Combining Survival Analysis and Machine Learning for Mass Cancer Risk Prediction using EHR data

Purely medical cancer screening methods are often costly, time-consuming, and weakly applicable on a large scale. Advanced Artificial Intelligence (AI) methods greatly help cancer detection but require specific or deep medical data. These aspects affect the mass implementation of cancer screening methods. For these reasons, it is a disruptive change for healthcare to apply AI methods for mass personalized assessment of the cancer risk among patients based on the existing Electronic Health Records (EHR) volume. This paper presents a novel method for mass cancer risk prediction using EHR data. Among other methods, our one stands out by the minimum data greedy policy, requiring only a history of medical service codes and diagnoses from EHR. We formulate the problem as a binary classification. This dataset contains 175 441 de-identified patients (2 861 diagnosed with cancer). As a baseline, we implement a solution based on a recurrent neural network (RNN). We propose a method that combines machine learning and survival analysis since these approaches are less computationally heavy, can be combined into an ensemble (the Survival Ensemble), and can be reproduced in most medical institutions. We test the Survival Ensemble in some studies. Firstly, we obtain a significant difference between values of the primary metric (Average Precision) with 22.8% (ROC AUC 83.7%, F1 17.8%) for the Survival Ensemble versus 15.1% (ROC AUC 84.9%, F1 21.4%) for the Baseline. Secondly, the performance of the Survival Ensemble is also confirmed during the ablation study. Thirdly, our method exceeds age baselines by a significant margin. Fourthly, in the blind retrospective out-of-time experiment, the proposed method is reliable in cancer patient detection (9 out of 100 selected). Such results exceed the estimates of medical screenings, e.g., the best Number Needed to Screen (9 out of 1000 screenings)

Dynamic nature of active chromatin hubs

Aim. In order to get more information about organization of active chromatin hubs and their role in the regulation of gene transcription we have studied the spatial organization of the a-globin gene domain in cultured chicken erythroblasts. Methods. The chromosome conformation capture (3C) protocol was employed to analyze the 3D configuration of the chicken a-globin gene domain. Results. We have demonstrated that in the same cell population the chicken domain of a-globin gene may be organized in two different active chromatin hubs. One of them appears essential for the activation of the a-globin gene expression while the other – for the activation of TMEM8 gene which constitutes a part of the a-globin gene domain in chicken, but not in human and other mammals. Importantly, two regulatory elements participate in the formation of both active chromatin hubs. Conclusions. The assembly of the same genomic area into two alternative chromatin hubs which share some regulatory elements suggests that active chromatin hubs are dynamic rather than static, and that regulatory elements may shuttle between different chromatin hubs. Keywords: active chromatin hub, globin gene, genomic domain, chromosome conformation capture.Мета. Щоб отримати нову інформацію стосовно організації активаторних хроматинових блоків та їхньої ролі в регуляції транскрипції ми вивчили просторову організацію домену a-глобінових генів у культивованих курячих еритробластах. Методи. Для анализу 3D конфігурації домену a-глобінових генів використано метод фіксації конформації хромосоми (3С). Результати. Ми продемонстрували, що в одній і тій самій популяції курячих клітин домен a-глобінових генів може бути організованим у два різних хроматинових блоки. Один з них необхідний для активації транскрипції a-глобінових генів, тоді як другий забезпечує активацію транскрипції гена TMEM8. Цей ген входить до складу домену aглобінових генів курей, але не ссавців і людини. Важливо, що два регуляторних елементи домену a-глобінових генів присутні у складі обох активаторних хроматинових блоків. Висновки. Існування в одному й тому ж геномному домені двох різних активаторних комплексів, які мають у своєму складі спільні регуляторні елементи, свідчить про динамічну природу активаторних хроматинових блоків, що дозволяє спільним регуляторним елементам періодично переміщуватися з одного комплексу в другий. Ключові слова: активаторні хроматинові блоки, глобіновий ген, геномний домен, метод фіксації хромосоми.Цель. Чтобы получить новую информацию об организации активаторных хроматиновых блоков и их роли в регуляции транскрипции, мы изучили пространственную организацию домена a-глобиновых генов в культивируемых куриных эритробластах. Методы. Для анализа 3D конфигурации домена a-глобиновых генов использован метод фиксации конформации хромосомы (3С). Результаты. Мы продемонстрировали, что в одной и той же популяции куриных клеток домен a-глобиновых генов может быть организован в два различных хроматиновых блока. Один из них необходим для активации транскрипции a-глобиновых генов, в то время как другой обеспечивает активацию транскрипции гена TMEM8. Этот ген входит в состав домена a-глобиновых генов кур, но не млекопитающих и человека. Важно, что два регуляторных элемента домена a-глобиновых генов присутствуют в составе обоих активаторных хроматиновых блоков. Выводы. Существование в одном и том же геномном домене двух разных активаторных комплексов, имеющих в своем составе общие регуляторные элементы, свидетельствует о динамической природе активаторных хроматиновых блоков, что позволяет общим регуляторным элементам периодически перемещаться из одного комплекса в другой. Ключевые слова: активаторные хроматиновые блоки, глобиновый ген, геномный домен, метод фиксации хромосомы

Mapping of the nuclear matrix-bound chromatin hubs by a new M3C experimental procedure

We have developed an experimental procedure to analyze the spatial proximity of nuclear matrix-bound DNA fragments. This protocol, referred to as Matrix 3C (M3C), includes a high salt extraction of nuclei, the removal of distal parts of unfolded DNA loops using restriction enzyme treatment, ligation of the nuclear matrix-bound DNA fragments and a subsequent analysis of ligation frequencies. Using the M3C procedure, we have demonstrated that CpG islands of at least three housekeeping genes that surround the chicken α-globin gene domain are assembled into a complex (presumably, a transcription factory) that is stabilized by the nuclear matrix in both erythroid and non-erythroid cells. In erythroid cells, the regulatory elements of the α-globin genes are attracted to this complex to form a new assembly: an active chromatin hub that is linked to the pre-existing transcription factory. The erythroid-specific part of the assembly is removed by high salt extraction. Based on these observations, we propose that mixed transcription factories that mediate the transcription of both housekeeping and tissue-specific genes are composed of a permanent compartment containing integrated into the nuclear matrix promoters of housekeeping genes and a ‘guest’ compartment where promoters and regulatory elements of tissue-specific genes can be temporarily recruited

Fichte and Hegel on recognition and slavery

In the first section of this essay I show how Hegel’s account of the struggle for recognition can be explained in terms of the role that Fichte accords to recognition in his deduction of the concept of right and, in particular, in terms of a problem to which this deduction gives rise. In the second section, I show how Hegel seeks to resolve this problem by means of his account of the struggle for recognition. Finally, in the third section, I show how Fichte’s and Hegel’s claims concerning the necessity of mutual recognition do not prevent them from regarding slavery as justified in certain circumstances, or at least as being as much the fault of the person enslaved as the person who has enslaved him or her, despite the fact that slavery represents one of the clearest possible examples of a situation in which mutual recognition is absent. One may therefore question the extent to which they regard mutual recognition as an absolutely fundamental norm of social relations. There is the difference, however, that Hegel’s position appears to be that mutual recognition becomes such a norm in the course of history, whereas Fichte implies that the absence of mutual recognition may be justified simply whenever an individual has failed to raise him-or herself to the level of a being whose attitude towards him-or herself as demonstrated through his or her actions is proof of a status that demands recognition from others

TMEM8 – a non-globin gene entrapped in the globin web

For more than 30 years it was believed that globin gene domains included only genes encoding globin chains. Here we show that in chickens, the domain of α-globin genes also harbor the non-globin gene TMEM8. It was relocated to the vicinity of the α-globin cluster due to inversion of an ∼170-kb genomic fragment. Although in humans TMEM8 is preferentially expressed in resting T-lymphocytes, in chickens it acquired an erythroid-specific expression profile and is upregulated upon terminal differentiation of erythroblasts. This correlates with the presence of erythroid-specific regulatory elements in the body of chicken TMEM8, which interact with regulatory elements of the α-globin genes. Surprisingly, TMEM8 is not simply recruited to the α-globin gene domain active chromatin hub. An alternative chromatin hub is assembled, which includes some of the regulatory elements essential for the activation of globin gene expression. These regulatory elements should thus shuttle between two different chromatin hubs