New model-free reinforcement learning methods outperform traditional scheduling in cancer chemotherapy.
problem Optimal scheduling of cancer chemotherapy in the presence of uncertainty.
method Used Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms.
result DDPG outperforms DQN in eradicating cancer, suggesting continuous action space advantages.
Deep learning predicts response to HER2-targeted breast cancer therapy.
problem Predicting response to HER2-targeted neoadjuvant chemotherapy.
method Developed and validated a deep learning approach using pre-treatment dynamic breast MRI.
result Deep learning model achieved strong performance in predicting pathological complete response.
Optimal treatment regimes (OTR) are individualised treatment assignment strategies that identify a medical treatment as optimal given all background information available on the individual. We discuss Bayes optimal treatment regimes estimated using a loss function defined on the bivariate distribution of dichotomous po…
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.
CAST models time-varying treatment effects in cancer patients.
problem Estimating treatment effects at fixed time points limits understanding of dynamic changes over time.
method CAST combines parametric and non-parametric methods to model continuous time-varying treatment effects.
result CAST reveals how treatment effects rise, peak, and decline over the follow-up period.
In many real life problems, objects are described by large number of binary features. For instance, documents are characterized by presence or absence of certain keywords; cancer patients are characterized by presence or absence of certain mutations etc. In such cases, grouping together similar objects/profiles based o…
Physics-Informed Neural Networks (PINNs) benchmarked against clinical estimator and reveal parameter identifiability
problem Chemotherapy pharmacokinetics (PK) with tissue concentration not measured
method Physics-Informed Neural Networks (PINNs) benchmarked against clinical estimator and reveal parameter identifiability
result PINN recovers tissue concentration and identifies non-identifiable parameters
DSL estimates heterogeneous treatment effects over time in survival settings.
problem Complicated by right censoring and time-varying treatment effects.
method Deep survival learner (DSL) for estimating CATEs over a clinically relevant time spectrum.
result DSL reveals heterogeneity in perioperative chemotherapy effects over time.
Bayesian model for cost-effectiveness analysis with subgroup discovery.
problem Statistical challenges in cost-effectiveness analysis, especially with non-random treatment assignment and censored data.
method Developed a nonparametric Bayesian model using Dirichlet and Gamma processes to estimate cost-survival distributions and identify cost-effectiveness subgroups.
result Identified and estimated policy-relevant causal CEA estimands using a Bayesian nonparametric g-computation procedure.
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. 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.
We estimate treatment cost-savings from early cancer diagnosis. For breast, lung, prostate and colorectal cancers and melanoma, which account for more than 50% of new incidences projected in 2017, we combine published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis. We extrapolate to…
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.
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.
With the increased affordability and availability of whole-genome sequencing, large-scale and high-throughput gene expression is widely used to characterize diseases, including cancers. However, establishing specificity in cancer diagnosis using gene expression data continues to pose challenges due to the high dimensio…
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.
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.
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.
Over the past decades, both critical care and cancer care have improved substantially. Due to increased cancer-specific survival, we hypothesized that both the number of cancer patients admitted to the ICU and overall survival have increased since the millennium change. MIMIC-III, a freely accessible critical care data…
Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose but also sub-classify …
We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data w…
Accurately predicting drug responses to cancer is an important problem hindering oncologists' efforts to find the most effective drugs to treat cancer, which is a core goal in precision medicine. The scientific community has focused on improving this prediction based on genomic, epigenomic, and proteomic datasets measu…
As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be…
System automates identification of cancer drug repurposing from PubMed.
problem Manual extraction of cancer drug repurposing evidence from scientific publications is infeasible.
method NLP pipeline including querying, filtering, entity extraction, classification, and study type classification.
result Automated system extracts cancer drug repurposing evidence from PubMed abstracts.
Study identifies biomarkers for lung cancer in female non-smokers.
problem Identifying prognostic biomarkers for stage III NSCLC in non-smoking females.
method Gene expression profiling and XGBoost machine learning algorithm.
result Top biomarkers validated in literature, with AUC score of 0.835.
PathologyGAN learns deep representations of cancer tissue images.
problem Limited high-quality labels for cancer tissue images.
method Developed a GAN framework for unsupervised learning of cancer tissue phenotypes.
result Generated high-quality images with interpretable latent space.
Deep CNN model improves breast cancer screening exam classification.
problem Improving accuracy in breast cancer screening exam classification.
method Localization-based deep CNN trained on 200,000 exams.
result AUC of 0.919 in predicting malignancy, reducing error rate by 23%.
We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit mutation clustering structure. Our results are in-sample stable. They are also out-o…
Modeling correlated mutations in cancer for personalized treatment.
problem Identifying mutations for personalized cancer therapy in heterogeneous profiles.
method Proposed correlated zero-inflated negative binomial process with mixed beta-Bernoulli and variational inference.
result Identified biologically relevant correlations between somatic mutations.
Network Elastic Net identifies smoking-specific gene expression for lung cancer prognosis.
problem Identifying smoking-specific gene expression biomarkers in lung cancer prognosis.
method Introduces Network Elastic Net, a method that clusters and regresses on graphs based on smoking behavior.
result Shows efficacy of clusters in identifying cancer stages using gene expression and smoking behavior.
Automates deep learning model development for cancer data.
problem Manual design of high-performing deep learning models for cancer data is time-consuming and requires expertise.
method Reinforcement-learning-based neural architecture search with custom building blocks.
result Automated discovery of deep neural network architectures with similar or higher accuracy.
BIDIFAC+ factorizes linked matrices for cancer studies.
problem Integrating multiple omics platforms across various cancer types.
method Flexible approach to simultaneous factorization and decomposition of linked matrices using BIDIFAC+.
result Identifies shared and specific modes of variability across multiple omics platforms and cancer types.
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.
Model learns cancer tissue images onto a low-dimensional space revealing tissue characteristics.
problem Improving cancer diagnosis through high-fidelity digital pathology.
method Deep generative model using PathologyGAN to map real images onto a latent space.
result Latent space encodes morphological characteristics and reveals distinct tissue clusters.
Breast cancer is the most frequently reported cancer type among the women around the globe and beyond that it has the second highest female fatality rate among all cancer types. Despite all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still unsatisfactory. …
Study identifies cancer genes through graph anomaly analysis of protein interactions.
problem Insufficient modeling of biological information in protein interaction networks for cancer gene identification.
method Proposes HIerarchical-Perspective Graph Neural Network (HIPGNN) to detect weight heterogeneity and spectral flattening in cancer gene nodes.
result HIPGNN detects weight heterogeneity and spectral flattening, leading to improved cancer gene identification.
Paper optimizes sparse feature selection for cancer detection using GSVP and SVM.
problem Sparse feature selection for cancer detection.
method Regularized GSVP with proximal gradient descent, feature selection via SVM.
result Near-perfect balanced accuracy with few selected features.
Deep neural network for cancer classification using autoencoders.
problem Cancer classification using molecular information.
method Using a Denoising Autoencoder (DAE) as weight initialization for a deep neural network, comparing two approaches: fixed weights and fine-tuning. Embedding strategies included encoding layers and complete autoencoder.
result Best F1 score of 98.04% for identifying thyroid cancer samples.
The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However,…
Objectives: Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary information on cancer treatment and outcomes, yet are rarely used synergistically. To enable detection of metastatic breast cancer (MBC), we appl…
Machine learning models improve cancer type classification accuracy.
problem Early and accurate cancer diagnosis is challenging due to high costs and biological marker limitations.
method Assessed five machine learning algorithms for 17 cancer types using RNA-seq data.
result Ensemble algorithms achieve 100% accuracy in 14 out of 17 cancer types.
A Deep Autoencoder based content retrieval algorithm is proposed for prediction and differentiation of cancer types based on the presence of epigenetic patterns of DNA methylation identified in genetic regions known as CpG islands. The developed deep learning system uses a CpG island state classification sub-system to …
Algorithm segments glandular structures in colon histology images for cancer grading.
problem Manual gland segmentation is time-consuming and risky for patients.
method Local intensity and texture features, Random Forest classifier, multilevel approach.
result Fast, accurate automatic gland segmentation for clinical use.
AI detects oral pre-cancerous lesions with high accuracy.
problem Manual screening of oral cavity cancer is expensive and lacks specialists.
method Deep convolutional neural networks (DCNNs) using transfer learning.
result DCNN models achieve high accuracy in distinguishing between benign and pre-cancerous tongue lesions.
Bayesian model for cancer drug studies maps dose-response curves.
problem Mapping dose-response curves in cancer drug studies.
method Bayesian Tensor Filtering (BTF) with low-dimensional embeddings and structured shrinkage priors.
result BTF outperforms state-of-the-art methods in cancer drug studies.
AI enhances cancer diagnostics using spectroscopy.
problem Early and accurate cancer diagnosis.
method Combining AI with spectroscopy-based techniques.
result AI improves cancer diagnostics speed and safety.