AIMC Topic: Machine Learning

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A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy.

Nature communications
Quantifying protein turnover is fundamental to understanding cellular processes and advancing drug discovery. Multiplex-DIA mass spectrometry (MS), combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC) reliably measures protein turnover and d...

Integrated plasma and vegetation proteomic characterization of infective endocarditis for early diagnosis and treatment.

Nature communications
Infective endocarditis, a life-threatening condition, poses challenges for early diagnosis and personalized treatment due to insufficient biomarkers and limited understanding of its pathophysiology. Here, we performed proteomic profiling of plasma an...

Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM in China.

Frontiers in public health
OBJECTIVES: This study investigated association between long-term PM exposure and lung cancer incidence, focusing on Jiangsu Province, China. We aimed to explore the effects of historical PM with time lags and build a prediction model using machine l...

EODA: A three-stage efficient outlier detection approach using Boruta-RF feature selection and enhanced KNN-based clustering algorithm.

PloS one
Outlier detection is essential for identifying unusual patterns or observations that significantly deviate from the normal behavior of a dataset. With the rapid growth of data science, the prevalence of anomalies and outliers has increased, which can...

Towards robust medical machine olfaction: Debiasing GC-MS data enhances prostate cancer diagnosis from urine volatiles.

PloS one
Prostate cancer (PCa) is a major, and increasingly global, health concern with current screening and diagnostic tools' severe limitations causing unnecessary, invasive biopsy procedures. While gas chromatography-mass spectrometry (GC-MS) has been use...

In Silico Screening of Small Molecule Inhibitors for Amyloid-β Aggregation.

Journal of chemical information and modeling
The self-aggregation of amyloid-β (Aβ) into fibrils is a hallmark of Alzheimer's disease (AD). Inhibition of Aβ aggregation with small molecule compounds represents a promising therapeutic strategy for AD. However, designing effective ligands is chal...

Developing Pharmaceutically Relevant Pd-Catalyzed C-N Coupling Reactivity Models Leveraging High-Throughput Experimentation.

Journal of the American Chemical Society
This manuscript presents machine learning models for Pd-catalyzed C-N couplings constructed using a large, pharmaceutically relevant, structurally diverse dataset (4204 unique products) generated using high-throughput experimentation. The dataset ge...

PRP: pathogenic risk prediction for rare nonsynonymous single nucleotide variants.

Human genetics
Reliable prediction of pathogenic variants plays a crucial role in personalized medicine, which aims to provide accurate diagnosis and individualized treatment using genomic medicine. This study introduces PRP, a pathogenic risk prediction for rare n...

Spatiotemporal patterns and climate-induced macroeconomic burden of malaria in sub-Saharan Africa.

BMC public health
BACKGROUND: The global malaria burden is characterized by economic, geographical, and climatic disparities, especially in sub-Saharan Africa (SSA). Moreover, meteorological factors have become increasingly important to understand the malaria burden i...

Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke.

Scientific reports
Ischemic stroke (IS) is a multifactorial disease caused by the interaction of a variety of environmental and genetic factors, which can lead to severe disability and heavy social burden. This study aimed to find potential biomarkers related to T cell...