PMC:4718081 / 21472-22514 JSONTXT

{"project":"TEST0","denotations":[{"id":"26834548-134-142-278852","span":{"begin":217,"end":221},"obj":"[\"24634832\"]"},{"id":"26834548-203-211-278853","span":{"begin":301,"end":305},"obj":"[\"21992749\"]"},{"id":"26834548-234-242-278854","span":{"begin":368,"end":372},"obj":"[\"17827035\"]"},{"id":"26834548-220-228-278855","span":{"begin":462,"end":466},"obj":"[\"24250789\"]"},{"id":"26834548-234-242-278856","span":{"begin":550,"end":554},"obj":"[\"21992749\"]"},{"id":"26834548-218-226-278857","span":{"begin":604,"end":608},"obj":"[\"26388719\"]"},{"id":"26834548-212-220-278858","span":{"begin":669,"end":673},"obj":"[\"26298855\"]"},{"id":"26834548-208-216-278859","span":{"begin":762,"end":766},"obj":"[\"25042445\"]"},{"id":"26834548-220-228-278860","span":{"begin":850,"end":854},"obj":"[\"21992749\"]"},{"id":"26834548-228-236-278861","span":{"begin":915,"end":919},"obj":"[\"26298855\"]"},{"id":"26834548-234-242-278862","span":{"begin":1008,"end":1012},"obj":"[\"25042445\"]"}],"text":"Research Biomarkers Normal vs. AD Normal vs. MCI\nACC (%) SEN (%) SPE (%) ACC (%) SEN (%) SPE (%)\nSVM based on network disruption model (this study) CSF 95 95 95 90 95 84\nSVM (Apostolova et al., 2014) CSF 82 – – 74 – –\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) CSF 93 – – 83 – –\nLogistic Regression (Teipel et al., 2007) CSF 81 78 83 – – –\nLarge-scale regularized logistic regression (Casanova et al., 2013) CSF 75 71 78 64 50 74\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) MRI 93 – – 83 – –\nSVM (Salvatore et al., 2015) MRI 76 – – 72 – –\nSparse representation (Xu et al., 2015) MRI 95 96 90 75 66 82\nImage-level hierarchical classifier learning (Suk et al., 2014) MRI 92 92 95 84 99 54\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) PET 93 – – 83 – –\nSparse representation (Xu et al., 2015) PET 91 89 93 72 65 79\nImage-level hierarchical classifier learning (Suk et al., 2014) PET 92 88 96 84 99 57"}

{"project":"0_colil","denotations":[{"id":"26834548-24634832-278852","span":{"begin":217,"end":221},"obj":"24634832"},{"id":"26834548-21992749-278853","span":{"begin":301,"end":305},"obj":"21992749"},{"id":"26834548-17827035-278854","span":{"begin":368,"end":372},"obj":"17827035"},{"id":"26834548-24250789-278855","span":{"begin":462,"end":466},"obj":"24250789"},{"id":"26834548-21992749-278856","span":{"begin":550,"end":554},"obj":"21992749"},{"id":"26834548-26388719-278857","span":{"begin":604,"end":608},"obj":"26388719"},{"id":"26834548-26298855-278858","span":{"begin":669,"end":673},"obj":"26298855"},{"id":"26834548-25042445-278859","span":{"begin":762,"end":766},"obj":"25042445"},{"id":"26834548-21992749-278860","span":{"begin":850,"end":854},"obj":"21992749"},{"id":"26834548-26298855-278861","span":{"begin":915,"end":919},"obj":"26298855"},{"id":"26834548-25042445-278862","span":{"begin":1008,"end":1012},"obj":"25042445"}],"text":"Research Biomarkers Normal vs. AD Normal vs. MCI\nACC (%) SEN (%) SPE (%) ACC (%) SEN (%) SPE (%)\nSVM based on network disruption model (this study) CSF 95 95 95 90 95 84\nSVM (Apostolova et al., 2014) CSF 82 – – 74 – –\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) CSF 93 – – 83 – –\nLogistic Regression (Teipel et al., 2007) CSF 81 78 83 – – –\nLarge-scale regularized logistic regression (Casanova et al., 2013) CSF 75 71 78 64 50 74\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) MRI 93 – – 83 – –\nSVM (Salvatore et al., 2015) MRI 76 – – 72 – –\nSparse representation (Xu et al., 2015) MRI 95 96 90 75 66 82\nImage-level hierarchical classifier learning (Suk et al., 2014) MRI 92 92 95 84 99 54\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) PET 93 – – 83 – –\nSparse representation (Xu et al., 2015) PET 91 89 93 72 65 79\nImage-level hierarchical classifier learning (Suk et al., 2014) PET 92 88 96 84 99 57"}

{"project":"2_test","denotations":[{"id":"26834548-24634832-38218442","span":{"begin":217,"end":221},"obj":"24634832"},{"id":"26834548-21992749-38218443","span":{"begin":301,"end":305},"obj":"21992749"},{"id":"26834548-17827035-38218444","span":{"begin":368,"end":372},"obj":"17827035"},{"id":"26834548-24250789-38218445","span":{"begin":462,"end":466},"obj":"24250789"},{"id":"26834548-21992749-38218446","span":{"begin":550,"end":554},"obj":"21992749"},{"id":"26834548-26388719-38218447","span":{"begin":604,"end":608},"obj":"26388719"},{"id":"26834548-26298855-38218448","span":{"begin":669,"end":673},"obj":"26298855"},{"id":"26834548-25042445-38218449","span":{"begin":762,"end":766},"obj":"25042445"},{"id":"26834548-21992749-38218450","span":{"begin":850,"end":854},"obj":"21992749"},{"id":"26834548-26298855-38218451","span":{"begin":915,"end":919},"obj":"26298855"},{"id":"26834548-25042445-38218452","span":{"begin":1008,"end":1012},"obj":"25042445"}],"text":"Research Biomarkers Normal vs. AD Normal vs. MCI\nACC (%) SEN (%) SPE (%) ACC (%) SEN (%) SPE (%)\nSVM based on network disruption model (this study) CSF 95 95 95 90 95 84\nSVM (Apostolova et al., 2014) CSF 82 – – 74 – –\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) CSF 93 – – 83 – –\nLogistic Regression (Teipel et al., 2007) CSF 81 78 83 – – –\nLarge-scale regularized logistic regression (Casanova et al., 2013) CSF 75 71 78 64 50 74\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) MRI 93 – – 83 – –\nSVM (Salvatore et al., 2015) MRI 76 – – 72 – –\nSparse representation (Xu et al., 2015) MRI 95 96 90 75 66 82\nImage-level hierarchical classifier learning (Suk et al., 2014) MRI 92 92 95 84 99 54\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) PET 93 – – 83 – –\nSparse representation (Xu et al., 2015) PET 91 89 93 72 65 79\nImage-level hierarchical classifier learning (Suk et al., 2014) PET 92 88 96 84 99 57"}