AIMC Topic: Principal Component Analysis

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Deep structural clustering reveals hidden systematic biases in RNA sequencing data.

Genome research
RNA sequencing (RNA-seq) is a pivotal tool for transcriptomic analysis, providing comprehensive exploration of gene expression across diverse biological contexts. However, RNA-seq data are susceptible to various biases that can significantly compromi...

Formulation and validation of a regional household wealth index for sub-Saharan Africa.

PloS one
A new era in global health assistance requires a focus on efficiently using limited and declining donor funds. This shift requires better evaluation methods to allocate resources effectively. Most evaluations in low- and middle-income countries (LMIC...

Comprehensive analysis of metabolic and molecular alterations in the blood of patients with Sjögren's syndrome based on untargeted metabolomics analysis.

BMC medical genomics
BACKGROUND: Sjögren's syndrome (SS) is a chronic autoimmune disorder marked by lymphocytic infiltration of exocrine glands, leading to xerostomia, keratoconjunctivitis sicca, and systemic involvement including fatigue, arthralgia, and visceral organ ...

Principal component analysis-assisted fluorescent phenylboronic acid probes for discrimination and detection of living pathogenic bacteria.

Analytical methods : advancing methods and applications
A simple analytical platform was constructed based on a phenylboronic acid-functionalized fluorescent probe with machine learning methods. Under the optimized experimental conditions, both fluorescein isothiocyanate-aminophenylboronic acid (FITC-APBA...

Surface water quality evaluation impacting drinking water sources and sanitation using water quality index, multivariate techniques, and interpretable machine learning models in Mahanadi River, Odisha (India).

Environmental geochemistry and health
Water quality and quantity affect crop productivity, with surface water quality having a significant impact. The amount of surface water being used for drinking is gradually rising. Thus, assessing surface water quality and related hydro-chemical cha...

Ligand Dissociation Pathways from Membrane Receptors Revealed by Weighted Ensemble Simulations.

The journal of physical chemistry. B
G-protein-coupled receptors (GPCRs) are pivotal in cellular signal transduction and serve as key drug targets. Among them, the β-adrenergic receptors (βAR and βAR) regulate cardiovascular function and are activated by endogenous catecholamines, norep...

Electrochemical "Super-Fingerprinting" in Combination with Machine Learning for the On-Site Detection of Illicit Drugs.

ACS sensors
On-site multidrug sensing remains challenging due to the complexity of real samples and the differing detection requirements of individual substances. In the current study, we present successful electrochemical multidrug detection that overcomes thes...

Identifying graft incompatible rootstocks for sweet cherry through machine learning algorithms.

PloS one
Graft incompatibility is a key factor in the development of dwarf and semi dwarf rootstocks for sweet cherry (Prunus avium L.) to improve yield, fruit quality, precocity, and labor efficiency. This study evaluated the graft incompatibility of eight g...

A novel hybrid model for species distribution prediction using probabilistic random forest, principal component analysis and genetic algorithm.

PloS one
Probabilistic Random Forest is an extension of the traditional Random Forest machine learning algorithm that is one of the frequently used machine learning algorithms employed for species distribution modeling. However, with the use of complex datase...

Momentum, volume and investor sentiment study for u.s. technology sector stocks-A hidden markov model based principal component analysis.

PloS one
In this paper, we study the impact of momentum, volume and investor sentiment on U.S. tech sector stock returns using Principal Component Analysis-Hidden Markov Model (PCA-HMM) methodology. Price and volume are two well-known aspects in general equil...