A new growing pruning deep learning neural network algorithm (GP-DLNN)

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.

Extraction of time-related expressions using text mining with application to Hebrew

In this research, we extract time-related expressions from a rabbinic text in a semi-automatic manner. These expressions usually appear next to rabbinic references (name / nickname / acronym / book-name). The first step toward our goal is to find all the expressions near references in the corpus. However, not all of the phrases around the references are time-related expressions. Therefore, these phrases are initially considered to be potential time-related expressions. To extract the time-related expressions, we formulate two new statistical functions, and we use screening and heuristic methods. We tested these statistical functions, grammatical screenings, and heuristic methods on a corpus containing responsa documents. In this corpus, many rabbinic citations are known and marked. The statistical functions and the screening methods filtered the potential time-related expressions and reduced 99.88% of the initial expressions (from 484,681 to 575).

Contributions by metaplasticity to solving the Catastrophic Forgetting Problem

Catastrophic forgetting (CF) refers to the sudden and severe loss of prior information in learning systems when acquiring new information. CF has been an Achilles heel of standard artificial neural networks (ANNs) when learning multiple tasks sequentially. The brain, by contrast, has solved this problem during evolution. Modellers now use a variety of strategies to overcome CF, many of which have parallels to cellular and circuit functions in the brain. One common strategy, based on metaplasticity phenomena, controls the future rate of change at key connections to help retain previously learned information. However, the metaplasticity properties so far used are only a subset of those existing in neurobiology. We propose that as models become more sophisticated, there could be value in drawing on a richer set of metaplasticity rules, especially when promoting continual learning in agents moving about the environment.

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de novo molecule design and discovery in this big data era. The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space. The present review summarizes the role of big data and AI techniques currently being implemented to satisfy the ever-increasing research demands in drug discovery pipelines.

A New Cascade-Correlation Growing Deep Learning Neural Network Algorithm

In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used to solve the actual problem by artificial neural networks as a new architecture and supervised learning algorithm. This process optimizes the architectures of the network which intends to accelerate the learning process and produce better performance in generalization. Many researchers have to date proposed several growing algorithms to optimize the feedforward neural network architectures. The proposed algorithm has been tested on various medical data sets. The results prove that the proposed algorithm is a better method to evaluate the accuracy and flexibility resulting from it.

Back propagation neural network model for medical expenses in patients with breast cancer

OBJECTIVE

Breast cancer seriously endangers women's life and health, and brings huge economic burden to the family and society. The aim of this study was to analyze the medical expenses and influencing factors of breast cancer patients, and provide theoretical basis for reasonable control of medical expenses of breast cancer patients.

METHODS

The medical expenses and related information of all female breast cancer patients diagnosed in our hospitals from 2017 to 2019 were collected. Through SSPS Clementine 12.0 software, the back propagation (BP) neural network model and multiple linear regression model were constructed respectively, and the influencing factors of medical expenses of breast cancer patients in the two models were compared.

RESULTS

In the study of medical expenses of breast cancer patients, the prediction error of BP neural network model is less than that of multiple linear regression model. At the same time, the results of the two models showed that the length of stay and region were the top two factors affecting the medical expenses of breast cancer patients.

CONCLUSION

Compared with multiple linear regression model, BP neural network model is more suitable for the analysis of medical expenses in patients with breast cancer.

Deep Learning in the Biomedical Applications: Recent and Future Status

Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.