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克羅地亞技術(shù)轉(zhuǎn)移中心提供生物混合物分?jǐn)?shù)判定方法,具體應(yīng)用于色譜分離法的病理數(shù)據(jù)
時(shí)間:2012-09-26 00:00:00 來(lái)源:本站原創(chuàng) 字號(hào):[ ]

克羅地亞技術(shù)轉(zhuǎn)移中心提供生物混合物分?jǐn)?shù)判定方法,具體應(yīng)用于色譜分離法的病理數(shù)據(jù)

克羅地亞一家技術(shù)轉(zhuǎn)移中心提供一項(xiàng)正在申請(qǐng)專利的計(jì)算方法,可應(yīng)用于生物分子混合物質(zhì)譜分析或色譜分離的測(cè)量數(shù)據(jù)。這項(xiàng)以機(jī)器學(xué)習(xí)為基礎(chǔ)的方法能夠過(guò)濾數(shù)據(jù),消除干擾數(shù)據(jù),以決定相關(guān)生物分子分?jǐn)?shù)。此方法能夠提供現(xiàn)存計(jì)算方法不具備的優(yōu)點(diǎn),可應(yīng)用于多種領(lǐng)域,如醫(yī)學(xué)診療。中心正在尋求被授權(quán)方與/或合作伙伴建立合資企業(yè)。

 此項(xiàng)新方法提供現(xiàn)存計(jì)算方法不具備的以下優(yōu)點(diǎn):

-    基于為每個(gè)樣本進(jìn)行復(fù)制實(shí)驗(yàn),具有統(tǒng)計(jì)可靠性。

-    通過(guò)檢測(cè)下的機(jī)器學(xué)習(xí)算法提供最優(yōu)化的數(shù)據(jù)表示參數(shù)(如分辨率)。

-    促進(jìn)測(cè)量數(shù)據(jù)中干擾相關(guān)部分和系統(tǒng)錯(cuò)誤的消除。

 此方法:

1)能夠準(zhǔn)確判斷特定辨別問(wèn)題的相關(guān)分?jǐn)?shù);

2)能夠基于一套篩選的相關(guān)分?jǐn)?shù)為種類分辨提供完善的計(jì)算模式。

 英文原文:

A method for determing fractions of biological mixtures specific for a physiological condition following chromatographic separation (09 HR 89GJ 3DJ9)

 A Croatian Technology Transfer office is offering patent-pending computational methodology which applies to measurements from mass spectrometry or from chromatographic separations of biomolecular mixtures. This machine learning-based method filters data in order to remove noise and determines the relevant biomolecular fractions, providing advantages over existing methods and fitting a variety of applications, e.g. in medical diagnostics. Licensee and/or partner for joint development are sought.

 The present invention provides several advantages over existing methods:
-it is based on a number of replicated experiments for each sample, drawing on statistical reliability
-it provides means for optimizing data representation parameters (e.g. resolution) by means of supervised machine learning algorithms
-it facilitates removal of components relating to noise and systematic errors in measurements.

This results in: (i) reliable determination of relevant fractions for the particular discrimination problem, and (ii) improved computational models for class distinction based on a filtered set of relevant fractions.

 

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