Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.
machine vision snyder qi pdf 15
The focus of this systematic review is to study machine learning (ML) as a way to support the discovery of biomarkers that can be measured regularly and inexpensively using non-invasive and readily-accessible techniques, thus reducing the test burden on PwMS and optimizing early detection and treatment management. ML refers to computational algorithms for gathering and making sense of evidence derived from large volumes of data thereby permitting, or facilitating, human judgement and decision-making [16, 17] (see Supplementary Material A for further background on ML problems; supervised and unsupervised ML algorithms; and their timeline). ML has the potential to help in the search for a clinically useful biomarker because it can assess how well candidate biomarkers perform in the assessment of disease severity and prognosis, either individually or in combination. ML may also assist in developing decision-support techniques to aid clinicians and PwMS in making optimal individual treatment choices and in assessing the response to a chosen treatment.
H. Yi, Z. Xie, R. Reetzke, A.G. Dimakis and B. Chandrasekaran.Vowel decoding from single-trial speech-evoked electrophysiological responses: A feature-based machine learning approach. Brain and Behavior. April 2017; (Open Access)
In detail, important features were first identified with three feature selection algorithms: Information Gain (InfoGain) [47]; Correlation Feature Selection (CFS) [24, 47, 48] and ReliefF, [49]. Then, to predict genes in the up and down categories, five widely used classifiers -Logistic Regression, Classification Via Regression, Random Forest, LMT, Random Subspace- were employed, which have been applied to solve various classification and prediction problems in biology, showed comparable or even higher performance than other commonly used machine learning algorithms (Additional file 1: Table S4 and S6) [23, 24, 50,51,52]. To perform this analysis, we first split the data sets into training data set and testing data set, with 80% of differentially expressed genes for training data set, and the remaining 20% genes for testing data set. Next, to achieve the best combination, 10-fold cross validation on various combinations of feature selection and classification methods were performed. Finally, to predict differentially expressed genes, the top 3 powerful combinations were applied to the candidate gene list. Biological functions of associated genes were assessed by Gene Ontology Consortium [53, 54] and agriGO [55, 56].
Dominant features, if any, would provide insight into the underlying biological process of transcriptional regulation. Hence, we assessed the contribution of top 10 individual features to DEGs identification. Interestingly, none of the top 10 features had a correlation to gene expression score higher than 0.4 (Fig. 2b). We then selected two of top 10 features (CK23_Log2FC_full_gene and CK14diff_full_gene) to compare its distribution in up- and down- regulated genes. The distributions of differential signals of H3K23Ac and H3K14Ac were distinct in ethylene up-regulated gene from ethylene down-regulated genes. However, a significant overlap was also detected (Fig. 2c and d), suggesting that a single feature is not sufficient to predict the gene expression. However, the correlation increased with 23 features incorporated into the model (Fig. 2b). This additive effect of combined features in classification indicated unrevealed interplay among epigenetic markers, which in turn addressed the usefulness of machine learning in such complicated biological contexts.
Although remarkable power of RNA-seq has been achieved in the past few years, currently available methods leave rooms for improvement in terms of sensitivity and efficiency that are greatly affected by experimental design/operation and the following data analysis processes [9, 67, 68]. To fill in this gap, two strategies have been employed: to improve experimental design/performance and to develop better algorithms/softwares [13, 14, 16, 67, 68]. Machine learning based methods provide a new way that could avoid the inherited limitations existed in experimental design or data analysis processes in RNA-seq. It has been used to predict stress-related genes in Arabidopsis [23] or cancer related genes in human [24]. In this study, we found that more than 70% of the predicted genes were never before reported as DEGs in the response to ethylene, thus provided us a greatly enlarged candidate pool for future research and showed the power of machine learning based method in the predcition of novel DEGs.
To validate our prediction, we examined the expression of the newly identified genes by qRT-PCR. Up to 60% of newly predicted genes were confirmed to be regulated by ethylene (Fig. 6). Yet, the accuracy of machine learning based method still needs improvement. Given the close relationship among gene expression, histone methylation [69], histone acetylation [70], DNA methylation [71] RNA methylation [72], and post transcriptional regulation, further studies including additional features such as other histone modifications, DNA modification, RNA modification or post transcriptional regulation would help improve the accuracy of prediction. In addition, we were not able to estimate gene expression quantitatively based on binary classification methods, which could be realized by emerging advanced models in deep learning, such as MultiLayer Perceptron and Stacked Denoising Auto-encoder [73].
Table S1. Primers for qPCR. Table S2. The list of all features considered prior to feature selection. Table S3. Training and testing data set used for machine learning. Table S4. Summary of feature evaluation. Table S5. Selected features via differential models. Table S6. AUC values of different feature selection methods with combination of different classification methods. Table S7. Predicted genes by the model based on InfoGain feature selection and Logistic Regression. Table S8. GO analysis of predicted genes. (ZIP 215 kb)
Haley Lepp, Chee Wee Leong, Katrina Roohr, Michelle Martin-Raugh and Vikram Ramanarayanan (2020). Effect of modality on human and machine scoring of presentation videos, in proceedings of: International Conference on Multimodal Interaction (ICMI 2020), Virtual Conference, Oct 2020 [pdf].
We extended the Laplacian SVM [14] and proposed the semi-supervised support vector machine learning algorithm, \(S^3VM-R\). In particular, we incorporated additional information of our feature space as a regularization term into the standard optimization formulation with regard to the Laplacian SVM. We also used geometry of the underlying data as an intrinsic regularization term in Laplacian SVM.
Beyond these works, the work of [22] studied the problem of isolating sources of human trafficking from online advertisements with a pairwise entity resolution approach. Specifically, they used phone number as a strong feature and trained a classifier to predict if two ads are from the same source. This classifier was then used to perform entity resolution using a heuristically learned value for the score of classifier. Another work of [6] used Backpage.com data and extracted most likely human trafficking spatio-temporal patterns with the help of law enforcement. Note that unlike our method, this work did not employ any machine learning methodologies for automatically identifying human trafficking related advertisements. The work of [23] also deployed machine learning for the advertisement classification problem, by training a supervised learning classifier on labeled data (based on phone numbers of known traffickers) provided by a victim advocacy group. We note that while phone numbers can provide a very precise set of positive labeled data, there are clearly many posts with previously unseen phone numbers.
To ensure their anonymity, traffickers would deploy techniques to generate diverse information and hence make their posts look more complicated. They usually do this to avoid being identified by either human analysts or automated programs. Thus, to obtain the third feature we take an approach from complexity theory, namely Kolmogorov complexity, which is defined as length of shortest program to reproduce a string of characters on a universal machine such as the Turing Machine [25]. Since the Kolmogorov complexity is not computable, we approximate the complexity of an advertisement content by first removing stop words and then computing entropy of the content [25]. To illustrate this, let X denote the content and \(x_i\) be a given word in the content. We use the following equation [31] to calculate the entropy of the content and thus approximate the Kolmogorov complexity of X:
We assume a set of l labeled pairs \(\(x_i,y_i)\_i=1^l\) and an unlabeled set of u instances \(\x_l+i\_i=1^u\), where \(x_i\in \mathbb R^n\) and \(y_i\in \+1,-1\\). Recall for the standard soft-margin support vector machine, the following optimization problem is solved: 2ff7e9595c
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