Template:胆固醇与甾体中间产物

Multi tool use
胆固醇与甾体代谢中间产物
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| 甲羟戊酸途径 |
到HMG-CoA
| 乙酰辅酶A · 乙酰乙酰辅酶A · β-羟-β-甲戊二酸单酰辅酶A · Β-羥基β-甲基丁酸
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| 酮体 | 丙酮 · 乙酰乙酸 · β-羟丁酸
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| 到二甲基烯丙基焦磷酸
| 甲羟戊酸 · 磷酸甲羟戊酸 · 5-二磷酸甲羟戊酸 · 异戊烯焦磷酸 · 二甲基丙烯酰焦磷酸
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| 牻牛儿基- | 牻牛儿基焦磷酸 · 牻牛儿基牻牛儿基焦磷酸
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| 类胡萝卜素 | 前植物烯二磷酸 · 八氢番茄红素
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| 非甲羟戊酸途径 |
DOXP · MEP · CDP-ME · CDP-MEP · MEcPP · HMB-PP · IPP · DMAPP
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| 到胆固醇
| 法尼基焦磷酸 · 鲨烯 · 2,3-氧鲨烯 · 羊毛甾醇 羊毛甾醇 · 7-烯胆甾烷醇 · 7-脱氢胆固醇 · 胆固醇
羊毛甾醇 · 酵母甾醇 · 7-脱氢胆甾醇 · 链甾醇 · 胆固醇 |
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| 甾体 |
皮质类固醇 (二十一碳孕烷)
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盐皮质激素 | 孕烯醇酮 · 孕酮 · 11-脱氧皮质酮 · 皮质酮 · 醛固酮
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| 糖皮质激素 | 孕烯醇酮 · 17-羟孕烯醇酮 · 17-羟孕酮 · 11-脱氧皮质醇 · 皮质醇 皮质醇 · 皮质酮
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| 性类固醇 |
雄激素 (十九碳雄固烷)
| DHEA · 雄烯二酮/5-雄烯二醇 · 睾酮 · 双氢睾酮 脱氢表雄酮硫酸盐 · 表睾酮
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| 雌激素 (十八碳雌烷)
| 雌酮 · 雌二醇 · 雌三醇
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| 非人类 |
植物固醇类
| 豆固醇 · 菜籽固醇
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| 麦角固醇类
| 麦角固醇 · 钙化醇
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| 代谢、k,c/g/r/p/y/i,f/h/s/l/o/e,a/u,n,m
| k,cgrp/y/i,f/h/s/l/o/e,au,n,m,人名体征
| 药物(A16/C10)、中间产物(k,c/g/r/p/y/i,f/h/s/o/e,a/u,n,m)
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內分泌系統索引
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| 描述 | |
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| 疾病 | |
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| 治療 | - 程序
- 藥物
- 鈣平衡
- 皮质类固醇
- 口服降血糖藥物
- 腦下垂體及下視丘
- 甲狀腺
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生物化學物質種類索引
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| 糖类 | |
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| 酯質 | - 类花生酸类
脂肪酸
- 甘油酯
- 磷脂
- 鞘脂類
- 胆固醇与甾体中间产物
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| 核酸 | |
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| 蛋白質 | |
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| 其他 | |
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up vote 2 down vote favorite There is a clear pattern that show for two separate subsets (set of columns); If one value is missing in a column, values of other columns in the same subset are missing for any row. Here is a visualization of missing data My tries up until now, I used ycimpute library to learn from other values, and applied Iterforest. I noted, score of Logistic regression is so weak (0.6) and thought Iterforest might not learn enough or anyway, except from outer subset which might not be enough? for example the subset with 11 columns might learn from the other columns but not from within it's members, and the same goes for the subset with four columns. This bar plot show better quantity of missings So of course, dealing with missings is better than dropping rows because It would affect my prediction which does contain the same missings quantity relatively. Any better way to deal with these ? [EDIT] The nullity pattern is confirmed: machine-learning cor...