Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory

Huang, Chien Hung; Chang, Peter Mu Hsin; Hsu, Chia Wei; Huang, Chi Ying F.; Ng, Ka-Lok

doi:10.1186/s12859-015-0845-0

Cited by 28 publications

(15 citation statements)

References 59 publications

(74 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since then, several advanced computational methods have been applied to formulate and validate drug repositioning hypotheses [373][374][375]. Using supervised learning and collaborative filtering to tackle this type of problem is proving successful, especially when coupling disease or compound omic data with topological information from protein-protein or protein-compound interaction networks [376][377][378].…”

Section: Drug Repositioningmentioning

confidence: 99%

Opportunities and obstacles for deep learning in biology and medicine

Ching

Himmelstein

Beaulieu‐Jones

et al. 2018

J. R. Soc. Interface.

1,570

1,001

View full text Add to dashboard Cite

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

show abstract

Section: Drug Repositioningmentioning

confidence: 99%

Opportunities and obstacles for deep learning in biology and medicine

Ching

Himmelstein

Beaulieu‐Jones

et al. 2018

J. R. Soc. Interface.

1,570

1,001

View full text Add to dashboard Cite

show abstract

“…In recent years, the use of machine learning has gained much importance in the field of clinical study [14,15]. Recently, many researchers and clinicians have proposed the use of machine learning in the field of colorectal cancer [16][17][18] and other types of cancers [19][20][21][22][23][24]. For instance, in [25] authors used ML for prostate cancer screening and have determined the impact of few variables such as rate of change of prostate-specific antigen (PSA), age, BMI, and race on the model's accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach

Gupta

Chiang

Sahoo

et al. 2019

Cancers

View full text Add to dashboard Cite

The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor. Performances of different ML algorithms were evaluated using five-fold cross-validation, which is an effective way of the model validation. The accuracy achieved by the algorithms taking both cases of standard TNM staging and TNM staging with the Tumor Aggression Score was determined. It was observed that the Random Forest model achieved an F-measure of 0.89, when the Tumor Aggression Score was considered as an attribute along with the standard attributes normally used for the TNM stage prediction. We also found that the Random Forest algorithm outperformed all other algorithms, with an accuracy of approximately 84% and an area under the curve (AUC) of 0.82 ± 0.10 for predicting the five years DFS.

show abstract

“…36a In 2014, they further reported the identification of raloxifene and bazedoxifene as novel inhibitors of IL-6/GP130 protein-protein interaction through MLSD-derived drug-repositioning methodology. 36b Novel in silico drug design approaches, especially those related to machine-learning algorithms, are being utilized for in silico drug repositioning, as exemplified by drug profile matching, 37 topological graph theory 38 and other computational methodologies. [39][40][41] Because of the limited variety of drugs on the market, it is necessary to expand the selection of drugs or drug-like molecules (investigational compounds).…”

Section: New Developments On Drug Repurposingmentioning

confidence: 99%