel representation, cross validation.
result Optimized policy reduces overall cost by 7% and excessive cost premium by 33%.
We categorise coherent band (aka nullification) pathways between knots and 2-component links. Additionally, we characterise the minimal coherent band pathways (with intermediates) between any two knots or 2-component links with small crossing number. We demonstrate these band surgeries for knots and links with small cr…
CRNN discovers chemical reaction pathways from data.
problem Challenging to infer reaction pathways for complex systems.
method Neural network approach that satisfies fundamental physics laws.
result CRNN autonomously discovers reaction pathways from species concentration data.
Bayesian model clusters diverse 'omics data for disease subtyping.
problem Clustering diverse 'omics datasets conflates multiple structures.
method Multi-view Bayesian mixture model with semi-supervised learning.
result Identifies distinct clusters of patients for stratified medicine.
Bayesian hypergraph inference models disease pathways from EHR data.
problem Modeling rare diseases influenced by shared risk factors.
method Bayesian hypergraph inference framework reframing multi-disease modeling.
result Interpretable disease pathways and well-calibrated uncertainty quantification.
In many scientific studies, it becomes increasingly important to delineate the causal pathways through a large number of mediators, such as genetic and brain mediators. Structural equation modeling (SEM) is a popular technique to estimate the pathway effects, commonly expressed as products of coefficients. However, it …
Develops a model for RNA-seq data clustering.
problem Challenges in clustering RNA-seq count data with variable selection.
method Sparse negative binomial mixture model with lasso or fused lasso regularization.
result Superior performance in clustering accuracy, feature selection, and biological interpretation.
Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks. The framework is based on encoding information on sparse pathways and recognizing the correct pathway at inference time. Elaborating further on this insight, we pro…
New method disentangles hidden data structures using HSIC and supervision.
problem Tackles the challenge of interpreting high-dimensional data.
method Supervised Independent Subspace Principal Component Analysis (sisPCA) using HSIC.
result Identifies and separates hidden data structures effectively.
Method infers multi-layer networks from gene expression data.
problem Inference of multi-level networks from gene expression data.
method Extension of latent graphical lasso method leveraging group structure.
result Efficacy in retrieving multi-layer network structure from synthetic data.
Estimates cost savings from early cancer diagnosis.
problem Improving early cancer diagnosis to reduce treatment costs.
method Combining published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis, and extrapolating to other cancer sites.
result Estimates U.S. national annual treatment cost-savings from early cancer diagnosis in the trillions.
Model predicts anti-cancer drug responses using gene and molecular data.
problem Expensive and time-consuming cancer drug discovery and tailoring.
method Uses variational autoencoders and multi-layer perceptrons to encode gene expression and drug data.
result High average R2 of 0.83 and 0.845 in predicting drug responses for breast and pan-cancer cell lines, respectively. Study uses machine learning to predict heart failure in cancer patients.
problem Early detection of cancer patients at risk for cardiotoxicity.
method Examined four machine learning algorithms on 143,199 cancer patients.
result Gradient boosting model achieved best AUC score of 0.9077.
System accurately detects lung cancer from CT images.
problem Early and accurate detection of lung cancer.
method Developed algorithms using a dataset of CT images.
result Accuracy of 72.2% on test dataset.
This research improves multitask learning by creating task-specific pathways.
problem Creating task-specific representations for efficient learning.
method Developed generalization bounds for learning multiple tasks over multiple pathways.
result Proves the superiority of multipath representation over traditional shallow supernets.
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.
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 …
Study predicts 10-year survival rates for breast cancer patients.
problem Predicting long-term survival of breast cancer patients.
method Machine learning approaches to assess survival rates.
result Improved accuracy in predicting 10-year survival.
Study uses Apple ML to accurately detect and classify lung cancer.
problem Accurate diagnosis and sub-classification of non-small cell lung cancer.
method Evaluation of Apple Create ML module on histopathological images.
result 100% detection and successful subclassification of non-small cell lung cancer.
Cancer patients admitted to ICU had improved survival over 10 years.
problem To assess changes in survival of cancer patients admitted to ICU over 10 years.
method Retrospective analysis of MIMIC-III database, adjusted for confounders using logistic regression.
result Cancer patients had significantly lower 28-day and 1-year mortality rates over 10 years.
Paper identifies key CpG methylation sites for breast cancer.
problem Early detection and treatment of breast cancer.
method Used machine learning on TCGA dataset to classify cancer vs. non-cancer samples.
result Reduced model with 25 key CpG sites achieves over 94% accuracy.
Data mining techniques predict breast cancer types with high accuracy.
problem Early detection of breast cancer to reduce mortality rates.
method Twelve classification algorithms applied to the Breast Cancer Wisconsin dataset.
result High accuracy in predicting malignant and benign breast cancer.
Study examines perceptions and attitudes about breast cancer on Twitter.
problem Understanding public perceptions and attitudes towards breast cancer on social media.
method Identified and collected tweets, used topic modeling and sentiment analysis.
result Identified themes and quantified users' perceptions and emotions about breast cancer.
Machine learning clusters mutations in cancer exomes, improving diagnostic speed and cost.
problem Extracting stable mutation structures from cancer exome data for early diagnostics.
method Statistically deterministic machine learning algorithm *K-means applied to exome samples.
result Majority of cancer types exhibit stable mutation clustering, while NMF methods are unstable.
Developed PathInf for network inference from incomplete data.
problem Massive and non-uniformly distributed missing values in data.
method Two-stage inference model: data summarization and graph inference.
result Consistently superior performance compared to state-of-the-art methods.
Deep learning predicts breast cancer with high accuracy from patient data.
problem Early detection of breast cancer from patient data.
method Feature selection and k-fold Monte Carlo cross-validation using deep learning.
result Deep learning model effectively distinguishes between cancer and healthy patients.
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.
Bayesian networks for gene regulatory pathways using hybrid quantum-classical ML.
problem Elucidate gene regulatory pathways from proteomics data.
method Hybrid quantum-classical machine learning framework to build Bayesian networks.
result Scalable framework for learning gene regulatory networks from proteomics data.
The paper develops methods to optimize personalized policies in complex causal pathways.
problem Optimizing policies in healthcare settings with entangled causal effects.
method Combining mediation analysis and dynamic treatment regime ideas for longitudinal data.
result Derived methods for learning high-quality policies from observational data.
Neural networks improve cancer risk prediction from family history data.
problem Improving cancer risk prediction from family history data using machine learning.
method Developed and trained neural network models on large pedigrees to predict hereditary cancers.
result Neural networks can achieve nearly optimal prediction performance and outperform traditional models in misreported data.
Machine learning detects metastatic breast cancer cases from linked EMR and cancer registry data.
problem Lack of metastatic recurrence data in cancer registries and EMRs.
method Semi-supervised machine learning on linked EMR and CCR data.
result Model achieved high accuracy in detecting metastatic breast cancer cases.