Artificial intelligence in clinical research of cancers

Shao, Dan; Dai, Yinfei; Li, Nianfeng; Cao, Xuqing; Zhao, Wei; Cheng, Li; RONG, ZHUQING; Huang, Lan; Wang, Yan; Zhao, Jing

doi:10.1093/bib/bbab523

Cited by 28 publications

(16 citation statements)

References 103 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We are also actively conducting clinical tests. Thus, its purpose is mainly to be used as a reference when making clinical decisions [22,23]. At present, the use of artificial intelligence in medicine is still in its infancy.…”

Section: Discussionmentioning

confidence: 99%

Early warning model of recurrence site for breast cancer rehabilitation patients based on adaptive mesh optimization XGBoost

2022

European Journal of Gynaecological Oncology

View full text Add to dashboard Cite

Objective: Breast cancer is a malignant disease with a high mortality rate. Using postoperative rehabilitation data of breast cancer patients, this study explored the effects of immune, tumor, microenvironmental, psychological, nutritional, aerobic exercise and advanced work indexes on the rehabilitation of breast cancer patients. To determine the weight of the impact of different indications on cancer recovery. By combining the adaptive grid optimization algorithm with the XGBoost (Extreme Gradient Boosting) algorithm, an intelligent prediction model for breast cancer rehabilitation was constructed using patients indexes as input and the recurrence location as the output. Our results showed that the model constructed in this study could effectively predict cancer cell metastasis during breast cancer recurrence in recovered patients. Compared with artificial intelligence algorithm models such as neural network algorithm, support vector machine algorithm, gradient boosting tree algorithm and Adaboost, the model demonstrated a forecast accuracy rate of >93%. The model established in this study could effectively predict the recurrence position of breast cancer and provide an auxiliary reference for doctors to treat breast cancer patients more effectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Early warning model of recurrence site for breast cancer rehabilitation patients based on adaptive mesh optimization XGBoost

2022

European Journal of Gynaecological Oncology

View full text Add to dashboard Cite

show abstract

“…DeepChem, DeepTox, gene2drug, STITCH, AlphaFold, DeepNeuralNetQSAR, and so on are a few examples of the applications. For example, the Deep Tox algorithm computationally predicts 12,000 medicines and environmental contaminants for 12 different harmful effects in assays that are specially constructed [41].…”

Section: Ai For Cancer Treatmentmentioning

confidence: 99%

“…Integrated knowledge can improve the accuracy of AI research on specific cancer-related medical occurrences while also enabling the interpretability of AI models. An emerging trend is explainable AI and interpretable DL [41]. Explainable AI explains a model's functioning, strengths and weaknesses, likely behaviour, and potential biases to a specific audience while enabling accuracy, fairness, accountability, stability, and transparency in decision making.…”

Section: Future Directionsmentioning

confidence: 99%

“…Collaborations between molecular biologists who can offer fresh experimental-model datasets and domain knowledge are highly feasible [40]. Genome wide association studies (GWAS) have effectively found genetic variations interacting to increase the risk of cancer [41]. Models based on single data types risk missing significant predictive information originating from the interaction of interdependent biological systems due to the complexity of cancer biology.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

Sebastian

Peter

2022

Life

View full text Add to dashboard Cite

The World Health Organization (WHO), in their 2022 report, identified cancer as one of the leading causes of death, accounting for about 16% of deaths worldwide. The Cancer-Moonshot community aims to reduce the cancer death rate by half in the next 25 years and wants to improve the lives of cancer-affected people. Cancer mortality can be reduced if detected early and treated appropriately. Cancers like breast cancer and cervical cancer have high cure probabilities when treated early in accordance with best practices. Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies. However, many of these AI tools will remain ineffective in fighting against cancer and saving the lives of millions of patients unless they are accessible and understandable to biologists, oncologists, and other medical cancer researchers. This paper presents the trends, challenges, and future directions of AI in cancer research. We hope that this paper will be of help to both medical experts and technical experts in getting a better understanding of the challenges and research opportunities in cancer diagnosis and treatment.

show abstract

“…Nowadays, the availability of many public databases provides powerful tools that facilitate research in clinical body-fluid proteomics [6]. For instance, the human bodyfluid proteome (HBFP) database (https://bmbl.bmi.osumc.edu/HBFP/, accessed on 15 November 2021) [7], our previous research, focuses on experimentally validated proteome and archives more than 11,000 unique proteins from 17 types of human body fluids.…”

Section: Introductionmentioning

confidence: 99%

DenSec: Secreted Protein Prediction in Cerebrospinal Fluid Based on DenseNet and Transformer

Huang

et al. 2022

Mathematics

Self Cite

View full text Add to dashboard Cite

Cerebrospinal fluid (CSF) exists in the surrounding spaces of mammalian central nervous systems (CNS); therefore, there are numerous potential protein biomarkers associated with CNS disease in CSF. Currently, approximately 4300 proteins have been identified in CSF by protein profiling. However, due to the diverse modifications, as well as the existing technical limits, large-scale protein identification in CSF is still considered a challenge. Inspired by computational methods, this paper proposes a deep learning framework, named DenSec, for secreted protein prediction in CSF. In the first phase of DenSec, all input proteins are encoded as a matrix with a fixed size of 1000 × 20 by calculating a position-specific score matrix (PSSM) of protein sequences. In the second phase, a dense convolutional network (DenseNet) is adopted to extract the feature from these PSSMs automatically. After that, Transformer with a fully connected dense layer acts as classifier to perform a binary classification in terms of secretion into CSF or not. According to the experiment results, DenSec achieves a mean accuracy of 86.00% in the test dataset and outperforms the state-of-the-art methods.

show abstract

Artificial intelligence in clinical research of cancers

Cited by 28 publications

References 103 publications

Early warning model of recurrence site for breast cancer rehabilitation patients based on adaptive mesh optimization XGBoost

Early warning model of recurrence site for breast cancer rehabilitation patients based on adaptive mesh optimization XGBoost

Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

DenSec: Secreted Protein Prediction in Cerebrospinal Fluid Based on DenseNet and Transformer

Contact Info

Product

Resources

About