PSXII-1 High-throughput Phenotyping of Rumen Microbial Contents Using Buccal Swabs

Abstract The rumen microbial community is the agent that allows cattle and other ruminants to process complex plant polymers into digestible fatty acids. Traditional methods to sample rumen microbes often involve labor-intensive stomach tubing, or invasive surgeries to access the rumen lumen via cannula ports, thereby limiting the number of animals that could be sampled in a specific study. In this study, we tested the viability of using buccal swabs as a proxy of the rumen microbial contents in a timecourse experiment on eight cannulated cows. Rumen contents and buccal swabs were collected at six equally spaced timepoints, with the first timepoint being 2 hours prior to feeding. Simpson diversity and Shannon evenness estimates of the microbial counts of each sample revealed that the first timepoint had the lowest diversity and highest evenness (Tukey HSD < 0.05) out of all other timepoints. Principal component analysis confirmed that the buccal swab samples from the first timepoint were the most similar to paired rumen samples taken at the same times. Using a Random Forest Classifier analysis, we estimated the Gini importance scores for individual microbial taxa as a proxy of their uniqueness to the rumen or oral environments of the cows. We identified 18 oral-only microbial taxa that are contaminants and could be removed from future comparisons using this method. Finally, we attempted to estimate the exact relative abundance of rumen microbial taxa from buccal swab samples using paired rumen-swab data in a Random Forest Regression model. The model was found to have moderate (~38%) accuracy in cross-validation studies. Our data suggests that buccal swabs can serve as fast and suitable proxies for rumen microbial contents of dairy cattle, but that additional factors must be measured to improve direct regression of results to those of the rumen.

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Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

Animal Production Science ◽

10.1071/an17240 ◽

2019 ◽

Vol 59 (6) ◽

pp. 1190 ◽

Cited By ~ 2

Author(s):

A. Bahri ◽

S. Nawar ◽

H. Selmi ◽

M. Amraoui ◽

H. Rouissi ◽

...

Keyword(s):

Principal Component Analysis ◽

Infrared Spectroscopy ◽

Random Forest ◽

Near Infrared Spectroscopy ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Principal Component ◽

Least Squares Regression ◽

Random Forest Regression ◽

Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

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IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier

BMC Bioinformatics ◽

10.1186/s12859-021-04104-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Rong Zhu ◽

Yong Wang ◽

Jin-Xing Liu ◽

Ling-Yun Dai

Keyword(s):

Principal Component Analysis ◽

Random Forest ◽

Cross Validation ◽

Search Algorithm ◽

Principal Component ◽

Component Analysis ◽

Biological Data ◽

Learning Technology ◽

Disease Associations ◽

Fold Cross Validation

Abstract Background Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential. Results Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611. Conclusions We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.

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Validating the Use of Bovine Buccal Sampling as a Proxy for the Rumen Microbiota by Using a Time Course and Random Forest Classification Approach

Applied and Environmental Microbiology ◽

10.1128/aem.00861-20 ◽

2020 ◽

Vol 86 (17) ◽

Author(s):

Juliana Young ◽

Joseph H. Skarlupka ◽

Madison S. Cox ◽

Rafael Tassinari Resende ◽

Amelie Fischer ◽

...

Keyword(s):

Random Forest ◽

Large Scale ◽

Time Course ◽

Optimal Time ◽

Buccal Swab ◽

Noninvasive Methods ◽

Representative Sampling ◽

Rumen Microbiota ◽

Rumen Microbes ◽

Development And Validation

ABSTRACT Analysis of the cow microbiome, as well as host genetic influences on the establishment and colonization of the rumen microbiota, is critical for development of strategies to manipulate ruminal function toward more efficient and environmentally friendly milk production. To this end, the development and validation of noninvasive methods to sample the rumen microbiota at a large scale are required. In this study, we further optimized the analysis of buccal swab samples as a proxy for direct bacterial samples of the rumen of dairy cows. To identify an optimal time for sampling, we collected buccal swab and rumen samples at six different time points relative to animal feeding. We then evaluated several biases in these samples using a machine learning classifier (random forest) to select taxa that discriminate between buccal swab and rumen samples. Differences in the inverse Simpson’s diversity, Shannon’s evenness, and Bray-Curtis dissimilarities between methods were significantly less apparent when sampling was performed prior to morning feeding (P < 0.05), suggesting that this time point was optimal for representative sampling. In addition, the random forest classifier was able to accurately identify nonrumen taxa, including 10 oral and putative feed-associated taxa. Two highly prevalent (>60%) taxa in buccal and rumen samples had significant variance in relative abundances between sampling methods but could be qualitatively assessed via regular buccal swab sampling. This work not only provides new insights into the oral community of ruminants but also further validates and refines buccal swabbing as a method to assess the rumen bacterial in large herds. IMPORTANCE The gastrointestinal tracts of ruminants harbor a diverse microbial community that coevolved symbiotically with the host, influencing its nutrition, health, and performance. While the influence of environmental factors on rumen microbes is well documented, the process by which host genetics influences the establishment and colonization of the rumen microbiota still needs to be elucidated. This knowledge gap is due largely to our inability to easily sample the rumen microbiota. There are three common methods for rumen sampling but all of them present at least one disadvantage, including animal welfare, sample quality, labor, and scalability. The development and validation of noninvasive methods, such as buccal swabbing, for large-scale rumen sampling is needed to support studies that require large sample sizes to generate reliable results. The validation of buccal swabbing will also support the development of molecular tools for the early diagnosis of metabolic disorders associated with microbial changes in large herds.

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Remote Sensing of Lake Sediment Core Particle Size Using Hyperspectral Image Analysis

Remote Sensing ◽

10.3390/rs12233850 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3850

Author(s):

Hamid Ghanbari ◽

Olivier Jacques ◽

Marc-Élie Adaïmé ◽

Irene Gregory-Eaves ◽

Dermot Antoniades

Keyword(s):

Particle Size ◽

Random Forest ◽

Lake Sediment ◽

Hyperspectral Image ◽

Sediment Cores ◽

Model Fitting ◽

Principal Component Regression ◽

Principal Component ◽

Support Vector ◽

Random Forest Regression

Hyperspectral imaging has recently emerged in the geosciences as a technology that provides rapid, accurate, and high-resolution information from lake sediment cores. Here we introduce a new methodology to infer particle size distribution, an insightful proxy that tracks past changes in aquatic ecosystems and their catchments, from laboratory hyperspectral images of lake sediment cores. The proposed methodology includes data preparation, spectral preprocessing and transformation, variable selection, and model fitting. We evaluated random forest regression and other commonly used statistical methods to find the best model for particle size determination. We tested the performance of combinations of spectral transformation techniques, including absorbance, continuum removal, and first and second derivatives of the reflectance and absorbance, along with different regression models including partial least squares, multiple linear regression, principal component regression, and support vector regression, and evaluated the resulting root mean square error (RMSE), R-squared, and mean relative error (MRE). Our results show that a random forest regression model built on spectra absorbance significantly outperforms all other models. The new workflow demonstrated herein represents a much-improved method for generating inferences from hyperspectral imagery, which opens many new opportunities for advancing the study of sediment archives.

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A novel hybrid approach utilizing principal component regression and random forest regression to bridge the period of GPS outages

Neurocomputing ◽

10.1016/j.neucom.2015.03.080 ◽

2015 ◽

Vol 166 ◽

pp. 185-192 ◽

Cited By ~ 19

Author(s):

Srujana Adusumilli ◽

Deepak Bhatt ◽

Hong Wang ◽

Vijay Devabhaktuni ◽

Prabir Bhattacharya

Keyword(s):

Random Forest ◽

Hybrid Approach ◽

Principal Component Regression ◽

Principal Component ◽

Random Forest Regression

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Deteksi Penyakit Kanker Payudara dengan Seleksi Fitur berbasis Principal Component Analysis dan Random Forest

Jurnal Infortech ◽

10.31294/infortech.v2i1.8079 ◽

2020 ◽

Vol 2 (1) ◽

pp. 96-101

Author(s):

Ahmad Fauzi ◽

Riki Supriyadi ◽

Nurlaelatul Maulidah

Keyword(s):

Breast Cancer ◽

Principal Component Analysis ◽

Random Forest ◽

Cross Validation ◽

Principal Component ◽

Component Analysis ◽

Data Set ◽

Fold Cross Validation

Abstrak - Skrining merupakan upaya deteksi dini untuk mengidentifikasi penyakit atau kelainan yang secara klinis belum jelas dengan menggunakan tes, pemeriksaan atau prosedur tertentu. Upaya ini dapat digunakan secara cepat untuk membedakan orang - orang yang kelihatannya sehat tetapi sesungguhnya menderita suatu kelainan.Tujuan utama penelitian ini adalah untuk meningkatkan peforma klasifikasi pada diagnosis kanker payudara dengan menerapkan seleksi fitur pada beberapa algoritme klasifikasi. Penelitian ini menggunakan database kanker payudara Breast Cancer Coimbra Data Set . Metode seleksi fitur berbasis pricipal component analysis akan dipasangkan dengan beberapa algoritme klasifikasi dan metode, seperti Logitboost,Bagging,dan Random Forest. Penelitian ini menggunakan 10 fold cross validation sebagai metode evaluasi. Hasil penelitian menunjukkan metode seleksi fitur berbasis pricipal component analysis mengalami peningkatan peforma klasifikasi secara signifikan setelah dipasangkan dengan seleksi fitur Random Forest dan logitboost, Random forest menunjukan peforma terbaik dengan akurasi 79.3103% dengan nilai AUC sebesar 0,843. Kata Kunci: Seleksi Fitur,PCA, Kanker Payudara,Skrining,Random Forest

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Studi Komparasi Algoritma Klasifikasi Mental Workload Berdasarkan Sinyal EEG

Jurnal Sistem Cerdas ◽

10.37396/jsc.v3i2.69 ◽

2020 ◽

Vol 3 (2) ◽

pp. 133-143

Author(s):

Dessy Kusumaningrum ◽

Elly Matul Imah

Keyword(s):

Random Forest ◽

Cross Validation ◽

Nearest Neighbor ◽

Mental Workload ◽

Principal Component ◽

Support Vector ◽

Multi Layer Perceptron ◽

K Nearest Neighbor ◽

Electroencephalogram Eeg ◽

Fold Cross Validation

Kondisi psikologis dan fisik manusia dapat memengaruhi proses berpikir. Apabila kondisi individu mengalami kelelahan, maka dapat memengaruhi penurunan tingkat produktivitas maupun penurunan proses berpikir yang menyebabkan timbulnya mental workload. Workload yang dimiliki harus seimbang terhadap kemampuan dan keterbatasan yang dimiliki. Mental workload yang berlebih berdampak buruk bagi individu karena menimbulkan penurunan produktivitas kerja. Perangkat khusus yang dapat digunakan untuk mengetahui tingkat mental workload seorang individu adalah Electroencephalogram (EEG). EEG adalah perangkat khusus yang digunakan untuk mengukur sinyal potensi listrik dari otak. Dataset yang digunakan dalam penelitian ini adalah STEW: Simultaneous Task EEG Dataset dengan 45 subjek. Dalam penelitian ini, telah dilakukan studi komparasi algoritma Random Forest, K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), dan Support Vector Machine (SVM) untuk klasifikasi mental workload berdasarkan sinyal EEG. Studi dilakukan untuk menentukan algoritma terbaik dalam klasifikasi dilihat dari segi nilai akurasi dan penggunaan memori saat proses klasifikasi. Dataset telah melalui beberapa tahapan, diantaranya pra-pemrosesan data, ekstraksi fitur, dan proses klasifikasi. Pra-pemrosesan data menerapkan pembagian data menjadi beberapa chunk. Untuk mendapatkan ciri dalam ekstraksi fitur, diterapkan metode Principal Component Analysis (PCA). Pada proses klasifikasi menggunakan pendekatan k-fold cross validation. Hasil studi penelitian ini adalah algoritma terbaik dari sisi akurasi adalah algoritma KNN, algoritma terbaik dari sisi waktu pembuatan model adalah algoritma Random Forest, serta algoritma terbaik dari sisi penggunaan memori adalah algoritma MLP.

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Validating the use of bovine buccal sampling as a proxy for the rumen microbiota using a time course and random forest classification approach

10.1101/2020.04.10.036665 ◽

2020 ◽

Author(s):

Juliana Young ◽

Joseph H. Skarlupka ◽

Rafael Tassinari Resende ◽

Amelie Fischer ◽

Kenneth F. Kalscheur ◽

...

Keyword(s):

Random Forest ◽

Large Scale ◽

Time Course ◽

Optimal Time ◽

Buccal Swab ◽

Noninvasive Methods ◽

Representative Sampling ◽

Rumen Microbiota ◽

Rumen Microbes ◽

Development And Validation

ABSTRACTAnalysis of the cow microbiome, as well as host genetic influences on the establishment and colonization of the rumen microbiota, is critical for development of strategies to manipulate ruminal function toward more efficient and environmentally friendly milk production. To this end, the development and validation of noninvasive methods to sample the rumen microbiota at a large-scale is required. Here, we further optimized the analysis of buccal swab samples as a proxy for direct microbial samples of the rumen of dairy cows. To identify an optimal time for sampling, we collected buccal swab and rumen samples at six different time points relative to animal feeding. We then evaluated several biases in these samples using a machine learning classifier (random forest) to select taxa that discriminate between buccal swab and rumen samples. Differences in the Simpson’s diversity, Shannon’s evenness and Bray-Curtis dissimilarities between methods were significantly less apparent when sampling was performed prior to morning feeding (P<0.05), suggesting that this time point was optimal for representative sampling. In addition, the random forest classifier was able to accurately identify non-rumen taxa, including 10 oral and feed-associated taxa. Two highly prevalent (> 60%) taxa in buccal and rumen samples had significant variance in absolute abundance between sampling methods, but could be qualitatively assessed via regular buccal swab sampling. This work not only provides new insights into the oral community of ruminants, but further validates and refines buccal swabbing as a method to assess the rumen microbiota in large herds.IMPORTANCEThe gastrointestinal tract of ruminants harbors a diverse microbial community that coevolved symbiotically with the host, influencing its nutrition, health and performance. While the influence of environmental factors on rumen microbes is well-documented, the process by which host genetics influences the establishment and colonization of the rumen microbiota still needs to be elucidated. This knowledge gap is due largely to our inability to easily sample the rumen microbiota. There are three common methods for rumen sampling but all of them present at least one disadvantage, including animal welfare, sample quality, labor, and scalability. The development and validation of non-invasive methods, such as buccal swabbing, for large-scale rumen sampling is needed to support studies that require large sample sizes to generate reliable results. The validation of buccal swabbing will also support the development of molecular tools for the early diagnosis of metabolic disorders associated with microbial changes in large herds.

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Klasifikasi Sinyal Emg Pada Otot Tungkai Selama Berjalan Menggunakan Random Forest

Jurnal Inotera ◽

10.31572/inotera.vol1.iss1.2016.id7 ◽

2017 ◽

Vol 1 (1) ◽

pp. 51

Author(s):

Darma Setiawan Putra ◽

Adhi Dharma Wibawa ◽

Mauridhi Hery Purnomo

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Random Forest ◽

Gait Cycle ◽

Cross Validation ◽

Vastus Lateralis ◽

Principal Component ◽

Rectus Femoris ◽

Component Analysis ◽

Gastrocnemius Lateralis

Sinyal electromyography (EMG) merupakan suatu sinyal elektrik yang terdapat dalam lapisan otot selama gerakan aktif. Cara orang berjalan ditentukan oleh struktur otot dan tulang sehingga cara berjalan ini adalah unik dan dapat digunakan sebagai data biometrik. Pada penelitian ini, kami mengklasifikasi data EMG dari delapan jenis otot tungkai selama percobaan berjalan normal: Rectus Femoris, Vastus Lateralis, Vastus Medialis, Bicep Femoris, Semitendinosus, Gastrocnemius Lateralis, Gastrocnemius Medialis, dan Tibialis Anterior. Enam orang subyek diminta untuk berjalan di laboratorium GaitLab dengan 8 buah elektroda EMG ditempel pada otot mereka. Subyek diminta untuk berjalan sebanyak 1 gait cycle dengan 3 kali pengambilan data. Total dataset EMG untuk klasifikasi adalah sebanyak 18 buah. Metode graph feature extraction dan principal component analysis digunakan untuk ekstraksi fitur data EMG. Metode Random Forest digunakan untuk mengklasifikasi data EMG berdasarkan subyek. Metode pelatihan dan pengujian data EMG menggunakan cross validation (CV). Akurasi klasifikasi yang dihasilkan dengan menggunakan metode graph feature extraction adalah sebesar 88.88% dan metode principal component analysis adalah sebesar 72.22%. Hasil ini menunjukkan bahwa data EMG ketika berjalan dari 8 jenis otot tungkai dapat digunakan untuk identitas biometrik gaya berjalan (gait).

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Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽

10.3390/en14071809 ◽

2021 ◽

Vol 14 (7) ◽

pp. 1809

Author(s):

Mohammed El Amine Senoussaoui ◽

Mostefa Brahami ◽

Issouf Fofana

Keyword(s):

Machine Learning ◽

Random Forest ◽

Oil Quality ◽

Principal Component ◽

Condition Assessment ◽

Classification Performance ◽

Transformer Oil ◽

Classification Model ◽

Insulation Degradation ◽

Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

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