Titlebook: Bioinformatics Research and Applications; 17th International S Yanjie Wei,Min Li,Zhipeng Cai Conference proceedings 2021 Springer Nature Sw

MAL

Combining Model-Based and Model-Free Reinforcement Learning Policies for More Efficient Sepsis Treat of sepsis is challenging because individual patients respond differently to the treatment, thus calling for a pressing need of personalized treatment strategies. Reinforcement learning (RL) has been widely used to learn optimal strategies for sepsis treatment, especially for the administration of i

歌曲

An Efficient Two-Stage Fusion Network for Computer-Aided Diagnosis of Diabetic Footlack of timely diagnosis of DF. Diagnosing DF at early stage is very essential. However, it is easy for inexperienced doctors to confuse Diabetic Foot Ulcer (DFU) wounds and other specific ulcer wounds when there is a lack of patients’ health records in underdeveloped areas. In this paper, we propos

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Indict

Identification of Gastric Cancer Immune Microenvironment Related Genes with Poor Prognosis and Tumorcape, local resistance, cancer development, and distant metastasis, thereby substantially affecting the future development of frontline interventions and prognosis outcomes. The molecular and cellular nature of the TIME influences disease outcome by altering the balance of suppressive versus cytotox

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陈旧

Reclaim

A Sequence-Based Antibody Paratope Prediction Model Through Combing Local-Global Information and Parn antigen, known as paratope, mediates antibody-antigen interaction with high affinity and specificity. And the accurate prediction of those regions from antibody sequence contributes to the design of therapeutic antibodies and remains challenging. However, the experimental methods are time-consumin

dithiolethione

SuccSPred: Succinylation Sites Prediction Using Fused Feature Representation and Ranking Method in biological structure and functions. However, experimental identification of succinylation sites is time-consuming and laborious. Traditional technology cannot meet the rapid growth of the sequence data sets. Therefore, we proposed a new computational method named SuccSPred to predict succinylati

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