Preventive Care

Artificial intelligence for breast cancer screening: Opportunity or hype?

Interpretation of mammography for breast cancer (BC) screening can confer a mortality benefit through early BC detection, can miss a cancer that is present or fast growing, or can result in false-positives. Efforts to improve screening outcomes have mostly focused on intensifying imaging practices (double instead of single-reading, more frequent screens, or supplemental imaging) that may add subst...

A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening.

Colorectal cancer (CRC) a leading cause of death by cancer, and screening programs for its early identification are at the heart of the increasing survival rates. To motivate population participation, non-invasive, accurate, scalable and cost-effective diagnosis methods are required. Blood fluorescence spectroscopy provides rich information that can be used for cancer identification. The main chal...

Predicting Ascospore Release of Monilinia vaccinii-corymbosi of Blueberry with Machine Learning.

Mummy berry, caused by Monilinia vaccinii-corymbosi, causes economic losses of highbush blueberry in the U.S. Pacific Northwest (PNW). Apothecia devel...

Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning.

We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to...

Identifying novel factor XIIa inhibitors with PCA-GA-SVM developed vHTS models.

There currently is renewed interest in blood clotting Factor XII as a potential target for thrombosis inhibition. Historically untargeted, there is li...

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data.

BACKGROUND: Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy r...

Identification of immune correlates of protection in Shigella infection by application of machine learning.

BACKGROUND: Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist ...

Leveraging machine learning-based approaches to assess human papillomavirus vaccination sentiment trends with Twitter data.

BACKGROUND: As one of the serious public health issues, vaccination refusal has been attracting more and more attention, especially for newly approved...

and cell-based analyses reveal strong divergence between prediction and observation of T-cell-recognized tumor antigen T-cell epitopes.

Tumor exomes provide comprehensive information on mutated, overexpressed genes and aberrant splicing, which can be exploited for personalized cancer i...

DeepPap: Deep Convolutional Networks for Cervical Cell Classification.

Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, wh...

Provider-specific quality measurement for ERCP using natural language processing.

BACKGROUND AND AIMS: Natural language processing (NLP) is an information retrieval technique that has been shown to accurately identify quality measur...

Towards automatic pulmonary nodule management in lung cancer screening with deep learning.

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will ...

The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening.

The machine learning-based virtual screening of molecular databases is a commonly used approach to identify hits. However, many aspects associated wit...

Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition.

The development of new antimalarial therapies is essential, and lowering the barrier of entry for the screening and discovery of new lead compound cla...

Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

BACKGROUND: Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine r...

Screening of autism based on task-free fMRI using graph theoretical approach.

Studies on autism spectrum disorder (ASD) have indicated several dysfunctions in the structure, and functional organization of the brain. However, fin...

Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets.

BACKGROUND: Analysing public opinions on HPV vaccines on social media using machine learning based approaches will help us understand the reasons behi...

RABiT-II: Implementation of a High-Throughput Micronucleus Biodosimetry Assay on Commercial Biotech Robotic Systems.

We demonstrate the use of high-throughput biodosimetry platforms based on commercial high-throughput/high-content screening robotic systems. The cytok...

Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer.

Individuals with colorectal cancer (CRC) have a tendency to intestinal bleeding which may result in mild to severe iron deficiency anemia, but for man...

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial...