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Keep up with the latest news from the NCI Center for Biomedical Informatics and Information Technology (CBIIT) and the data science communities.

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Publications Highlight the Power of Deep Learning for Cancer Drug Response Prediction

Explore these two articles, both published by members of the NCI and Department of Energy Collaboration!

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NCI and DOE Support Study for Creating More Efficient Simulations of Cancer Protein Interactions

Researchers create a theoretical model to enable simulations based on Dynamic Density Functional Theory. This new model can help them understand the behavior of cancer-causing proteins better.

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NCI-Funded Challenge Advances Artificial Intelligence for Cancer Detection

Discover how the algorithms produced in this challenge performed in detecting breast cancer.

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Give Input on the Plan to Enhance Access to Data and Other Results of NIH-Supported Research

NIH wants to hear from you about the NIH Public Access Plan, which shows how to accelerate access to federally supported scientific data and research.

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APOLLO Study Focuses on Data Methods to Predict Lung Cancer Prognosis and Treatment

Data analysis of the DNA, RNA, protein, and phosphoprotein in lung adenocarcinoma cells connected molecular features of tumors with patient survivability. This study allowed researchers to better predict prognosis and treatment in lung cancer patients.

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NCI Multi-omic Data Helps Identify Resistance to Chemotherapy in Patients with Breast Cancer

Discover how researchers are using NIH/NCI genomics and proteomics data to gain insight into chemotherapy resistance in triple negative breast cancer.

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NCI-Supported Study Uses Deep Learning for Cancer Prognosis

An NCI training grant and resources such as the NCI Cancer Research Data Commons’ Genomic Data Commons, in part, made it possible for this study to use multimodal deep learning. This model allowed researchers to examine pathology whole slide images and molecular profile data from 14 cancer types to enable more accurate patient outcome predictions.

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Deadline Extended: Submit Abstracts for the Eighth Computational Approaches for Cancer Workshop by August 16

As the necessity for and availability of large data sets in cancer applications grows, so do the challenges when conducting research and clinical applications with computational solutions. Share your experience with how to address such challenges.

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Keeping Big Data Fraud at Bay With Machine Learning

NCI’s Office of Cancer Clinical Proteomics Research’s new blog highlights recent findings from scientists in the Clinical Proteomic Tumor Analysis Consortium. It describes a proof-of-concept approach to identifying fraudulent data in biological data sets.

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Machine Learning Modeling Generates Accurate Insights into RAS-membrane Biology

In RAS-related diseases, such as cancer, mutations in the RAS genes or their regulators render RAS proteins persistently active. Investigating RAS activation events is challenging when using conventional techniques. An unprecedented multiscale platform is using machine learning to change that.

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