LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer

TP63-TRIM29 axis regulates enhancer methylation and chromosomal instability in prostate cancer

BACKGROUND

Prostate adenocarcinoma (PRAD) is the second leading cause of cancer-related deaths in men. High variability in DNA methylation and a high rate of large genomic rearrangements are often observed in PRAD.

RESULTS

To investigate the reasons for such high variance, we integrated DNA methylation, RNA-seq, and copy number alterations datasets from The Cancer Genome Atlas (TCGA), focusing on PRAD, and employed weighted gene co-expression network analysis (WGCNA). Our results show that only single cluster of co-expressed genes is associated with genomic and epigenomic instability. Within this cluster, TP63 and TRIM29 are key transcription regulators and are downregulated in PRAD. We discovered that TP63 regulates the level of enhancer methylation in prostate basal epithelial cells. TRIM29 forms a complex with TP63 and together regulates the expression of genes specific to the prostate basal epithelium. In addition, TRIM29 binds DNA repair proteins and prevents the formation of the TMPRSS2:ERG gene fusion typically observed in PRAD.

CONCLUSION

Our study demonstrates that TRIM29 and TP63 are important regulators in maintaining the identity of the basal epithelium under physiological conditions. Furthermore, we uncover the role of TRIM29 in PRAD development.

Landslide Susceptibility Mapping with Deep Learning Algorithms

Among natural hazards, landslides are devastating in China. However, little is known regarding potential landslide-prone areas in Maoxian County. The goal of this study was to apply four deep learning algorithms, the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) networks, and recurrent neural network (RNN) in evaluating the possibility of landslides throughout Maoxian County, Sichuan, China. A total of 1290 landslide records was developed using historical records, field observations, and remote sensing techniques. The landslide susceptibility maps showed that most susceptible areas were along the Minjiang River and in some parts of the southeastern portion of the study area. Slope, rainfall, and distance to faults were the most influential factors affecting landslide occurrence. Results revealed that proportion of landslide susceptible areas in Maoxian County was as follows: identified landslides (13.65–23.71%) and non-landslides (76.29–86.35%). The resultant maps were tested against known landslide locations using the area under the curve (AUC). This study indicated that the DNN algorithm performed better than LSTM, CNN, and RNN in identifying landslides in Maoxian County, with AUC values (for prediction accuracy) of 87.30%, 86.50%, 85.60%, and 82.90%, respectively. The results of this study are useful for future landslide risk reduction along with devising sustainable land use planning in the study area.

Machine Learning and Deep Learning based AI Tools for Development of Diagnostic Tools

Artificial intelligence (AI) systems exhibit human-like intelligence. Human intelligence is converted to machines or computer technologies using AI algorithms. Machine learning (ML) is a subset of AI that can learn from extracted data and models to perform a task whereas deep learning (DL) is a subset of ML that imitates the human brain functioning to solve real-world problems in almost all fields. AI caused a paradigm shift in healthcare that can be employed for decision support and forecasting. Medical diagnostic tools developed using AI, perform disease diagnosis based on the symbolic models of disease and provide therapy recommendations. The key AI applications employed with medical diagnosis are characterized as learning systems and expert systems. Diagnostic tools, developed using Expert systems utilize facts, implications, and knowledge processing techniques for disease diagnosis, whereas a learning system utilizes statistical pattern recognition, ML, and neural networks. In March 2020, an infectious disease caused by the severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) virus, Coronavirus disease-2019 (COVID-19) was declared a pandemic by the World Health Organization. Recent research studies have shown that AI, ML, and DL can be leveraged to combat COVID-19 having objectives of disease diagnosis, to forecast epidemic and sustainable development, and so on. DL algorithms are implemented on image data, more specifically on chest X-rays and computed tomography scans, for developing diagnostic tools. In this chapter, various ML and DL-based AI tools for the development of diagnostic tools have been discussed.

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.