As a main part of automated machine learning, Neural Architecture Search (NAS) has been employed in exploring effective Click-Through Rate (CTR) prediction models of recommender systems in recent studies. However...
详细信息
When designing a biological regulatory network, new information or wet experiments can require adding variables or interactions, inside a previously validated model. They can result in complete reconsiderations of est...
详细信息
ISBN:
(纸本)9789897584909
When designing a biological regulatory network, new information or wet experiments can require adding variables or interactions, inside a previously validated model. They can result in complete reconsiderations of established behaviours. Fortunately, formal methods allow for fully automated verification of properties, and TotemBioNet is an efficient software integrating a collection of formal approaches for regulatory networks. It allowed us to develop a multidisciplinary methodology for designing large dynamical models in an incremental way, including non regression proofs (preservation of important biological properties).
This work is focused on ab initio prediction of bacterial small RNAs (sRNAs). We gathered a series of classification features used by existent computational tools in order to select the best ones for classifying putat...
详细信息
ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
This work is focused on ab initio prediction of bacterial small RNAs (sRNAs). We gathered a series of classification features used by existent computational tools in order to select the best ones for classifying putative sRNA sequences, leading to 22 relevant and non-redundant features. The Random Forest algorithm was chosen to build a predictive model using a dataset made with experimentally-validated sRNAs from different substrains. Our approach could achieve a higher predictive power in comparison with current methods for non-coding RNA prediction. We also describe our work in progress comprising a framework for genome-wide sRNA ab initio prediction. Our classifier and datasets can be found at ***.
Deep Learning models, such as convolutional neural networks (CNN), are hard to interpret due to their complex, nonlinear, and high-dimensional algorithms. We focused on interpreting CNNs for predicting Hepatocellular ...
Deep Learning models, such as convolutional neural networks (CNN), are hard to interpret due to their complex, nonlinear, and high-dimensional algorithms. We focused on interpreting CNNs for predicting Hepatocellular Carcinoma (HCC), the most common primary liver cancer, using histopathology images. We used a 50-layer residual neural network (RNN50), an inception CNN, and a recent innovation called concept whitening (CW). The results showed that adding CW layers to CNN models did not significantly affect model accuracy. However, post-hoc analysis methods, including inter-concept similarity rating, intra-concept similarity rating, concept importance rating, and feature vector displays, revealed improved interpretability.
Coronary Heart-disease (CHD) is one the foremost causes of death globally, making the accurate recognition of it vital. Machine-learning (ML) and deep-learning (DL) are two of the newest technologies being put to use ...
详细信息
Existing multimodal summarization methods primarily focus on multimodal fusion to efficiently utilize the visual information for summarization. However, they fail to exploit the deep interaction between textual and vi...
详细信息
Druggable proteins are defined as proteins that can interact with drugs to modulate certain biological activity. The identification of druggable proteins holds significant clinical importance, directly impacting the d...
详细信息
ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Druggable proteins are defined as proteins that can interact with drugs to modulate certain biological activity. The identification of druggable proteins holds significant clinical importance, directly impacting the development of targeted therapies for diseases like cancer and metabolic disorders. Identifying druggable proteins involves various methods, including computational prediction models, mass spectrometry (MS), and biochemical assays, but achieving high accuracy remains a challenge. This study proposes DrugEL, an ensemble learning model that uses Bayesian inference to integrate predictions from multiple algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), LightGBM (LGBM), and Decision Tree (DT) with seven feature extraction methods (LSA, AAC, PAAC, GAAC, NMBroto, AAIndex, and KNN). The results show that DrugEL outperforms existing models in terms of accuracy (0.9758), MCC (0.9515), AUC (0.9758), sensitivity (0.9742) and specificity (0.9774), particularly excelling with the LSA method.
With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to...
详细信息
Text-Image Person Re-Identification (TIReID) is a computer vision task that involves identifying person in images or videos based on textual descriptions. Current works mainly employ Vision Language Pretrained (VLP) m...
详细信息
Deep learning methods have been successfully applied to the tasks of predicting functional genomic elements such as histone marks, transcriptions factor binding sites, non-B DNA structures, and regulatory variants. In...
详细信息
暂无评论