Bioterrorism

Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy.

IMPORTANCE: Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-err...

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in tri...

Response to comments on: Modeling and mitigating human annotations to design processing systems with human-in-the-loop machine learning for glaucomatous defects: The future in artificial intelligence.

The prediction approach of drug-induced liver injury: response to the issues of reproducible science of artificial intelligence in real-world applications.

In the previous study, we developed the generalized drug-induced liver injury (DILI) prediction mode...

An approachable, flexible and practical machine learning workshop for biologists.

SUMMARY: The increasing prevalence and importance of machine learning in biological research have cr...

A Response to Paolo Euron's "Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots".

The emergence of sex robots raises important issues about what it means to be human and the commodif...

Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.

The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred furth...

World Health Organization's Early AI-supported Response with Social Listening Platform.

(WHO EARS). WHO HQ, Avenue Appia 20, 1211, Geneva 27, Switzerland; https://www.who-ears.com/; free.

Channel response-aware photonic neural network accelerators for high-speed inference through bandwidth-limited optics.

Photonic neural network accelerators (PNNAs) have been lately brought into the spotlight as a new cl...

Assessment of Disease Status and Treatment Response With Artificial Intelligence-Enhanced Electrocardiography in Obstructive Hypertrophic Cardiomyopathy.

AI analysis of HCM ECGs correlates with longitudinal hemodynamic, cardiac structural and laboratory ...

Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies.

SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been u...

Artificial intelligence for drug response prediction in disease models.

An overview of machine learning methods for monotherapy drug response prediction.

For an increasing number of preclinical samples, both detailed molecular profiles and their response...

Representation of molecules for drug response prediction.

The rapid development of machine learning and deep learning algorithms in the recent decade has spur...

How much can deep learning improve prediction of the responses to drugs in cancer cell lines?

The drug response prediction problem arises from personalized medicine and drug discovery. Deep neur...

Prediction of whole-cell transcriptional response with machine learning.

MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell resp...

TGSA: protein-protein association-based twin graph neural networks for drug response prediction with similarity augmentation.

MOTIVATION: Drug response prediction (DRP) plays an important role in precision medicine (e.g. for c...

Call for better systems and data to support artificial intelligence for pandemic response.

Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study.

BACKGROUND: Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personal...

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

BACKGROUND: In recent years, the availability of high throughput technologies, establishment of larg...

Response to the Comment on "Situating Artificial Intelligence in Surgery: A Focus on Disease Severity".