AIMC Topic: Principal Component Analysis

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Dimensionality reduction of genetic data using contrastive learning.

Genetics
We introduce a framework for using contrastive learning for dimensionality reduction on genetic datasets to create principal component analysis (PCA)-like population visualizations. Contrastive learning is a self-supervised deep learning method that ...

Predicting purification process fit of monoclonal antibodies using machine learning.

mAbs
In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation. However, the work required for upstream protein expression and downstream purifi...

Novel reliable model by integrating the discrete wavelet transform with fuzzy intelligent systems for the simultaneous spectrophotometric determination of anticancer drug and anti-acquired resistance drug in biological samples.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Simultaneous measurement of drugs used to treat cancer and medications prescribed to overcome resistance to these drugs is important in pharmaceutical formulations and biological samples. In this study, a spectrophotometric method with a hybrid of di...

Integrated analysis of restraint stress in rat serum using ATR-FTIR and Raman spectroscopy with Machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In forensic practice, accurately determining whether an individual has been subjected to prolonged restraint or assessing injuries resulting from restraint can be challenging. To address this, we explored a novel approach using attenuated total refle...

Machine Learning in Rugby Union: Predicting and Identifying Key Performance Indicators for Professional Rugby Union Players in Match Play Based Workload.

European journal of sport science
Rugby union is an intermittent high-intensity contact sport requiring the analysis of various training and match metrics. Time-motion analysis and video analysis have enhanced the understanding of the interplay between these two factors. However, lim...

Lyophilized nasal swabs for COVID-19 detection by ATR-FTIR spectroscopy: Machine learning-based approach.

Biophysical chemistry
The COVID-19 pandemic continues to pose challenges for global health. The disease burden and diagnostic pressure has forced scientists to explore alternate diagnostic tools beyond the standard PCR testing. One such promising tool is the use of spectr...

Univariate versus multivariate approaches for resolving the overlapped spectra of azelastine hydrochloride and mometasone furoate.

Analytical biochemistry
Azelastine hydrochloride (AZE) and Mometasone furoate (MOM) combination is used to treat allergic rhinitis' symptoms. The aim of this work is to qualitatively and quantitatively analyze both medications using univariate and multivariate spectrophotom...

A Novel NICU Sleep State Stratification: Multiperspective Features, Adaptive Feature Selection and Ensemble Model.

IEEE transactions on bio-medical engineering
The examination of sleep patterns in newborns, particularly premature infants, is crucial for understanding neonatal development. This study presents an automated multi-sleep state classification approach for infants in neonatal intensive care units ...

Demystifying food flavor: Flavor data interpretation through machine learning.

Food chemistry
Flavor data obtained from analytical techniques are vast and complex, which increases the difficulty of multi-factorial analysis. This study aims to provide a machine learning (ML)-based framework to interpret flavor data, exploiting four widely used...