Uncertain Time Series Classification

Time series analysis has gained a lot of interest during the last decade with diverse applications in a large range of domains such as medicine, physic, and industry. The field of time series classification has been particularly active recently with the development of more and more efficient methods. However, the existing methods assume that the input time series is free of uncertainty. However, there are applications in which uncertainty is so important that it can not be neglected. This project aims to build efficient, robust, and interpretable classification methods for uncertain time series.

Computational analysis of pathological image enables interpretable prediction for microsatellite instability

ObjectiveMicrosatellite instability (MSI) is associated with several tumor types and its status has become increasingly vital in guiding patient treatment decisions. However, in clinical practice, distinguishing MSI from its counterpart is challenging since the diagnosis of MSI requires additional genetic or immunohistochemical tests. In this study, we aimed to establishe an interpretable pathological image analysis strategies to help medical experts to identify MSI automatically.DesignThree cohorts of Haematoxylin and eosin-stained whole-slide images from 1033 patients with different tumor types were collected from The Cancer Genome Atlas. These images were preprocessed and tessallated into small tiles. A image-level interpretable deep learning model and a feature-level interpretable random forest model were built up on these files.ResultsBoth models performed well in the three datasets and achieved image-level and feature-level interpretability repectively. Importantly, both from the image-level and feature-level interpretability, color features and texture characteristics are shown to contribute the most to the MSI prediction. Based on them, we established an interpretable classification framework. Therefore, the classification models under the proposed framework can serve as an efficient tool for predicting the MSI status of patients.ConclusionThis study establishes a interpretable classification framework to for predicting the MSI status of patients and provide more insights to pathologists with clinical understanding.

An interpretable classification method for predicting drug resistance in M. tuberculosis

MotivationThe prediction of drug resistance and the identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Modern methods based on testing against a catalogue of previously identified mutations often yield poor predictive performance. On the other hand, machine learning techniques have demonstrated high predictive accuracy, but many of them lack interpretability to aid in identifying specific mutations which lead to resistance. We propose a novel technique, inspired by the group testing problem and Boolean compressed sensing, which yields highly accurate predictions and interpretable results at the same time.ResultsWe develop a modified version of the Boolean compressed sensing problem for identifying drug resistance, and implement its formulation as an integer linear program. This allows us to characterize the predictive accuracy of the technique and select an appropriate metric to optimize. A simple adaptation of the problem also allows us to quantify the sensitivity-specificity trade-off of our model under different regimes. We test the predictive accuracy of our approach on a variety of commonly used antibiotics in treating tuberculosis and find that it has accuracy comparable to that of standard machine learning models and points to several genes with previously identified association to drug resistance.Availabilityhttps://github.com/hoomanzabeti/[email protected]