Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, only a few interpretable machine learning methods comply with its challen...
This study investigated the generalizability of Arabidopsis thaliana immune responses across diverse pathogens, including Botrytis cinerea, Sclerotinia sclerotiorum, and Pseudomonas syringae, using a data-driven, machine learning approach. Machine le...
Prostate-specific antigen (PSA) is the most commonly used marker of prostate cancer. However, nearly 25% of men with elevated PSA levels do not have cancer and nearly 20% of patients with prostate cancer have normal serum PSA levels. Therefore, in th...
Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, ...
Journal of clinical laboratory analysis
Nov 21, 2024
BACKGROUND: HER2-positive breast cancer (BC), a highly aggressive malignancy, has been treated with the targeted therapy inetetamab for metastatic cases. Inetetamab (Cipterbin) is a recently approved targeted therapy for HER2-positive metastatic BC, ...
IEEE transactions on bio-medical engineering
Nov 21, 2024
OBJECTIVE: Recent years have seen an increase in machine learning (ML)-based blood glucose (BG) forecasting models, with a growing emphasis on potential application to hybrid or closed-loop predictive glucose controllers. However, current approaches ...
IEEE transactions on bio-medical engineering
Nov 21, 2024
The severity of mobility deficits is one of the most critical parameters in the diagnosis of Parkinson's disease (PD) and rehabilitation. The current approach for severity evaluation is clinical scaling that relies on a clinician's subjective observa...
There exist significant challenges for lung adenocarcinoma (LUAD) due to its poor prognosis and limited treatment options, particularly in the advanced stages. It is crucial to identify genetic biomarkers for improving outcome predictions and guidin...
BACKGROUND: Pulmonary hemorrhage (PH) in respiratory distress syndrome (RDS) in extremely preterm infants exhibits a high mortality rate and poor long-term outcomes. The aim of the present study was to develop a machine learning (ML) predictive model...
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