Next-Gen AI for Disease Definition, Patient Stratification, and Placebo Effect

Tsay, MIke; Geraci, Joseph; Agrawal, Abhishek

doi:10.31219/osf.io/pc7ak

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…This organizational technique was used to extract insights from models that could then be compared with statistical methods suitable for small data. An interactive hypothesis-generating interface was used such that human interaction could facilitate the analysis of different models [44,45]. This methodology allows the user to explore hypotheses generated by the unsupervised clustering methods of the system.…”

Section: Methodsmentioning

confidence: 99%

“…This organizational technique was used to extract insights from models that could then be compared with statistical methods suitable for small data. The only proprietary method used for these results are the techniques referred to as a feature selection tool [20, 21], in order to help us reduce the size of the data set to 16 dimensions. More specifically, we used these methods to create several new 16-dimensional data sets.…”

Section: Methodsmentioning

confidence: 99%

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Small Patient Datasets Reveal Genetic Drivers of Non-Small Cell Lung Cancer Subtypes Using a Novel Machine Learning Approach

Cook

Qorri

Baskar

et al. 2021

Preprint

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There are many small datasets of significant value in the medical space that are being underutilized. Due to the heterogeneity of complex disorders found in oncology, systems capable of discovering patient subpopulations while elucidating etiologies is of great value as it can indicate leads for innovative drug discovery and development. Here, we report on a machine intelligence-based study that utilized a combination of two small non-small cell lung cancer (NSCLC) datasets consisting of 58 samples of adenocarcinoma (ADC) and squamous cell carcinoma (SCC) and 45 samples from the gene expression analysis of human lung cancer and control samples series (GSE18842). Utilizing a novel machine learning approach, we were able to uncover subpopulations of ADC and SCC while simultaneously extracting which genes, in combination, were significantly involved in defining the subpopulations. An interactive hypothesis-generating interface designed to work with machine learning methods allowed us to explore the hypotheses generated by the unsupervised components of the system. Using these methods, we were able to uncover genes implicated by other methods and accurately discover known subpopulations without being asked, such as different levels of aggressiveness within the SCC and ADC subtypes. Furthermore, PIGX was a novel gene implicated in this study that warrants further study due to its role in breast cancer proliferation. Here we demonstrate the ability to learn from small datasets and reveal well-established properties of NSCLC. These machine learning techniques can reveal the driving factors behind subpopulations of patients altering the approach to drug discovery and development by making precision medicine a reality.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Small Patient Datasets Reveal Genetic Drivers of Non-Small Cell Lung Cancer Subtypes Using a Novel Machine Learning Approach

Cook

Qorri

Baskar

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…A suite of machine learning methods that were uniquely assembled due to their ability to extract subpopulations from high dimensional data and their ability to provide explanations for the driving mechanisms behind the subpopulations were used. The methods can be reviewed here [29] and here [30]. These methods included statistical measures of feature importance, ensemble methods, neural networks, and a novel system designed to work with patient population data [31].…”

Section: Methodsmentioning

confidence: 99%

“…The machine is rewarded for finding collections of samples that have several variables simultaneously in common, while those samples belong to the same perspective class. Thus, these methods are semi-supervised [29]. This particular machine intelligence utilizes geometric representation methods coupled with a fast learner.…”

Section: Methodsmentioning

confidence: 99%

Using Machine Intelligence to Uncover Alzheimer’s Disease Progression Heterogeneity

Qorri¹,

Tsay²,

Agrawal³

et al. 2020

Preprint

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Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Several existing and proprietary machine learning frameworks were applied to transcriptomic data. Through a proprietary interactive machine learning system, we were able to uncover several underlying biological mechanisms associated with AD pathology. These results, in turn, informed new hypotheses and identified novel targets for treatment. This paper reviews how the use of explainable machine learning technologies that are capable of extracting insights from small data can potentially provide a new taxonomy of disease. This is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD. We share results from such an effort involving a set of AD subject transcriptomic samples where we provide a combinatorial view of how the pathology associated with what we call AD could emerge.

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“…Only GSE10245 was used when analyzing gene expression levels for discriminating differences between sex as this data was omitted from GSE18842. Genetic expression levels denote Robust Multi-Array Average-calculated signal intensity [23].…”

Section: Datasetsmentioning

confidence: 99%

Small patient datasets reveal genetic drivers of non-small cell lung cancer subtypes using machine learning for hypothesis generation

Cook

Qorri²,

Baskar

et al. 2023

Exploration of Medicine

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Aim: Many small datasets of significant value exist in the medical space that are being underutilized. Due to the heterogeneity of complex disorders found in oncology, systems capable of discovering patient subpopulations while elucidating etiologies are of great value as they can indicate leads for innovative drug discovery and development. Methods: Two small non-small cell lung cancer (NSCLC) datasets (GSE18842 and GSE10245) consisting of 58 samples of adenocarcinoma (ADC) and 45 samples of squamous cell carcinoma (SCC) were used in a machine intelligence framework to identify genetic biomarkers differentiating these two subtypes. Utilizing a set of standard machine learning (ML) methods, subpopulations of ADC and SCC were uncovered while simultaneously extracting which genes, in combination, were significantly involved in defining the subpopulations. A previously described interactive hypothesis-generating method designed to work with ML methods was employed to provide an alternative way of extracting the most important combination of variables to construct a new data set. Results: Several genes were uncovered that were previously implicated by other methods. This framework accurately discovered known subpopulations, such as genetic drivers associated with differing levels of aggressiveness within the SCC and ADC subtypes. Furthermore, phyosphatidylinositol glycan anchor biosynthesis, class X (PIGX) was a novel gene implicated in this study that warrants further investigation due to its role in breast cancer proliferation. Conclusions: The ability to learn from small datasets was highlighted and revealed well-established properties of NSCLC. This showcases the utility of ML techniques to reveal potential genes of interest, even from small datasets, shedding light on novel driving factors behind subpopulations of patients.

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Next-Gen AI for Disease Definition, Patient Stratification, and Placebo Effect

Cited by 4 publications

References 11 publications

Small Patient Datasets Reveal Genetic Drivers of Non-Small Cell Lung Cancer Subtypes Using a Novel Machine Learning Approach

Small Patient Datasets Reveal Genetic Drivers of Non-Small Cell Lung Cancer Subtypes Using a Novel Machine Learning Approach

Using Machine Intelligence to Uncover Alzheimer’s Disease Progression Heterogeneity

Small patient datasets reveal genetic drivers of non-small cell lung cancer subtypes using machine learning for hypothesis generation

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