More than a decade ago, researchers launched the BabySeq Project, a pilot program to return newborn genomic sequencing results to parents and measure the effects on newborn care. Today, over 30 ...

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision ...

Predictive factors for the efficacy of nal-IRI + 5FU/LV therapy in patients previously treated with conventional irinotecan. This is an ASCO Meeting Abstract from the 2025 ASCO Gastrointestinal ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

Plants are constantly exposed to a wide array of biotic and abiotic stresses in their natural environments, posing ...

Read more about AI and machine learning drive digital transformation across global mining operations on Devdiscourse ...