Individualized Generation of Appropriate Assistance Timings for Active Lower Limb Robots via Machine Learning

Abstract Background: Obtaining appropriate assistance timings for individual users of active lower limb assistant robots (ALLARs) is one of the major challenges that limit the practical application of robots since very small assistance timing errors greatly affect the robot's assistance effect. However, neither theoretical nor experimental methods can currently generate appropriate assistance timings due to their respective availability or accuracy limitations. Method: In this paper, we proposed a new method to generate appropriate assistance timings for individual users of ALLARs via machine learning. The method has the accuracy of theoretical methods and the availability of experimental methods. We established a database of ten static physiological parameters, three dynamic parameters, and theoretical appropriate assistance timings, and mapped the static physiological parameters and the dynamic parameters to the theoretical assistance timings using multilayer neuron networks. Fold-cross validation and determination efficient were used to test the fit of the model. The root mean square error between generated values and true values of each subject was compared to that between the mean of the sample and the true values of each subject to evaluate the data accuracy of our method. We also set ±2% error as the boundary of the practical accuracy and compared the practical accuracy when using our method to that when using the mean generally. Result: The model achieved a small standard deviation of the square root error in the 10-fold cross-validation experiment and a large determination coefficient. We reduced the data error of starting and ending assistance timing from 0.0265 and 0.0172 to 0.014±0.000429 and 0.0079±0.000875, respectively, and improved the practical accuracy of starting and ending assistance timing from 54.93% and 75.49% to 89.54% and 99.95%, respectively.Conclusion: The proposed method can generate an appropriate assistance timings for different users of ALLARs walking at different speeds. Moreover, a new reference for ending assistance timings is provided and the database can be used as a reference for futer research. The practical effect of the method will be tested in future work.

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Using Machine Learning to Classify the "Goodness" of Hmsc-Derived and AML-Derived EV's

Blood ◽

10.1182/blood-2018-99-120183 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 5244-5244

Author(s):

Chibuikem Chukwudalu Nwizu ◽

Lorin Crawford ◽

Theo Borgovan ◽

Sicheng Wen ◽

Mark S Dooner ◽

...

Keyword(s):

Machine Learning ◽

Diffusion Coefficient ◽

Research Funding ◽

Cross Validation ◽

Patient Specific ◽

Summary Statistics ◽

Patient Sample ◽

Oncology Research ◽

The Mean ◽

Fold Cross Validation

Abstract Introduction Extracellular vesicles (EVs) form a unique class of messengers for intercellular communication. Depending on their cell of origin, EVs have the ability to induce a phenotypic change in the recipient cell. For example, EVs from explant prostate cancer induce a neoplastic phenotype in normal prostate cell lines. Conversely, EVs from human mesenchymal stem cells (hMSC) reverse the malignant phenotype in prostate and colorectal cancer and mitigate radiation damage to the marrow. Characterization of EVs as "good" or "bad" has the potential to be a very important diagnostic tool in regard to direct therapy and biomarker identification. Currently, there is no way of characterizing the "goodness" of an EV sample. We leveraged advances in the area of machine learning to develop a novel therapeutic tool that can classify the goodness of an EV particle distribution in a serum sample. Methods EVs were harvested from three sources: hMSC primary progenitor cells, Kasumi Acute Myeloid Leukemia (AML) cells lines, and patient samples. Using a standard centrifugation isolation, EVs were isolate, resuspended in 1% DMSO, and frozen. All samples were analyzed using the NanoSight N500. We collected biophysical properties of the EVs such as diameter and diffusion coefficient. The results were summarized in a distribution based on either size or diffusion coefficient. Summary statistics from each distributions were calculated. Summary statistics included mean, mode, and the diameter at which 10%, 50%, and 90% of size or diffusion coefficient is comprised of smaller particles (D10, D50, and D90, respectively). These served as inputs into a softmax Multilayer Perceptron. This neural network classifier was trained on only the hMSC-derived EVs and Kasumi AML-derived EVs, which served to represent a healthy patient and leukemic patient respectively. Results/Conclusion The mean accuracy after 10 fold cross validation was 90.16% ± 9.26%. For each validation run, a Receiver Operating Characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated. The mean AUC (after 10 fold cross validation) was 95.97% ± 5.38%. We programmed the algorithm, when given a patient sample, to calculate and return similarity to the Kasumi AML-derived EVs. The algorithm was given three patient sample representing three leukemic disease processes: AML, Chronic Myelomonocytic Leukemia (CMML), and Multiple myeloma (MM). The result was a calculate % similarity of 100%, 100%, and 66% for AML, CMML, and MM respectively. These results are promising and have prompted us to begin collecting and test the algorithms on normal, active leukemic, and recovered leukemic patients. We endeavor to evolve our predictive algorithms to include disease and patient specific information, allowing us to adapt our learning models towards clinically relevant endpoints. Disclosures Reagan: Alexion: Honoraria; Pfizer: Research Funding; Takeda Oncology: Research Funding. Olszewski:Spectrum Pharmaceuticals: Consultancy, Research Funding; TG Therapeutics: Research Funding; Genentech: Research Funding.

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Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

Current Pharmaceutical Design ◽

10.2174/1381612825666191107092214 ◽

2020 ◽

Vol 25 (40) ◽

pp. 4296-4302 ◽

Cited By ~ 2

Author(s):

Yuan Zhang ◽

Zhenyan Han ◽

Qian Gao ◽

Xiaoyi Bai ◽

Chi Zhang ◽

...

Keyword(s):

Machine Learning ◽

Inclusion Bodies ◽

Cross Validation ◽

Independent Set ◽

K562 Cells ◽

Machine Learning Algorithms ◽

Learning Approaches ◽

Validation Test ◽

Excess Number ◽

Fold Cross Validation

Background: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen. Methods: In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors. Results: The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging. Conclusion: This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

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Prostate and dominant intraprostatic lesion segmentation on PET/CT using cascaded regional-net

Physics in Medicine and Biology ◽

10.1088/1361-6560/ac3c13 ◽

2021 ◽

Author(s):

Luke A Matkovic ◽

Tonghe Wang ◽

Yang Lei ◽

Oladunni O Akin-Akintayo ◽

Olayinka A Abiodun Ojo ◽

...

Keyword(s):

Radiation Therapy ◽

Cross Validation ◽

Ct Images ◽

Second Step ◽

Prostate Segmentation ◽

Positron Emission ◽

Pet Ct ◽

The Mean ◽

And Training ◽

Fold Cross Validation

Abstract Focal dose boost to dominant intraprostatic lesions (DILs) has recently been proposed for prostate radiation therapy. Accurate and fast delineation of the prostate and DILs is thus required during treatment planning. We propose a learning-based method using positron emission tomography (PET)/computed tomography (CT) images to automatically segment the prostate and its DILs. To enable end-to-end segmentation, a deep learning-based method, called cascaded regional-Net, is utilized. The first network, referred to as dual attention network (DAN), is used to segment the prostate via extracting comprehensive features from both PET and CT images. A second network, referred to as mask scoring regional convolutional neural network (MSR-CNN), is used to segment the DILs from the PET and CT within the prostate region. Scoring strategy is used to diminish the misclassification of the DILs. For DIL segmentation, the proposed cascaded regional-Net uses two steps to remove normal tissue regions, with the first step cropping images based on prostate segmentation and the second step using MSR-CNN to further locate the DILs. The binary masks of DILs and prostates of testing patients are generated from PET/CT by the trained network. To evaluate the proposed method, we retrospectively investigated 49 PET/CT datasets. On each dataset, the prostate and DILs were delineated by physicians and set as the ground truths and training targets. The proposed method was trained and evaluated using a five-fold cross-validation and a hold-out test. The mean surface distance and DSC values were 0.666±0.696mm and 0.932±0.059 for the prostate and 1.209±1.954mm and 0.757±0.241 for the DILs among all 49 patients. The proposed method has demonstrated great potential for improving the efficiency and reducing the observer variability of prostate and DIL contouring for DIL focal boost prostate radiation therapy.

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Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription

BioMed Research International ◽

10.1155/2019/6847685 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14

Author(s):

Shilun Yang ◽

Yanjia Shen ◽

Wendan Lu ◽

Yinglin Yang ◽

Haigang Wang ◽

...

Keyword(s):

Machine Learning ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Cross Validation ◽

Bayesian Models ◽

Machine Learning Algorithms ◽

Therapeutic Effects ◽

Test Set ◽

Screening Experiments ◽

Fold Cross Validation

Xiaoxuming decoction (XXMD), a classic traditional Chinese medicine (TCM) prescription, has been used as a therapeutic in the treatment of stroke in clinical practice for over 1200 years. However, the pharmacological mechanisms of XXMD have not yet been elucidated. The purpose of this study was to develop neuroprotective models for identifying neuroprotective compounds in XXMD against hypoxia-induced and H2O2-induced brain cell damage. In this study, a phenotype-based classification method was designed by machine learning to identify neuroprotective compounds and to clarify the compatibility of XXMD components. Four different single classifiers (AB, kNN, CT, and RF) and molecular fingerprint descriptors were used to construct stacked naïve Bayesian models. Among them, the RF algorithm had a better performance with an average MCC value of 0.725±0.014 and 0.774±0.042 from 5-fold cross-validation and test set, respectively. The probability values calculated by four models were then integrated into a stacked Bayesian model. In total, two optimal models, s-NB-1-LPFP6 and s-NB-2-LPFP6, were obtained. The two validated optimal models revealed Matthews correlation coefficients (MCC) of 0.968 and 0.993 for 5-fold cross-validation and of 0.874 and 0.959 for the test set, respectively. Furthermore, the two models were used for virtual screening experiments to identify neuroprotective compounds in XXMD. Ten representative compounds with potential therapeutic effects against the two phenotypes were selected for further cell-based assays. Among the selected compounds, two compounds significantly inhibited H2O2-induced and Na2S2O4-induced neurotoxicity simultaneously. Together, our findings suggested that machine learning algorithms such as combination Bayesian models were feasible to predict neuroprotective compounds and to preliminarily demonstrate the pharmacological mechanisms of TCM.

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North American Hardwoods Identification Using Machine-Learning

Forests ◽

10.3390/f11030298 ◽

2020 ◽

Vol 11 (3) ◽

pp. 298 ◽

Cited By ~ 2

Author(s):

Dercilio Junior Verly Lopes ◽

Greg W. Burgreen ◽

Edward D. Entsminger

Keyword(s):

Machine Learning ◽

North American ◽

Mobile Application ◽

Cross Validation ◽

Data Augmentation ◽

Technical Note ◽

Machine Learning Method ◽

Training Set ◽

Hardwood Species ◽

Fold Cross Validation

This technical note determines the feasibility of using an InceptionV4_ResNetV2 convolutional neural network (CNN) to correctly identify hardwood species from macroscopic images. The method is composed of a commodity smartphone fitted with a 14× macro lens for photography. The end-grains of ten different North American hardwood species were photographed to create a dataset of 1869 images. The stratified 5-fold cross-validation machine-learning method was used, in which the number of testing samples varied from 341 to 342. Data augmentation was performed on-the-fly for each training set by rotating, zooming, and flipping images. It was found that the CNN could correctly identify hardwood species based on macroscopic images of its end-grain with an adjusted accuracy of 92.60%. With the current growing of machine-learning field, this model can then be readily deployed in a mobile application for field wood identification.

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Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

Applied Sciences ◽

10.3390/app10061999 ◽

2020 ◽

Vol 10 (6) ◽

pp. 1999 ◽

Cited By ~ 7

Author(s):

Milica M. Badža ◽

Marko Č. Barjaktarović

Keyword(s):

Neural Network ◽

Machine Learning ◽

Brain Tumors ◽

Convolutional Neural Network ◽

Cross Validation ◽

Magnetic Resonance Images ◽

Generalization Capability ◽

Data Set ◽

Fold Cross Validation

The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.

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Issues in performance evaluation for host–pathogen protein interaction prediction

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500116 ◽

2016 ◽

Vol 14 (03) ◽

pp. 1650011 ◽

Cited By ~ 9

Author(s):

Wajid Arshad Abbasi ◽

Fayyaz Ul Amir Afsar Minhas

Keyword(s):

Machine Learning ◽

Protein Interactions ◽

Cross Validation ◽

Protein Protein Interactions ◽

Evaluation Scheme ◽

Host Pathogen ◽

Pathogen Protein ◽

Protein Interaction Prediction ◽

Underlying Mechanisms ◽

Fold Cross Validation

The study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein–protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs. A number of machine learning based methods for predicting host–pathogen interactions (HPI) have been proposed in the literature. The techniques used for assessing the accuracy of such predictors are of critical importance in this domain. In this paper, we question the effectiveness of K-fold cross-validation for estimating the generalization ability of HPI prediction for proteins with no known interactions. K-fold cross-validation does not model this scenario, and we demonstrate a sizable difference between its performance and the performance of an alternative evaluation scheme called leave one pathogen protein out (LOPO) cross-validation. LOPO is more effective in modeling the real world use of HPI predictors, specifically for cases in which no information about the interacting partners of a pathogen protein is available during training. We also point out that currently used metrics such as areas under the precision-recall or receiver operating characteristic curves are not intuitive to biologists and propose simpler and more directly interpretable metrics for this purpose.

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Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM)

Teknika ◽

10.34148/teknika.v10i1.311 ◽

2021 ◽

Vol 10 (1) ◽

pp. 18-26

Author(s):

Hendry Cipta Husada ◽

Adi Suryaputra Paramita

Keyword(s):

Machine Learning ◽

Social Media ◽

Support Vector Machine ◽

Cross Validation ◽

Support Vector ◽

Learning Approach ◽

Social Media Platform ◽

Machine Learning Approach ◽

Media Platform ◽

Fold Cross Validation

Perkembangan teknologi saat ini telah memberikan kemudahan bagi banyak orang dalam mendapatkan dan menyebarkan informasi di berbagai social media platform. Twitter merupakan salah satu media yang kerap digunakan untuk menyampaikan opini sebagai bentuk reaksi seseorang atas suatu hal. Opini yang terdapat di Twitter dapat digunakan perusahaan maskapai penerbangan sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi perusahaan. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif, negatif, atau netral melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Klasifikasi opini dilakukan dengan machine learning approach memanfaatkan algoritma multi-class Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah opini dalam bahasa Inggris dari para pengguna Twitter terhadap maskapai penerbangan. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh menggunakan SVM kernel RBF pada nilai parameter 𝐶(complexity) = 10 dan 𝛾(gamma) = 1, dengan nilai accuracy sebesar 84,37% dan 80,41% ketika menggunakan 10-fold cross validation.

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The Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

10.26434/chemrxiv.6895646.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mahendra Awale ◽

Jean-Louis Reymond

Keyword(s):

Machine Learning ◽

Cross Validation ◽

Nearest Neighbor ◽

Target Prediction ◽

Molecular Shape ◽

Public Access ◽

Molecular Fingerprints ◽

Small Molecule Drug ◽

Fold Cross Validation

<div>Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and substructures (ECfp4), and features an unprecedented combination of nearest neighbor (NN) searches and Naïve Bayes (NB) machine learning, together with simple NN searches, NB and Deep Neural Network (DNN) machine learning models as further options. Although NN(ECfp4) gives the best results in terms of recall in a 10-fold cross-validation study, combining NN searches with NB machine learning provides superior precision statistics, as well as better results in a case study predicting off-targets of a recently reported TRPV6 calcium channel inhibitor, illustrating the value of this combined approach. PPB2 is available to assess possible off-targets of small molecule drug-like compounds by public access at ppb2.gdb.tools.</div>

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Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.662749 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yuhong Huang ◽

Wenben Chen ◽

Xiaoling Zhang ◽

Shaofu He ◽

Nan Shao ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Cross Validation ◽

Learning Model ◽

Training Dataset ◽

Tumor Shrinkage ◽

Clinicopathologic Characteristics ◽

Testing Dataset ◽

Machine Learning Model ◽

Fold Cross Validation

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

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