Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.
- Keywords
- compositionality, data preprocessing, human microbiome, machine learning, metagenomics data, normalization,
- Publication type
- Journal Article MeSH
- Review MeSH
The paper is focused on an examination of the use of entropy in the field of web usage mining. Entropy creates an alternative possibility of determining the ratio of auxiliary pages in the session identification using the Reference Length method. The experiment was conducted on two different web portals. The first log file was obtained from a course of virtual learning environment web portal. The second log file was received from the web portal with anonymous access. A comparison of the results of entropy estimation of the ratio of auxiliary pages and a sitemap estimation of the ratio of auxiliary pages showed that in the case of sitemap abundance, entropy could be a full-valued substitution for the estimate of the ratio of auxiliary pages.
- Keywords
- Reference Length, data preprocessing, information entropy, session identification, web usage mining,
- Publication type
- Journal Article MeSH
The Vehicular Reference Misbehavior Dataset (VeReMi) is a vital resource for advancing Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV). However, its large size (∼7 GB) and inherent class imbalance pose significant challenges for machine learning model development. This paper presents a preprocessing framework to enhance VeReMi's usability and relevance. Through 10 % down-sampling, the dataset was reduced to ∼724MB, making it computationally manageable. Biases were addressed by balancing benign and malicious samples through synthesis and identifying benign instances using predefined criteria. A refined feature set, including key attributes like rcvTime, pos_0, pos_1, and attack_type (renamed attacker_type), was selected to improve machine learning compatibility. This preprocessing pipeline effectively maintains data integrity and preserves the representativeness of malicious patterns. The optimized dataset is well-suited for ITS and IoV applications, such as anomaly detection and network security, underscoring the crucial role of preprocessing in overcoming real-world constraints and enhancing model performance.
- Keywords
- Anomaly detection, Cybersecurity, Data preprocessing, Dataset optimization, Intelligent transportation systems (ITS), Internet of vehicles (IoV), Intrusion detection systems (IDS), Machine learning (ML), Network security, Vehicular reference misbehavior dataset (VeReMi),
- Publication type
- Journal Article MeSH
An introductory review of hardware aspects of on-line experimental data processing reveals that the combination of a specialized (hard-wired) preprocessing unit coupled with a programmable laboratory computer is an optimal set up for an electrophysiological laboratory. The paper deals with a proposed modular system, which makes the assembly of a large number of different preprocessing units possible. Some practical applications of the preprocessing units coupled with a LINC (D.E.C.) computer are presented in conclusion.
- MeSH
- Electrophysiology * MeSH
- Computers, Hybrid * MeSH
- Rats MeSH
- Neurons physiology MeSH
- Reticular Formation physiology MeSH
- Amplifiers, Electronic MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
MOTIVATION: Meticulous selection of chromatographic peak detection parameters and algorithms is a crucial step in preprocessing liquid chromatography-mass spectrometry (LC-MS) data. However, as mass-to-charge ratio and retention time shifts are larger between batches than within batches, finding apt parameters for all samples of a large-scale multi-batch experiment with the aim of minimizing information loss becomes a challenging task. Preprocessing independent batches individually can curtail said problems but requires a method for aligning and combining them for further downstream analysis. RESULTS: We present two methods for aligning and combining individually preprocessed batches in multi-batch LC-MS experiments. Our developed methods were tested on six sets of simulated and six sets of real datasets. Furthermore, by estimating the probabilities of peak insertion, deletion and swap between batches in authentic datasets, we demonstrate that retention order swaps are not rare in untargeted LC-MS data. AVAILABILITY AND IMPLEMENTATION: kmersAlignment and rtcorrectedAlignment algorithms are made available as an R package with raw data at https://metabocombiner.img.cas.cz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Colorectal cancer remains a major health burden, and its early detection is crucial for effective treatment. This study investigates the use of a handheld Raman spectrometer in combination with machine learning to classify colorectal tissue samples collected during colonoscopy. A dataset of 330 spectra from 155 participants was preprocessed using a standardized pipeline, and multiple classification models were trained to distinguish between healthy and pathological tissue. Due to the strong class imbalance and limited data size, a custom grid search approach was implemented to optimize both model hyperparameters and preprocessing parameters. Unlike standard GridSearchCV, our method prioritized balanced accuracy on the test set to reduce bias toward the dominant class. Among the tested classifiers, the Decision Tree (DT) and Support Vector Classifier (SVC) achieved the highest balanced accuracy (71.77% for DT and 70.77% for SVC), outperforming models trained using traditional methods. These results demonstrate the potential of Raman spectroscopy as a rapid, non-destructive screening tool and highlight the importance of tailored model selection strategies in biomedical applications. While this study is based on a limited dataset, it serves as a promising step toward more robust classification models and supports the feasibility of this approach for future clinical validation.
- Keywords
- Balanced accuracy, Colorectal cancer, Machine learning, Preprocessing pipeline, Raman spectroscopy, Spectral classification,
- Publication type
- Journal Article MeSH
BACKGROUND: Magnetic resonance spectroscopy provides metabolic information about living tissues in a non-invasive way. However, there are only few multi-centre clinical studies, mostly performed on a single scanner model or data format, as there is no flexible way of documenting and exchanging processed magnetic resonance spectroscopy data in digital format. This is because the DICOM standard for spectroscopy deals with unprocessed data. This paper proposes a plugin tool developed for jMRUI, namely jMRUI2XML, to tackle the latter limitation. jMRUI is a software tool for magnetic resonance spectroscopy data processing that is widely used in the magnetic resonance spectroscopy community and has evolved into a plugin platform allowing for implementation of novel features. RESULTS: jMRUI2XML is a Java solution that facilitates common preprocessing of magnetic resonance spectroscopy data across multiple scanners. Its main characteristics are: 1) it automates magnetic resonance spectroscopy preprocessing, and 2) it can be a platform for outputting exchangeable magnetic resonance spectroscopy data. The plugin works with any kind of data that can be opened by jMRUI and outputs in extensible markup language format. Data processing templates can be generated and saved for later use. The output format opens the way for easy data sharing- due to the documentation of the preprocessing parameters and the intrinsic anonymization--for example for performing pattern recognition analysis on multicentre/multi-manufacturer magnetic resonance spectroscopy data. CONCLUSIONS: jMRUI2XML provides a self-contained and self-descriptive format accounting for the most relevant information needed for exchanging magnetic resonance spectroscopy data in digital form, as well as for automating its processing. This allows for tracking the procedures the data has undergone, which makes the proposed tool especially useful when performing pattern recognition analysis. Moreover, this work constitutes a first proposal for a minimum amount of information that should accompany any magnetic resonance processed spectrum, towards the goal of achieving better transferability of magnetic resonance spectroscopy studies.
- MeSH
- Algorithms * MeSH
- Electronic Data Processing statistics & numerical data MeSH
- Humans MeSH
- Magnetic Resonance Spectroscopy methods MeSH
- Magnetic Resonance Imaging methods MeSH
- Image Processing, Computer-Assisted methods MeSH
- Software * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Functional connectivity analysis of resting-state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix is a useful approximate representation of the brain's connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, Typicality of Functional Connectivity, to capture deviations from standard brain functional connectivity patterns. In a resting-state fMRI dataset of 245 healthy subjects, this measure was significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity, preprocessing options, and other datasets, including 1,081 subjects from the Human Connectome Project. In principle, Typicality of Functional Connectivity should be sensitive also to other types of artifacts, processing errors, and possibly also brain pathology, allowing extensive use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.
- Keywords
- atlas, functional connectivity, motion, quality, rs-fMRI,
- MeSH
- Artifacts MeSH
- Atlases as Topic * MeSH
- Datasets as Topic * MeSH
- Adult MeSH
- Head Movements MeSH
- Connectome * methods standards MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods standards MeSH
- Young Adult MeSH
- Brain diagnostic imaging physiology MeSH
- Image Processing, Computer-Assisted * methods standards MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- MeSH
- Behavior, Animal * MeSH
- Computers * MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
BACKGROUND AND OBJECTIVE: Severe courses of COVID-19 disease can lead to long-term complications. The post-acute phase of COVID-19 refers to the persistent or new symptoms. This problem is becoming more relevant with the increasing number of patients who have contracted COVID-19 and the emergence of new virus variants. In this case, preventive treatment with corticosteroids can be applied. However, not everyone benefits from the treatment, moreover, it can have severe side effects. Currently, no study would analyze who benefits from the treatment. METHODS: This work introduces a novel approach to the recommendation of Corticosteroid (CS) treatment for patients in the post-acute phase. We have used a novel combination of clinical data, including blood tests, spirometry, and X-ray images from 273 patients. These are very challenging to collect, especially from patients in the post-acute phase of COVID-19. To our knowledge, no similar dataset exists in the literature. Moreover, we have proposed a unique methodology that combines machine learning and deep learning models based on Vision Transformer (ViT) and InceptionNet, preprocessing techniques, and pretraining strategies to deal with the specific characteristics of our data. RESULTS: The experiments have proved that combining clinical data with CXR images achieves 8% higher accuracy than independent analysis of CXR images. The proposed method reached 80.0% accuracy (78.7% balanced accuracy) and a ROC-AUC of 0.89. CONCLUSIONS: The introduced system for CS treatment prediction using our neural network and learning algorithm is unique in this field of research. Here, we have shown the efficiency of using mixed data and proved it on real-world data. The paper also introduces the factors that could be used to predict long-term complications. Additionally, this system was deployed to the hospital environment as a recommendation tool, which admits the clinical application of the proposed methodology.
- Keywords
- Chest X-ray images, Clinical data, Image classification, Post-acute COVID-19, Treatment prediction, Vision transformer,
- Publication type
- Journal Article MeSH
