Oncology/Hematology

Chemotherapy

Latest AI and machine learning research in chemotherapy for healthcare professionals.

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Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks.

P-glycoprotein (P-gp) is an ATP binding cassette (ABC) transporter that helps to protect several cer...

Jan 2015 25588022
Media-fill simulation tests in manual and robotic aseptic preparation of injection solutions in syringes.

OBJECTIVE: The purpose of this study was to evaluate the contamination rate of media-fill products e...

Dec 2014 25549919
Functional outcomes after TORS for oropharyngeal cancer: a systematic review.

Summarize functional outcomes after transoral robotic surgery (TORS) ± adjuvant therapy for orophary...

Mar 2014 24643851
Outcome of transoral robotic surgery for stage I-II oropharyngeal cancer.

Traditionally T1-2N0 oropharyngeal carcinoma is treated with a single treatment modality, being eith...

Mar 2014 24609641
AI-driven phenotypic profiling of organoids

Organoids have advanced in vitro modeling by more faithfully recapitulating native tissue architectu...

CoRe-WM: Conditioned Response-State World Modeling for Longitudinal Treatment-Response Prediction in Breast DCE-MRI

Pathologic complete response (pCR) after neoadjuvant chemotherapy is a key prognostic indicator in a...

NoiTox: A Machine Learning Strategy for Peptide Toxicity Prediction Reveals Label Noise in Benchmark Datasets

Motivation: Peptide toxicity remains a major bottleneck in drug development, as small sequence varia...

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Patient, Place, Prior (P$^3$): What Counts as Personalization in Medical World Models?

Longitudinal models forecast how a patient's imaging state evolves, but accuracy does not show wheth...

Cross-cohort generalizability of machine learning models for early-stage node-negative breast cancer relapse prediction

Background: Adjuvant therapy decision in early-stage node-negative breast cancer remains one of the ...

Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies

Scarcity of labeled data limits development of deep learning biomarkers in oncology. We develop a tw...

An AI-Based Multi-Stage Approach for Androgenetic Alopecia Assessment from Low-Magnification Scalp Images

Androgenetic alopecia (AGA) is characterized by patterned follicular miniaturization, increased sing...

POME: Graph-based embeddings for partially observed mixed-type data

Partially observed mixed-type (POM) data, as often encountered in clinical, epidemiological, and phe...

AutoScreen: AI Co-Scientist System for Target Discovery in Functional Genomics

Target discovery in functional genomics remains largely manual and time-consuming, lacking systemati...

Surprisal-based large language models reveal immunologic insights in lobular breast cancer

In large data sets discovery is often limited to pre-conceived hypotheses and data fishing. Here we ...

Large Language Model Embeddings of Surgical Procedural Names for Confounding Adjustment in Perioperative Observational Studies

Background: Perioperative observational studies are increasingly used to evaluate anesthesia practic...

Microbial bioprospecting for benzoxazolinate-like molecules: unleashing the potential of genome mining

The benzoxazolinate moiety is a key functional group found in a few natural products (NPs), exhibiti...

A Multi-stage Precision Stratification (MPS) Framework for Navigating Adjuvant Immunotherapy in Hepatocellular Carcinoma After Resection

BackgroundRecurrence rates following curative resection for hepatocellular carcinoma (HCC) remain pe...

A Vision-Language Framework for Predicting Brain Tumor Recurrence from Multimodal, Longitudinal Patient Data

Accurate prediction of tumor recurrence in brain tumor patients following surgery is essential for o...

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