5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.

Evaluation of different computational methods on 5-methylcytosine sites identification.

Recently, language representation models have drawn a lot of attention in the natural language processing field due to their remarkable results. Among them, bidirectional encoder representations from transformers (BERT) has proven to be a simple, yet powerful language model that achieved novel state-of-the-art performance. BERT adopted the concept of contextualized word embedding to capture the semantics and context of the words in which they appeared. In this study, we present a novel technique by incorporating BERT-based multilingual model in bioinformatics to represent the information of DNA sequences. We treated DNA sequences as natural sentences and then used BERT models to transform them into fixed-length numerical matrices. As a case study, we applied our method to DNA enhancer prediction, which is a well-known and challenging problem in this field. We then observed that our BERT-based features improved more than 5-10% in terms of sensitivity, specificity, accuracy and Matthews correlation coefficient compared to the current state-of-the-art features in bioinformatics. Moreover, advanced experiments show that deep learning (as represented by 2D convolutional neural networks; CNN) holds potential in learning BERT features better than other traditional machine learning techniques. In conclusion, we suggest that BERT and 2D CNNs could open a new avenue in biological modeling using sequence information.

A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information

Cybersecurity has become an emerging challenge for business information management in recent years. Artificial Intelligence (AI) is widely used in different field, but it is still relatively new in cybersecurity. However, the applications in cybersecurity is crucial for everyone’s daily life. In this paper, we introduce the current state of AI in cybersecurity field, and then describe several case studies and applications of AI to help the community including engineering managers and leaders, researchers, educators, innovators, entrepreneurs, and students to better understand this field, such as the challenges and unresolved issues of AI in cybersecurity. Managerial implications and policy recommendations are provided for business and government.

Survey of AI in Cybersecurity for Information Technology Management

Enhancers are deoxyribonucleic acid (DNA) fragments which when bound by transcription factors enhance the transcription of related genes. Due to its sporadic distribution and similar fractions, identification of enhancers from the human genome seems a daunting task. Compared to the traditional experimental approaches, computational methods with easy-to-use platforms could be efficiently applied to annotate enhancers' functions and physiological roles. In this aspect, several bioinformatics tools have been developed to identify enhancers. Despite their spectacular performances, existing methods have certain drawbacks and limitations, including fixed length of sequences being utilized for model development and cell-specificity negligence. A novel predictor would be beneficial in the context of genome-wide enhancer prediction by addressing the above-mentioned issues. In this study, we constructed new datasets for eight different cell types. Utilizing these data, we proposed an integrative machine learning (ML)-based framework called Enhancer-IF for identifying cell-specific enhancers. Enhancer-IF comprehensively explores a wide range of heterogeneous features with five commonly used ML methods (random forest, extremely randomized tree, multilayer perceptron, support vector machine and extreme gradient boosting). Specifically, these five classifiers were trained with seven encodings and obtained 35 baseline models. The output of these baseline models was integrated and again inputted to five classifiers for the construction of five meta-models. Finally, the integration of five meta-models through ensemble learning improved the model robustness. Our proposed approach showed an excellent prediction performance compared to the baseline models on both training and independent datasets in different cell types, thus highlighting the superiority of our approach in the identification of the enhancers. We assume that Enhancer-IF will be a valuable tool for screening and identifying potential enhancers from the human DNA sequences.

Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.

The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.

/pdf/protein-sequence-to-structure-learning-is-this-the-end-to-2xpc6l1mqw.pdf

Protein sequence-to-structure learning: Is this the end(-to-end revolution)?

Artificial Intelligence (AI) technologies have been successfully applied to many fields, such as object detection and speech recognition. Among these applications, few consider cases where some feature values are missing or unreliable, such as in the prediction of job-hopping patterns where many profiles are incomplete, even though these missing features may be important for businesses (e.g. human resources and management). In this paper, we propose a novel AI technology, Sequential Optimization of Naive Bayesian (SONB), which not only makes predictions, but also learns the underlying pattern and automatically estimates missing or unreliable feature values. We analyzed several important job-hopping features and applied it to predict job-hopping patterns on many incomplete profiles. Our experiment shows SONB accurately estimates missing values and achieves state-of-the-art performance. In addition, the accuracy of deep learning is improved by 3% on the new dataset generated by SONB over the raw data. In summary, we introduce a novel AI technology for forecasting, which could also be used to estimate missing values in the input data. It is applied to a large (20,185,365 employee profiles) dataset and successfully predicts job-hopping patterns for employees based on their profiles, which could be a valuable resource for businesses.

Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping

Purpose: The aim of this study is to investigate the effects of machine learning algorithms in predicting and optimizing the properties of new materials in the United States. 
Materials and Methods: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low-cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. 
Results: The research found that machine learning algorithms have a significant impact on materials prediction and optimization in the United States, particularly in energy storage, catalysis, electronics, and aerospace. These algorithms offer advantages in efficiency, scalability, and accuracy compared to traditional methods, but challenges such as data quality, scarcity, interpretability, and reliability need to be addressed to ensure robust and reliable predictions. 
Recommendations:  This study contributes to the understanding of the effects of machine learning algorithms in predicting and optimizing the properties of new materials in the United States. The research advances the knowledge in the field of materials science, materials prediction, and materials optimization. The findings provide insights into the potential of machine learning algorithms for accelerating materials discovery and innovation, and highlight the challenges and opportunities in their application for materials prediction and optimization. The study has practical implications for researchers, engineers, and policymakers involved in materials science, materials design, and materials innovation. The research underscores the importance of leveraging machine learning algorithms as a powerful tool for materials prediction and optimization, and emphasizes the need for further research, development, and integration of these techniques in materials science and engineering practices. 
 

/pdf/effects-of-machine-learning-algorithms-for-predicting-and-1mfwg3yn.pdf

Effects of Machine Learning Algorithms for Predicting and Optimizing the Properties of New Materials in the United States

Correctly predicting the complex three-dimensional structure of a protein from its sequence would allow for a superior understanding of the function of specific proteins with many applications. We propose a novel method aimed to tackle a crucial step in the protein prediction problem, assessing the quality of generated predictions. Unlike traditional methods, our method, to the best of our knowledge, is the first to analyse the topology of the predicted structure. We found that our new representation provided accurate information regarding the location of the protein's backbone. Using this information, we implemented a novel algorithm based on convolutional neural network (CNN) to predict GDT_TS score for given protein models. Our method has shown promising results - overall correlation of 0.41 on CASP12 dataset. Future work will aim to implement additional features into our representation. The software is freely available at GitHub: https://github.com/caorenzhi/TopQA.

TopQA: a topological representation for single-model protein quality assessment with machine learning

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Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping

Effects of Machine Learning Algorithms for Predicting and Optimizing the Properties of New Materials in the United States

TopQA: a topological representation for single-model protein quality assessment with machine learning