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Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization

機(jī)譯：基于Cox和AFT模型并采用L1 / 2正則化的半監(jiān)督學(xué)習(xí)方法進(jìn)行癌癥生存分析

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BackgroundOne of the most important objectives of the clinical cancer research is to diagnose cancer more accurately based on the patients’ gene expression profiles. Both Cox proportional hazards model (Cox) and accelerated failure time model (AFT) have been widely adopted to the high risk and low risk classification or survival time prediction for the patients’ clinical treatment. Nevertheless, two main dilemmas limit the accuracy of these prediction methods. One is that the small sample size and censored data remain a bottleneck for training robust and accurate Cox classification model. In addition to that, similar phenotype tumours and prognoses are actually completely different diseases at the genotype and molecular level. Thus, the utility of the AFT model for the survival time prediction is limited when such biological differences of the diseases have not been previously identified.
機(jī)譯：背景技術(shù)臨床癌癥研究的最重要目標(biāo)之一是根據(jù)患者的基因表達(dá)譜更準(zhǔn)確地診斷癌癥。 Cox比例風(fēng)險(xiǎn)模型（Cox）和加速失敗時(shí)間模型（AFT）已被廣泛用于患者臨床治療的高風(fēng)險(xiǎn)和低風(fēng)險(xiǎn)分類或生存時(shí)間預(yù)測(cè)。盡管如此，兩個(gè)主要難題限制了這些預(yù)測(cè)方法的準(zhǔn)確性。一個(gè)是小樣本量和審查數(shù)據(jù)仍然是訓(xùn)練魯棒且準(zhǔn)確的Cox分類模型的瓶頸。除此之外，在基因型和分子水平上，相似的表型腫瘤和預(yù)后實(shí)際上是完全不同的疾病。因此，當(dāng)先前尚未確定疾病的這種生物學(xué)差異時(shí)，AFT模型在生存時(shí)間預(yù)測(cè)中的用途就受到限制。

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