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Ren S, Li J, Dorado J, Sierra A, González-Díaz H, Duardo A, Shen B. From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine. Health Inf Sci Syst 2024; 12:6. [PMID: 38125666 PMCID: PMC10728428 DOI: 10.1007/s13755-023-00264-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.
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Affiliation(s)
- Shumin Ren
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
| | - Jiakun Li
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
| | - Julián Dorado
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
| | - Alejandro Sierra
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Humbert González-Díaz
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Aliuska Duardo
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Bairong Shen
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
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2
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Yang X, Tang X, Li C, Han H. Singular value thresholding two-stage matrix completion for drug sensitivity discovery. Comput Biol Chem 2024; 110:108071. [PMID: 38718497 DOI: 10.1016/j.compbiolchem.2024.108071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 04/06/2024] [Accepted: 04/11/2024] [Indexed: 05/27/2024]
Abstract
Incomplete data presents significant challenges in drug sensitivity analysis, especially in critical areas like oncology, where precision is paramount. Our study introduces an innovative imputation method designed specifically for low-rank matrices, addressing the crucial challenge of data completion in anticancer drug sensitivity testing. Our method unfolds in two main stages: Initially, the singular value thresholding algorithm is employed for preliminary matrix completion, establishing a solid foundation for subsequent steps. Then, the matrix rows are segmented into distinct blocks based on hierarchical clustering of correlation coefficients, applying singular value thresholding to the largest block, which has been proved to possess the largest entropy. This is followed by a refined data restoration process, where the reconstructed largest block is integrated into the initial matrix completion to achieve the final matrix completion. Compared to other methods, our approach not only improves the accuracy of data restoration but also ensures the integrity and reliability of the imputed values, establishing it as a robust tool for future drug sensitivity analysis.
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Affiliation(s)
- Xuemei Yang
- School of Mathematics and Statistics, Xianyang Normal University, Xianyang, 712000, China.
| | - Xiaoduan Tang
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China.
| | - Chun Li
- College of Elementary Education, Hainan Normal University, Haikou 571158, China; Key Laboratory of Data Science and Intelligence Education of Ministry of Education, Hainan Normal University, Haikou 571158, China.
| | - Henry Han
- The Laboratory of Data Science and Artificial Intelligence Innovation, Department of Computer Science, School of Engineering and Computer Science, Baylor University, Waco, TX 76798 USA.
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3
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Yan D, Sun Z, Fang J, Cao S, Wang W, Chang X, Badirli S, Fu H, Liu Y. scRAA: the development of a robust and automatic annotation procedure for single-cell RNA sequencing data. J Biopharm Stat 2023:1-14. [PMID: 37162278 DOI: 10.1080/10543406.2023.2208671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
A critical task in single-cell RNA sequencing (scRNA-Seq) data analysis is to identify cell types from heterogeneous tissues. While the majority of classification methods demonstrated high performance in scRNA-Seq annotation problems, a robust and accurate solution is desired to generate reliable outcomes for downstream analyses, for instance, marker genes identification, differentially expressed genes, and pathway analysis. It is hard to establish a universally good metric. Thus, a universally good classification method for all kinds of scenarios does not exist. In addition, reference and query data in cell classification are usually from different experimental batches, and failure to consider batch effects may result in misleading conclusions. To overcome this bottleneck, we propose a robust ensemble approach to classify cells and utilize a batch correction method between reference and query data. We simulated four scenarios that comprise simple to complex batch effect and account for varying cell-type proportions. We further tested our approach on both lung and pancreas data. We found improved prediction accuracy and robust performance across simulation scenarios and real data. The incorporation of batch effect correction between reference and query, and the ensemble approach improve cell-type prediction accuracy while maintaining robustness. We demonstrated these through simulated and real scRNA-Seq data.
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Affiliation(s)
- Dongyan Yan
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Zhe Sun
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Jiyuan Fang
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Shanshan Cao
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Wenjie Wang
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Xinyue Chang
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Sarkhan Badirli
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Haoda Fu
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Yushi Liu
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
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4
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Singh DP, Kaushik B. CTDN (Convolutional Temporal Based Deep- Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction. Comput Biol Chem 2023; 105:107868. [PMID: 37257399 DOI: 10.1016/j.compbiolchem.2023.107868] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 06/02/2023]
Abstract
The characterization of drug - metabolizing enzymes is a significant problem for customized therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast how those drugs will react is usually based on the available information, genetic sequence, and structural properties. To the finest of our knowledge, this is the first study to evaluate optimization algorithms for selection of features and pharmacogenetics categorization using classification methods based on a successful evolutionary algorithm using datasets from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC). The study proposes the uses of Firefly and Grey Wolf Optimization techniques for feature extraction, while comparing the traditional Machine Learning (ML), ensemble ML and Stacking Algorithm with the proposed Convolutional Temporal Deep Neural Network or CTDN. With the potential to increase efficiency from the suggested intelligible classifier model for a suggestive chemotherapeutic drugs response prediction, our study is important in particular for selecting an acceptable feature selection method. The comparison analysis demonstrates that the proposed model not only surpasses the prior state-of-the-art methods, but also uses Grey Wolf and Fire Fly Optimization to lessen multicollinearity and overfitting.
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Affiliation(s)
- Davinder Paul Singh
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra 182320, Jammu and Kashmir, India.
| | - Baijnath Kaushik
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra 182320, Jammu and Kashmir, India
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5
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Petinrin OO, Saeed F, Toseef M, Liu Z, Basurra S, Muyide IO, Li X, Lin Q, Wong KC. Machine Learning in Metastatic Cancer Research: Potentials, Possibilities, and Prospects. Comput Struct Biotechnol J 2023; 21:2454-2470. [PMID: 37077177 PMCID: PMC10106342 DOI: 10.1016/j.csbj.2023.03.046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
Cancer has received extensive recognition for its high mortality rate, with metastatic cancer being the top cause of cancer-related deaths. Metastatic cancer involves the spread of the primary tumor to other body organs. As much as the early detection of cancer is essential, the timely detection of metastasis, the identification of biomarkers, and treatment choice are valuable for improving the quality of life for metastatic cancer patients. This study reviews the existing studies on classical machine learning (ML) and deep learning (DL) in metastatic cancer research. Since the majority of metastatic cancer research data are collected in the formats of PET/CT and MRI image data, deep learning techniques are heavily involved. However, its black-box nature and expensive computational cost are notable concerns. Furthermore, existing models could be overestimated for their generality due to the non-diverse population in clinical trial datasets. Therefore, research gaps are itemized; follow-up studies should be carried out on metastatic cancer using machine learning and deep learning tools with data in a symmetric manner.
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Singh DP, Kaushik B. A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques. Chem Biol Drug Des 2023; 101:175-194. [PMID: 36303299 DOI: 10.1111/cbdd.14164] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/13/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.
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Affiliation(s)
- Davinder Paul Singh
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Baijnath Kaushik
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
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7
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Utilization of Cancer Cell Line Screening to Elucidate the Anticancer Activity and Biological Pathways Related to the Ruthenium-Based Therapeutic BOLD-100. Cancers (Basel) 2022; 15:cancers15010028. [PMID: 36612025 PMCID: PMC9817855 DOI: 10.3390/cancers15010028] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/30/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
BOLD-100 (sodium trans-[tetrachlorobis(1H indazole)ruthenate(III)]) is a ruthenium-based anticancer compound currently in clinical development. The identification of cancer types that show increased sensitivity towards BOLD-100 can lead to improved developmental strategies. Sensitivity profiling can also identify mechanisms of action that are pertinent for the bioactivity of complex therapeutics. Sensitivity to BOLD-100 was measured in a 319-cancer-cell line panel spanning 24 tissues. BOLD-100's sensitivity profile showed variation across the tissue lineages, including increased response in esophageal, bladder, and hematologic cancers. Multiple cancers, including esophageal, bile duct and colon cancer, had higher relative response to BOLD-100 than to cisplatin. Response to BOLD-100 showed only moderate correlation to anticancer compounds in the Genomics of Drug Sensitivity in Cancer (GDSC) database, as well as no clear theme in bioactivity of correlated hits, suggesting that BOLD-100 may have a differentiated therapeutic profile. The genomic modalities of cancer cell lines were modeled against the BOLD-100 sensitivity profile, which revealed that genes related to ribosomal processes were associated with sensitivity to BOLD-100. Machine learning modeling of the sensitivity profile to BOLD-100 and gene expression data provided moderative predictive value. These findings provide further mechanistic understanding around BOLD-100 and support its development for additional cancer types.
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Alqahtani A. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:6201067. [PMID: 35509623 PMCID: PMC9060979 DOI: 10.1155/2022/6201067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/17/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022]
Abstract
Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.
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Affiliation(s)
- Amal Alqahtani
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, 31541, Saudi Arabia
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
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9
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Automatic identification of drug sensitivity of cancer cell with novel regression-based ensemble convolution neural network model. Soft comput 2022. [DOI: 10.1007/s00500-022-07098-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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10
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Shahzad M, Tahir MA, Khan MA, Jiang R, Malick RAS. EBSRMF: Ensemble based similarity-regularized matrix factorization to predict anticancer drug responses. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Drug sensitivity prediction to a panel of cancer cell lines using computational approaches has been a challenge for two decades. With the emergence of high-throughput screening technologies, thousands of compounds and cancer cell lines panels with drug sensitivity data are publicly available at various pharmacogenomics databases. Analyzing these data is crucial to improve cancer treatment and develop new anticancer drugs. In this work, we propose EBSRMF: Ensemble Based Similarity-Regularized Matrix Factorization, which is a bagging based framework to improve the drug sensitivity prediction on the Cancer Cell Line Encyclopedia (CCLE) data. Based on the fact that similar drugs and cell lines exhibit similar drug response, we have investigated cell line and drug similarity matrices based on gene expression profiles and chemical structure respectively. The drug sensitivity value is used as outcome values which are the half maximal inhibitory concentrations (IC50). In order to improve the generalization ability of the proposed model, a homogeneous ensemble based bagging learning approach is also investigated where multiple SRMF models are used to train N subsets of the input data. The outcome of each training algorithm is aggregated using the averaging method to predict the outcome. Experiments are conducted on two benchmark datasets: CCLE and GDSC. The proposed model is compared with state-of-the-art models using multiple evaluation metrics including Root Means Square Error (RMSE) and Pearson Correlation Coefficient (PCC). The proposed model is quite promising and achieves better performance on CCLE dataset when compared with the existing approaches.
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Affiliation(s)
- Muhammad Shahzad
- School of Computing and Communications, Lancaster University, Lancaster, United Kingdom
- FAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi Campus, Pakistan
| | - M. Atif Tahir
- FAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi Campus, Pakistan
| | - M. Atta Khan
- FAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi Campus, Pakistan
| | - Richard Jiang
- School of Computing and Communications, Lancaster University, Lancaster, United Kingdom
| | - Rauf Ahmed Shams Malick
- FAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi Campus, Pakistan
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11
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Advancements in Oncology with Artificial Intelligence—A Review Article. Cancers (Basel) 2022; 14:cancers14051349. [PMID: 35267657 PMCID: PMC8909088 DOI: 10.3390/cancers14051349] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary With the advancement of artificial intelligence, including machine learning, the field of oncology has seen promising results in cancer detection and classification, epigenetics, drug discovery, and prognostication. In this review, we describe what artificial intelligence is and its function, as well as comprehensively summarize its evolution and role in breast, colorectal, and central nervous system cancers. Understanding the origin and current accomplishments might be essential to improve the quality, accuracy, generalizability, cost-effectiveness, and reliability of artificial intelligence models that can be used in worldwide clinical practice. Students and researchers in the medical field will benefit from a deeper understanding of how to use integrative AI in oncology for innovation and research. Abstract Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.
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12
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Miranda L, Viterbo J, Bernardini F. A survey on the use of machine learning methods in context-aware middlewares for human activity recognition. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10094-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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An X, Chen X, Yi D, Li H, Guan Y. Representation of molecules for drug response prediction. Brief Bioinform 2021; 23:6375515. [PMID: 34571534 DOI: 10.1093/bib/bbab393] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 08/28/2021] [Accepted: 08/30/2021] [Indexed: 12/18/2022] Open
Abstract
The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening, drug toxicity prediction, quantitative structure-activity relationship prediction, anti-cancer synergy score prediction, etc. This review is dedicated to the application of machine learning in drug response prediction. Specifically, we focus on molecular representations, which is a crucial element to the success of drug response prediction and other chemistry-related prediction tasks. We introduce three types of commonly used molecular representation methods, together with their implementation and application examples. This review will serve as a brief introduction of the broad field of molecular representations.
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Affiliation(s)
- Xin An
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Xi Chen
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Daiyao Yi
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Hongyang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
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14
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Hou Z, Lao W, Wang Y, Lu W. Homotopy-based hyper-heuristic searching approach for reciprocal feedback inversion of groundwater contamination source and aquifer parameters. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107191] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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15
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Zhu Y, Brettin T, Evrard YA, Partin A, Xia F, Shukla M, Yoo H, Doroshow JH, Stevens RL. Ensemble transfer learning for the prediction of anti-cancer drug response. Sci Rep 2020; 10:18040. [PMID: 33093487 PMCID: PMC7581765 DOI: 10.1038/s41598-020-74921-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 10/08/2020] [Indexed: 12/13/2022] Open
Abstract
Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used to improve the performance of anti-cancer drug response prediction models. Previous transfer learning studies for drug response prediction focused on building models to predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. Uniquely, we investigate the power of transfer learning for three drug response prediction applications including drug repurposing, precision oncology, and new drug development, through different data partition schemes in cross-validation. We extend the classic transfer learning framework through ensemble and demonstrate its general utility with three representative prediction algorithms including a gradient boosting model and two deep neural networks. The ensemble transfer learning framework is tested on benchmark in vitro drug screening datasets. The results demonstrate that our framework broadly improves the prediction performance in all three drug response prediction applications with all three prediction algorithms.
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Affiliation(s)
- Yitan Zhu
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA.
| | - Thomas Brettin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Yvonne A Evrard
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD, 21702, USA
| | - Alexander Partin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Fangfang Xia
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Maulik Shukla
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Hyunseung Yoo
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - James H Doroshow
- Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Rick L Stevens
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA
- Department of Computer Science, The University of Chicago, Chicago, IL, 60637, USA
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16
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Liang G, Fan W, Luo H, Zhu X. The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed Pharmacother 2020; 128:110255. [DOI: 10.1016/j.biopha.2020.110255] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 04/22/2020] [Accepted: 05/10/2020] [Indexed: 12/12/2022] Open
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