{"id":217,"date":"2022-07-09T18:08:00","date_gmt":"2022-07-09T10:08:00","guid":{"rendered":"https:\/\/www.helixmatrix.net\/?p=217"},"modified":"2022-11-10T11:08:26","modified_gmt":"2022-11-10T03:08:26","slug":"%e7%94%9f%e4%bf%a1%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e9%9b%86%e6%88%90%e8%bf%90%e7%ae%97%e5%b9%b3%e5%8f%b0","status":"publish","type":"post","link":"https:\/\/www.helixmatrix.net\/index.php\/2022\/07\/09\/%e7%94%9f%e4%bf%a1%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e9%9b%86%e6%88%90%e8%bf%90%e7%ae%97%e5%b9%b3%e5%8f%b0\/","title":{"rendered":"\u751f\u4fe1\u673a\u5668\u5b66\u4e60\u5e73\u53f0"},"content":{"rendered":"\n<p>\u76ee\u524d\u4f7f\u7528\u591a\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5bfb\u4f18\uff0c\u662f\u751f\u4fe1\u5206\u6790\u9ad8\u5206\u6587\u7ae0\u7684\u5e38\u7528\u601d\u8def\u3002\u5982\u6700\u8fd1\u53d1\u8868\u5728\u300aNature Communications\u300b\uff08<strong>IF: 14.919\uff09<\/strong>\u7684\u6587\u7ae0\u300aMachine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer\u300b<strong>,\u5c31\u4f7f\u7528\u4e86101 \u79cd\u673a\u5668\u5b66\u4e60\u9884\u6d4b\u6a21\u578b<\/strong>\u8fdb\u884c\u5206\u6790\uff0c\u6700\u7ec8\u53d1\u73b0\u6700\u4f73\u6a21\u578b\u662fLasso \u548c\u9010\u6b65 Cox\u56de\u5f52\u7684\u7ec4\u5408\uff0c\u8be5\u7ec4\u5408\u6a21\u578b\u5728\u6240\u6709\u9a8c\u8bc1\u6570\u636e\u96c6\u4e2d\u90fd\u5177\u6709\u5f88\u9ad8\u7684C-index.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/nature-1024x505.jpg\" alt=\"\" class=\"wp-image-219\" width=\"541\" height=\"267\" srcset=\"https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/nature-1024x505.jpg 1024w, https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/nature-300x148.jpg 300w, https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/nature-768x379.jpg 768w, https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/nature.jpg 1422w\" sizes=\"(max-width: 541px) 100vw, 541px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-style-default\"><img decoding=\"async\" loading=\"lazy\" width=\"765\" height=\"1024\" src=\"https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/QQ\u622a\u56fe20221109174932-765x1024.png\" alt=\"\" class=\"wp-image-221\" srcset=\"https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/QQ\u622a\u56fe20221109174932-765x1024.png 765w, https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/QQ\u622a\u56fe20221109174932-224x300.png 224w, https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/QQ\u622a\u56fe20221109174932-768x1028.png 768w, https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/QQ\u622a\u56fe20221109174932-1148x1536.png 1148w, https:\/\/www.helixmatrix.net\/wp-content\/uploads\/2022\/11\/QQ\u622a\u56fe20221109174932.png 1299w\" sizes=\"(max-width: 765px) 100vw, 765px\" \/><figcaption class=\"wp-element-caption\">\u6700\u4f18\u6a21\u578b\u7684\u641c\u7d22<\/figcaption><\/figure>\n\n\n\n<p>\u87ba\u65cb\u77e9\u9635\u516c\u53f8\u5f00\u53d1\u7684\u751f\u4fe1\u591a\u6a21\u578b\u673a\u5668\u5b66\u4e60\u7cfb\u7edf BioMatrix \uff0c\u4e00\u6b21\u6027\u8fd0\u884c\u4e0a\u767e\u4e2a\u6a21\u578b\uff0c\u5e76\u81ea\u52a8\u8fdb\u884c\u8d1d\u53f6\u65af\u8d85\u53c2\u6570\u4f18\u5316\u3002\u641c\u7d22\u6700\u4f18\u89e3\u51b3\u65b9\u6848\u3002\u4ee5\u4e0b\u4e3a\u90e8\u5206\u652f\u6301\u7b97\u6cd5\uff0c\u6b22\u8fce\u5404\u4f4d\u8001\u5e08\uff0c\u8054\u7cfb\u4f7f\u7528<\/p>\n\n\n\n<ul>\n<li><strong>Linear Models<\/strong>\n<ul>\n<li>Ordinary Least Squares<\/li>\n\n\n\n<li>Ridge regression and classification<\/li>\n\n\n\n<li>Lasso<\/li>\n\n\n\n<li>Multi-task Lasso<\/li>\n\n\n\n<li>Elastic-Net<\/li>\n\n\n\n<li>Multi-task Elastic-Net<\/li>\n\n\n\n<li>Least Angle Regression<\/li>\n\n\n\n<li>LARS Lasso<\/li>\n\n\n\n<li>Orthogonal Matching Pursuit (OMP)<\/li>\n\n\n\n<li>Bayesian Regression<\/li>\n\n\n\n<li>Logistic regression<\/li>\n\n\n\n<li>Generalized Linear Regression<\/li>\n\n\n\n<li>Stochastic Gradient Descent &#8211; SGD<\/li>\n\n\n\n<li>Perceptron<\/li>\n\n\n\n<li>Passive Aggressive Algorithms<\/li>\n\n\n\n<li>Robustness regression: outliers and modeling errors<\/li>\n\n\n\n<li>Quantile Regression<\/li>\n\n\n\n<li>Polynomial regression: extending linear models with basis functions<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Linear and Quadratic Discriminant Analysis<\/strong>\n<ul>\n<li>Dimensionality reduction using Linear Discriminant Analysis<\/li>\n\n\n\n<li>Mathematical formulation of the LDA and QDA classifiers<\/li>\n\n\n\n<li>Mathematical formulation of LDA dimensionality reduction<\/li>\n\n\n\n<li>Shrinkage and Covariance Estimator<\/li>\n\n\n\n<li>Estimation algorithms<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Kernel ridge regression<\/strong><\/li>\n\n\n\n<li><strong>Support Vector Machines<\/strong>\n<ul>\n<li>Classification<\/li>\n\n\n\n<li>Regression<\/li>\n\n\n\n<li>Density estimation, novelty detection<\/li>\n\n\n\n<li>Complexity<\/li>\n\n\n\n<li>Tips on Practical Use<\/li>\n\n\n\n<li>Kernel functions<\/li>\n\n\n\n<li>Mathematical formulation<\/li>\n\n\n\n<li>Implementation details<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Stochastic Gradient Descent<\/strong>\n<ul>\n<li>Classification<\/li>\n\n\n\n<li>Regression<\/li>\n\n\n\n<li>Online One-Class SVM<\/li>\n\n\n\n<li>Stochastic Gradient Descent for sparse data<\/li>\n\n\n\n<li>Complexity<\/li>\n\n\n\n<li>Stopping criterion<\/li>\n\n\n\n<li>Tips on Practical Use<\/li>\n\n\n\n<li>Mathematical formulation<\/li>\n\n\n\n<li>Implementation details<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Nearest Neighbors<\/strong>\n<ul>\n<li>Unsupervised Nearest Neighbors<\/li>\n\n\n\n<li>Nearest Neighbors Classification<\/li>\n\n\n\n<li>Nearest Neighbors Regression<\/li>\n\n\n\n<li>Nearest Neighbor Algorithms<\/li>\n\n\n\n<li>Nearest Centroid Classifier<\/li>\n\n\n\n<li>Nearest Neighbors Transformer<\/li>\n\n\n\n<li>Neighborhood Components Analysis<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Gaussian Processes<\/strong>\n<ul>\n<li>Gaussian Process Regression (GPR)<\/li>\n\n\n\n<li>GPR examples<\/li>\n\n\n\n<li>Gaussian Process Classification (GPC)<\/li>\n\n\n\n<li>GPC examples<\/li>\n\n\n\n<li>Kernels for Gaussian Processes<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cross decomposition<\/strong>\n<ul>\n<li>PLSCanonical<\/li>\n\n\n\n<li>PLSSVD<\/li>\n\n\n\n<li>PLSRegression<\/li>\n\n\n\n<li>Canonical Correlation Analysis<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Naive Bayes<\/strong>\n<ul>\n<li>Gaussian Naive Bayes<\/li>\n\n\n\n<li>Multinomial Naive Bayes<\/li>\n\n\n\n<li>Complement Naive Bayes<\/li>\n\n\n\n<li>Bernoulli Naive Bayes<\/li>\n\n\n\n<li>Categorical Naive Bayes<\/li>\n\n\n\n<li>Out-of-core naive Bayes model fitting<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Decision Trees<\/strong>\n<ul>\n<li>Classification<\/li>\n\n\n\n<li>Regression<\/li>\n\n\n\n<li>Multi-output problems<\/li>\n\n\n\n<li>Complexity<\/li>\n\n\n\n<li>Tips on practical use<\/li>\n\n\n\n<li>Tree algorithms: ID3, C4.5, C5.0 and CART<\/li>\n\n\n\n<li>Mathematical formulation<\/li>\n\n\n\n<li>Minimal Cost-Complexity Pruning<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Ensemble methods\n<ul>\n<li>Bagging meta-estimator<\/li>\n\n\n\n<li>Forests of randomized trees<\/li>\n\n\n\n<li>AdaBoost<\/li>\n\n\n\n<li>Gradient Tree Boosting<\/li>\n\n\n\n<li>Histogram-Based Gradient Boosting<\/li>\n\n\n\n<li>Voting Classifier<\/li>\n\n\n\n<li>Voting Regressor<\/li>\n\n\n\n<li>Stacked generalization<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Multiclass and multioutput algorithms<\/strong>\n<ul>\n<li>Multiclass classification<\/li>\n\n\n\n<li>Multilabel classification<\/li>\n\n\n\n<li>Multiclass-multioutput classification<\/li>\n\n\n\n<li>Multioutput regression<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Feature selection<\/strong><\/li>\n\n\n\n<li><strong>Semi-supervised learning<\/strong>\n<ul>\n<li>Self Training<\/li>\n\n\n\n<li>Label Propagation<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Isotonic regression<\/strong><\/li>\n\n\n\n<li><strong>Probability calibration<\/strong>\n<ul>\n<li>1.16.1. Calibration curves<\/li>\n\n\n\n<li>1.16.2. Calibrating a classifier<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>1.17. Neural network models <\/strong>\n<ul>\n<li>Multi-layer Perceptron<\/li>\n\n\n\n<li>Classification<\/li>\n\n\n\n<li>Regression<\/li>\n\n\n\n<li>Regularization<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\u76ee\u524d\u4f7f\u7528\u591a\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5bfb\u4f18\uff0c\u662f\u751f\u4fe1\u5206\u6790\u9ad8\u5206\u6587\u7ae0\u7684\u5e38\u7528\u601d\u8def\u3002\u5982\u6700\u8fd1\u53d1\u8868\u5728\u300aNature Communicati [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":196,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[5],"tags":[],"_links":{"self":[{"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/posts\/217"}],"collection":[{"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/comments?post=217"}],"version-history":[{"count":8,"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/posts\/217\/revisions"}],"predecessor-version":[{"id":229,"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/posts\/217\/revisions\/229"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/media\/196"}],"wp:attachment":[{"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/media?parent=217"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/categories?post=217"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.helixmatrix.net\/index.php\/wp-json\/wp\/v2\/tags?post=217"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}