id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_p2xqvo6isfb75mbvnnkzerthaa	Michal B. Rozenwald	A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features	2020	21	.pdf	application/pdf	8589	917	60	patterns in Drosophila based on chromatin marks across three cell lines. to any similar biological dataset of chromatin features across various cell lines and experiments, Linear Regression, Gradient Boosting, Chromatin, DNA folding patterns, Machine for the prediction of chromatin folding in Drosophila using epigenetic features. Drosophila chromosome partitioning into TADs. Active chromatin marks are preferably characteristics of TADs result in assigning a continuous score to genomic bins along the TAD boundary prediction in Drosophila, where the histone modifications of extended chromatin features are most significant in predicting the TAD state. the RNN model, yt/2 represents the corresponding target value transitional gamma of the middle bin xt/2. First, we assessed whether the TAD state could be predicted from the set of chromatin Table 1 Evaluation of classical machine learning scores for all models, based on 5-features and 18TAD state prediction models are transferable between cell lines of model: genome-wide chromatin looping prediction.	./cache/work_p2xqvo6isfb75mbvnnkzerthaa.pdf	./txt/work_p2xqvo6isfb75mbvnnkzerthaa.txt