RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery

The extraction of sequence patterns from a collection of functionally linked unlabeled DNA sequences is known as DNA motif discovery, and it is a key task in computational biology. Several deep learning-based techniques have recently been introduced to address this issue. However, these algorithms can not be used in real-world situations because of the need for labeled data. Here, we presented RL-MD, a novel reinforcement learning based approach for DNA motif discovery task. RL-MD takes unlabelled data as input, employs a relative information-based method to evaluate each proposed motif, and utilizes these continuous evaluation results as the reward. The experiments show that RL-MD can identify high-quality motifs in real-world data.Comment: This paper is accepted by DSAA2022. The 9th IEEE International Conference on Data Science and Advanced Analytic

Machine Unlearning Method Based On Projection Residual

Machine learning models (mainly neural networks) are used more and more in real life. Users feed their data to the model for training. But these processes are often one-way. Once trained, the model remembers the data. Even when data is removed from the dataset, the effects of these data persist in the model. With more and more laws and regulations around the world protecting data privacy, it becomes even more important to make models forget this data completely through machine unlearning. This paper adopts the projection residual method based on Newton iteration method. The main purpose is to implement machine unlearning tasks in the context of linear regression models and neural network models. This method mainly uses the iterative weighting method to completely forget the data and its corresponding influence, and its computational cost is linear in the feature dimension of the data. This method can improve the current machine learning method. At the same time, it is independent of the size of the training set. Results were evaluated by feature injection testing (FIT). Experiments show that this method is more thorough in deleting data, which is close to model retraining.Comment: This paper is accepted by DSAA2022. The 9th IEEE International Conference on Data Science and Advanced Analytic

Blur the Linguistic Boundary: Interpreting Chinese Buddhist Sutra in English via Neural Machine Translation

Buddhism is an influential religion with a long-standing history and profound philosophy. Nowadays, more and more people worldwide aspire to learn the essence of Buddhism, attaching importance to Buddhism dissemination. However, Buddhist scriptures written in classical Chinese are obscure to most people and machine translation applications. For instance, general Chinese-English neural machine translation (NMT) fails in this domain. In this paper, we proposed a novel approach to building a practical NMT model for Buddhist scriptures. The performance of our translation pipeline acquired highly promising results in ablation experiments under three criteria.Comment: This paper is accepted by ICTAI 2022. The 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI

Immunotherapy: Review of the Existing Evidence and Challenges in Breast Cancer

Breast cancer (BC) is a representative malignant tumor that affects women across the world, and it is the main cause of cancer-related deaths in women. Although a large number of treatment methods have been developed for BC in recent years, the results are sometimes unsatisfying. In recent years, treatments of BC have been expanded with immunotherapy. In our article, we list some tumor markers related to immunotherapy for BC. Moreover, we introduce the existing relatively mature immunotherapy and the markers’ pathogenesis are involved. The combination of immunotherapy and other therapies for BC are introduced in detail, including the combination of immunotherapy and chemotherapy, the combined use of immunosuppressants and chemotherapy drugs, immunotherapy and molecular targeted therapy. We summarize the clinical effects of these methods. In addition, this paper also makes a preliminary exploration of the combination of immunotherapy, radiotherapy, and nanotechnology for BC

Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China

Studying the spatiotemporal evolution of carbon emissions from the perspective of major function-oriented zones (MFOZs) is crucial for making a carbon reduction policy. However, most previous research has ignored the spatial characteristics and MFOZ influence. Using statistical and spatial analysis tools, we explored the spatiotemporal characteristics of carbon emissions in Guangdong Province from 2001 to 2021. The following results were obtained: (1) Carbon emissions fluctuated from 2020 to 2021 because of COVID-19. (2) Over the last 20 years, the proportion of carbon emissions from urbanization development zones (UDZs) has gradually decreased, whereas those of the main agricultural production zones (MAPZs) and key ecological function zones (KEFZs) have increased. (3) Carbon emissions efficiency differed significantly among the three MFOZs. (4) Carbon emissions from coastal UDZs were increasingly apparent; however, the directional characteristics of MAPZ and KEFZ emissions were not remarkable. (5) Carbon transfer existed among the three kinds of MFOZs, resulting in the economy and carbon emissions being considerably misaligned across Guangdong Province. These results indicated that the MFOZ is noteworthy in revealing how carbon emissions evolved. Furthermore, spatiotemporal characteristics, especially spatial characteristics, can help formulate carbon reduction policies for realizing carbon peak and neutrality goals in Guangdong Province