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Exscientia, MD Anderson Collaborate on AI-Driven Oncology Drug Discovery and Development


AI-driven pharmatech company Exscientia announced today that it has commenced an oncology drug discovery and development collaboration with the University of Texas MD Anderson Cancer Center to identify novel anti-cancer, cell-intrinsic small-molecule compounds based on jointly identified therapeutic targets. The researchers at MD Anderson’s Therapeutics Discovery division will use Exscientia’s expertise in building machine learning models to help identify druggable targets and in what it terms a “data-agnostic” approach to drug design.

Machine Learning Helps Predict Response to Immunotherapy


Scientists at the Johns Hopkins Kimmel Comprehensive Cancer Center and its Bloomberg~Kimmel Institute for Cancer Immunotherapy report that they successfully trained a machine learning algorithm to predict, in hindsight, which patients with melanoma would respond to treatment and which would not respond.

Artificial intelligence may help predict cardiotoxicity in renal cell carcinoma


Artificial intelligence models can help predict cardiotoxicity risk among patients with renal cell carcinoma treated with VEGF receptor inhibitors, according to study results. Integration of artificial intelligence (AI) models into electronic medical records can help oncologists and other members of the clinical care team identify those who may benefit from cardio-oncology monitoring and treatment, findings presented at International Kidney Cancer Symposium: North America showed.

Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer


Immune checkpoint inhibitors (ICIs) are standard-of-care as first-line (1L) therapy for advanced non-small cell lung cancer (aNSCLC) without actionable oncogenic driver mutations. We applied machine learning (ML)-based survival models to a real-world cohort of patients with aNSCLC who received 1L ICI therapy extracted from a US-based electronic health record database.

How “the most advanced machine learning approach” is finding new cancer-causing mutational signatures.


This is the most advanced machine learning approach for analysing mutational signatures. We know that because we’ve extensively compared it with everything else that exists.

Machine-Learning Model Can Help Identify Ovarian Cancer Treatment Targets


A study published last month in Nature Metabolism shows that a machine learning (ML)-based computational platform can identify specific metabolic targets in ovarian cancer, which could be used in personalized treatment therapies.

A Deep Learning Tool Can Help Avoid False Positive Breast Cancer Scans


A Deep Learning Tool Can Help Avoid False Positive Breast Cancer Scans

Using a deep learning tool can help improve accuracy and reduce false positives during magnetic resonance imaging (MRI) scans to check for breast cancer, shows research led by New York University

Personalised medicine and the advantages of big data and AI-based diagnostics


Artificial intelligence (AI) and big data are transforming healthcare with high-throughput analyses of complex diseases. Machine learning and sophisticated computational methods can be used to efficiently interpret human genomes and other biomarkers, providing insights for patient treatment and with major applications in diagnostics and preventive care.


From a small village in China to MD Anderson: Genomic medicine researcher looks to the future of big data in cancer care


As an associate professor in Genomic Medicine, Linghua Wang, M.D., Ph.D., studies how normal cells become cancerous and how cancer cells develop resistance to drugs.

Wang believes the rise in data science, machine learning and artificial intelligence will advance precision and predictive oncology and accelerate drug development.

Automated detection of microsatellite status in early colon cancer by infrared imaging combined with artificial intelligence


The researchers of PRODI (Center for Protein Diagnostics, Ruhr-University Bochum, Germany) used tissue sections from the prospective multicentre AIO ColoPredictPlus 2.0 registry study to verify the novel label-free infrared (IR) imaging combined with artificial intelligence (AI) for tumour detection and classification of microsatellite status.

The 2021 landscape of FDA-approved artificial intelligence/machine learning-enabled medical devices: An analysis of the characteristics and intended use


Machine learning (ML), a type of artificial intelligence (AI) technology that uses a data-driven approach for pattern recognition, has been shown to be beneficial for many tasks across healthcare. To characterize the commercial availability of AI/ML applications in the clinic, we performed a detailed analysis of AI/ML-enabled medical devices approved/cleared by the US Food and Drug Administration (FDA) by June 2021.

AI tool allows clinicians to make optimal, personalized chemotherapy doses for patients


A team of researchers from National University of Singapore (NUS), in collaboration with clinicians from the National University Cancer Institute, Singapore (NCIS) which is part of the National University Health System (NUHS), has reported promising results in using CURATE.AI, an artificial intelligence (AI) tool that identifies and better allows clinicians to make optimal and personalized doses of chemotherapy for patients.

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