عنوان

Machine Learning in Label-free Phenotypic Screening

شماره

TL5q49m2c6

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Machine Learning in Label-free Phenotypic Screening

نام عام مواد

[Thesis]

نام نخستين پديدآور

Chen, Lifan Claire

نام ساير پديدآوران

Jalali, Bahram

تاریخ نشرو بخش و غیره

2015

کسي که مدرک را اعطا کرده

Jalali, Bahram

امتياز متن

2015

متن يادداشت

High-throughput multivariate sensing is essential for high-content cellular phenotypic screening, where large volume of data for detection and accurate classification of rare events are often required. Here, we introduce time-stretch quantitative phase imaging (TS-QPI), a high-throughput label-free imaging flow cytometer developed for big data acquisition and analysis in phenotypic screening. TS-QPI is able to capture quantitative optical phase and intensity images simultaneously, enabling high-content cell analysis, cancer diagnostics, personalized genomics, and drug development. We further developed a complete machine learning pipeline that performs optical phase measurement, image processing, feature extraction, and classification. Multiple biophysical features such as morphological parameters, optical loss characteristics, and protein concentration are measured on individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. The technology is in clinical testing for blood screening and circulating tumor cell detection, as well as studying lipid accumulating algal strains for biofuel production. By integrating machine learning with high-throughput quantitative imaging, this system achieves record high accuracy in label-free cellular phenotypic screening and opens up a new path to data-driven diagnosis. Furthermore, we demonstrated, for the first time, real-time image compression performed in the optical domain to solve the big data challenge created by ultrafast measurement systems. Many ultrafast and high-throughput data acquisition equipment, including TS-QPI, produce a large volume of data in a short time, e.g. tens of Terabytes per hour. Such a data volume and velocity place a burden on data acquisition, storage, and processing and calls for technologies that compress images in optical domain and in real-time. As a solution, we have experimentally demonstrated warped time stretch, which offers variable spectral-domain sampling rate, as well as the ability to engineer the time-bandwidth product of the signal's envelope to match that of the data acquisition systems. We also show how to design the kernel of the transform and specifically, the nonlinear group delay profile governed by the signal sparsity. Such a kernel leads to smart detection with nonuniform spectral resolution, having direct utility in improvement of data acquisition rate, real-time data compression, and enhancement of ultrafast data capture accuracy.

مستند نام اشخاص تاييد نشده

Sidore, Angus Morgan

مستند نام اشخاص تاييد نشده

Chen, Lifan Claire

مستند نام تنالگان تاييد نشده

UCLA

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y