A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Neural networks improve cancer risk prediction from family history data.
Copula-based fusion improves breast cancer risk stratification.
Study uses machine learning to predict heart failure in cancer patients.
New algorithm predicts lung cancer progression and mortality.
Proposes a new model to analyze CT scans for lung cancer patients.
SurvLatent ODE predicts VTE risk for cancer patients, outperforming current methods.
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 …
We develop a neural network model to classify liver cancer patients into high-risk and low-risk groups using genomic data. Our approach provides a novel technique to classify big data sets using neural network models. We preprocess the data before training the neural network models. We first expand the data using wavel…
Model learns cancer tissue images onto a low-dimensional space revealing tissue characteristics.
Febrile neutropenia (FN) has been associated with high mortality, especially among adults with cancer. Understanding the patient and provider level heterogeneity in FN hospital admissions has potential to inform personalized interventions focused on increasing survival of individuals with FN. We leverage machine learni…
Deep learning predicts breast cancer with high accuracy from patient data.
Method predicts ODX scores for breast cancer patients based on clinical data.
PersonalizedUS assesses breast cancer risk with local coverage guarantees.
Improving cancer treatment decisions requires considering causal effects, not just model accuracy.
Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive …
Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patient…
Study compares LSTM, Transformer, and Mamba for bladder cancer recurrence analysis.
A novel dose-finding design for cancer clinical trials using level set estimation.
This study predicts ovarian cancer from cysts using TVUS and machine learning.
Paper presents a machine learning method to predict cancer sub-clones.
CAT framework improves AI medical screening fairness and reliability.
Optimal cutoff interval for risk scores improves binary classification accuracy.
New model improves cancer screening prediction accuracy.
The purpose of this paper is to construct confidence intervals for the regression coefficients in the Fine-Gray model for competing risks data with random censoring, where the number of covariates can be larger than the sample size. Despite strong motivation from biomedical applications, a high-dimensional Fine-Gray mo…
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…
New method groups genetic data into coherent topics for disease insights.
Bayesian framework improves survival prediction accuracy and uncertainty quantification.
Paper develops a method to predict cancer patient survival using molecular profiles.
Over 150,000 new people in the United States are diagnosed with colorectal cancer each year. Nearly a third die from it (American Cancer Society). The only approved noninvasive diagnosis tools currently involve fecal blood count tests (FOBTs) or stool DNA tests. Fecal blood count tests take only five minutes and are av…
Model predicts anti-cancer drug responses using gene and molecular data.
System accurately detects lung cancer from CT images.
PDBAL targets experiments for probabilistic models to maximize insights.
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…
Study predicts 10-year survival rates for breast cancer patients.
In this paper we describe a novel implementation of adaboost for prediction of survival function. We take different variations of the algorithm and compare the algorithms based on system run time and root mean square error. Our construction includes right censoring data and competing risk data too. We take different da…
Gradient boosting enhances existing Mendelian models for genetic disease risk prediction.
Paper identifies key CpG methylation sites for breast cancer.
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…
Machine learning accurately diagnoses cancer from whole genome sequencing data.
Deep learning models are vulnerable to adversarial attacks, leading to critical errors.
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…
Non-synonymous single nucleotide polymorphisms (nsSNPs) are single nucleotide substitution occurring in the coding region of a gene and leads to a change in amino-acid sequence of protein. The studies have shown these variations may be associated with disease. Thus, investigating the effects of nsSNPs on protein functi…
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.
Study identifies biomarkers for lung cancer in female non-smokers.