Titlebook: Bioinformatics Research and Applications; 19th International S Xuan Guo,Serghei Mangul,Alexander Zelikovsky Conference proceedings 2023 The

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angina-pectoris

,Unveiling the Robustness of Machine Learning Models in Classifying COVID-19 Spike Sequences,ledge the possibility of errors in spike proteins, which could lead to misleading outcomes and misguide decision-making authorities. Hence, a comprehensive examination of the robustness of ML and deep learning models in classifying spike sequences is essential. In this paper, we propose a framework

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,Efficient Sequence Embedding for SARS-CoV-2 Variants Classification,trings) from a given biological sequence, computing a sketch of the sequence, and performing dot product calculations using the sketch. By avoiding the need to compute the entire spectrum (frequency count) and operating with low-dimensional vectors (sketches) for sequences instead of the memory-inte

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南极

,Deep Learning Architectures for the Prediction of YY1-Mediated Chromatin Loops,ng for sequence representation and long short-term memory, and a convolution neural network along with a gradient flow strategy similar to DenseNet architectures. Both of the predictors are evaluated on two different benchmark datasets of two cell lines HCT116 and K562. Overall the proposed predicto

冷峻

,MPFNet: ECG Arrhythmias Classification Based on Multi-perspective Feature Fusion,s from two different perspectives to enhance the completeness and accuracy of the final representation by utilizing the correlation and complementarity between perspectives. We evaluate our method on a large public ECG dataset and the experimental results demonstrate that MPFNet outperforms the stat

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BUDGE

Radiology Report Generation via Visual Recalibration and Context Gating-Aware,ating-aware module to combine lesion location and report context information to solve the problem of long-distance dependence in diagnostic reports. Meanwhile, the context gating-aware module can identify text fragments related to lesion descriptions, improve the model’s perception of lesion text in

Armada

,Using Generating Functions to Prove Additivity of Gene-Neighborhood Based Phylogenetics - Extended ion. In this work, we provide an alternative, stand alone combinatorial proof of the same result. By using generating function techniques we derive explicit expressions of the system’s probabilistic dynamics in the form of rational functions of the model parameters. This, in turn, allows us to infer