1
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Kapoor S, Kalmegh V, Kumar H, Mandoli A, Shard A. Rare diseases and pyruvate kinase M2: a promising therapeutic connection. Drug Discov Today 2024; 29:103949. [PMID: 38492882 DOI: 10.1016/j.drudis.2024.103949] [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: 09/23/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 03/18/2024]
Abstract
Pyruvate kinase M2 (PKM2) is a key glycolytic enzyme that regulates proliferating cell metabolism. The role of PKM2 in common diseases has been well established, but its role in rare diseases (RDs) is less understood. Over the past few years, PKM2 has emerged as a crucial player in RDs, including, neoplastic, respiratory, metabolic, and neurological disorders. Herein, we summarize recent findings and developments highlighting PKM2 as an emerging key player in RDs. We also discuss the current status of PKM2 modulation in RDs with particular emphasis on preclinical and clinical studies in addition to current challenges in the field.
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Affiliation(s)
- Saumya Kapoor
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research Ahmedabad (NIPER-A), Gandhinagar, Gujarat, India
| | - Vaishnavi Kalmegh
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research Ahmedabad (NIPER-A), Gandhinagar, Gujarat, India
| | - Hemant Kumar
- Department of Pharmacology and Toxicology, NIPER-A, Gandhinagar, Gujarat, India.
| | - Amit Mandoli
- Department of Biotechnology, NIPER-A, Gandhinagar, Gujarat, India.
| | - Amit Shard
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research Ahmedabad (NIPER-A), Gandhinagar, Gujarat, India.
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2
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Waseem T, Rajput TA, Mushtaq MS, Babar MM, Rajadas J. Computational biology approaches for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:91-109. [PMID: 38789189 DOI: 10.1016/bs.pmbts.2024.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.
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Affiliation(s)
- Tanya Waseem
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Tausif Ahmed Rajput
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | | | - Mustafeez Mujtaba Babar
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan; Advanced Drug Delivery and Regenerative Biomaterials Laboratory, Cardiovascular Institute and Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, United States.
| | - Jayakumar Rajadas
- Advanced Drug Delivery and Regenerative Biomaterials Laboratory, Cardiovascular Institute and Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, United States
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3
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Napolitano G, Has C, Schwerk A, Yuan JH, Ullrich C. Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases. Pharmaceut Med 2024; 38:79-86. [PMID: 38315404 DOI: 10.1007/s40290-023-00504-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2023] [Indexed: 02/07/2024]
Abstract
The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a strong focus on the specific achievements and challenges posed by rare diseases. Data paucity and high costs make drug development for rare diseases especially hard. AI can enable otherwise inaccessible approaches based on the large-scale integration of heterogeneous datasets and knowledge bases, guided by expert biological understanding. Obstacles still exist for the routine use of AI in the usually conservative pharmaceutical domain, which can easily become disillusioned. It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.
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Affiliation(s)
| | - Canan Has
- Centogene GmbH, Alboinstraße 36-42, 12103, Berlin, Germany
| | - Anne Schwerk
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jui-Hung Yuan
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Ullrich
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
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4
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Zheng Y, Sun X, Feng B, Kang K, Yang Y, Zhao A, Wu Y. Rare and complex diseases in focus: ChatGPT's role in improving diagnosis and treatment. Front Artif Intell 2024; 7:1338433. [PMID: 38283995 PMCID: PMC10808657 DOI: 10.3389/frai.2024.1338433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/02/2024] [Indexed: 01/30/2024] Open
Abstract
Rare and complex diseases pose significant challenges to both patients and healthcare providers. These conditions often present with atypical symptoms, making diagnosis and treatment a formidable task. In recent years, artificial intelligence and natural language processing technologies have shown great promise in assisting medical professionals in diagnosing and managing such conditions. This paper explores the role of ChatGPT, an advanced artificial intelligence model, in improving the diagnosis and treatment of rare and complex diseases. By analyzing its potential applications, limitations, and ethical considerations, we demonstrate how ChatGPT can contribute to better patient outcomes and enhance the healthcare system's overall effectiveness.
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Affiliation(s)
- Yue Zheng
- Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xu Sun
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Baijie Feng
- West China School of Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Kai Kang
- Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuqi Yang
- West China School of Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Ailin Zhao
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yijun Wu
- Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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5
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Yang X, Huang K, Yang D, Zhao W, Zhou X. Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review. GLOBAL CHALLENGES (HOBOKEN, NJ) 2024; 8:2300163. [PMID: 38223896 PMCID: PMC10784210 DOI: 10.1002/gch2.202300163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/20/2023] [Indexed: 01/16/2024]
Abstract
The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.
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Affiliation(s)
- Xue Yang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Kexin Huang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Dewei Yang
- College of Advanced Manufacturing EngineeringChongqing University of Posts and TelecommunicationsChongqingChongqing400000China
| | - Weiling Zhao
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
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6
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Wojtara M, Rana E, Rahman T, Khanna P, Singh H. Artificial intelligence in rare disease diagnosis and treatment. Clin Transl Sci 2023; 16:2106-2111. [PMID: 37646577 PMCID: PMC10651639 DOI: 10.1111/cts.13619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/30/2023] [Accepted: 08/13/2023] [Indexed: 09/01/2023] Open
Abstract
Artificial intelligence (AI) utilization in health care has grown over the past few years. It also has demonstrated potential in improving the efficiency of diagnosis and treatment. Some types of AI, such as machine learning, allow for the efficient analysis of vast datasets, identifying patterns, and generating key insights. Predictions can then be made for medical diagnosis and personalized treatment recommendations. The use of AI can bypass some conventional limitations associated with rare diseases. Namely, it can optimize traditional randomized control trials, and may eventually reduce costs for drug research and development. Recent advancements have enabled researchers to train models based on large datasets and then fine-tune these models on smaller datasets typically associated with rare diseases. In this mini-review, we discuss recent advancements in AI and how AI can be applied to streamline rare disease diagnosis and optimize treatment.
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Affiliation(s)
- Magda Wojtara
- Department of Human GeneticsUniversity of MichiganAnn ArborMichiganUSA
| | - Emaan Rana
- Department of ScienceUniversity of Western OntarioLondonOntarioCanada
| | - Taibia Rahman
- Department of MedicineDavid Tvildiani Medical UniversityTbilisiGeorgia
| | - Palak Khanna
- Department of MedicineIvane Javakhishvili Tbilisi State UniversityTbilisiGeorgia
| | - Heshwin Singh
- Department of BiologyStony Brook UniversityStony BrookNew YorkUSA
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7
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Abdallah S, Sharifa M, I Kh Almadhoun MK, Khawar MM, Shaikh U, Balabel KM, Saleh I, Manzoor A, Mandal AK, Ekomwereren O, Khine WM, Oyelaja OT. The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders. Cureus 2023; 15:e46860. [PMID: 37954711 PMCID: PMC10636514 DOI: 10.7759/cureus.46860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2023] [Indexed: 11/14/2023] Open
Abstract
Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.
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Affiliation(s)
- Shenouda Abdallah
- Surgery, Jaber Al Ahmad Al Jaber Al Sabah Hospital, Kuwait City, KWT
| | | | | | | | - Unzla Shaikh
- Internal Medicine, Liaquat University of Medical and Health Sciences, Hyderabad, PAK
| | | | - Inam Saleh
- Pediatrics, University of Kentucky College of Medicine, Lexington, USA
| | - Amima Manzoor
- Internal Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Arun Kumar Mandal
- General Medicine, Mahawai Basic Hospital/The Oda Foundation, Kalikot, NPL
- Medicine, Manipal College of Medical Sciences, Pokhara, NPL
| | - Osatohanmwen Ekomwereren
- Trauma and Orthopaedics, Royal Shrewsbury Hospital, Shrewsbury and Telford Hospital NHS Trust, Shrewsbury, GBR
| | - Wai Mon Khine
- Internal Medicine, Caribbean Medical School, St. Georges, GRD
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8
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Zhu C, Xia X, Li N, Zhong F, Yang Z, Liu L. RDKG-115: Assisting drug repurposing and discovery for rare diseases by trimodal knowledge graph embedding. Comput Biol Med 2023; 164:107262. [PMID: 37481946 DOI: 10.1016/j.compbiomed.2023.107262] [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: 05/22/2023] [Revised: 07/07/2023] [Accepted: 07/16/2023] [Indexed: 07/25/2023]
Abstract
Rare diseases (RDs) may affect individuals in small numbers, but they have a significant impact on a global scale. Accurate diagnosis of RDs is challenging, and there is a severe lack of drugs available for treatment. Pharmaceutical companies have shown a preference for drug repurposing from existing drugs developed for other diseases due to the high investment, high risk, and long cycle involved in RD drug development. Compared to traditional approaches, knowledge graph embedding (KGE) based methods are more efficient and convenient, as they treat drug repurposing as a link prediction task. KGE models allow for the enrichment of existing knowledge by incorporating multimodal information from various sources. In this study, we constructed RDKG-115, a rare disease knowledge graph involving 115 RDs, composed of 35,643 entities, 25 relations, and 5,539,839 refined triplets, based on 372,384 high-quality literature and 4 biomedical datasets: DRKG, Pathway Commons, PharmKG, and PMapp. Subsequently, we developed a trimodal KGE model containing structure, category, and description embeddings using reverse-hyperplane projection. We utilized this model to infer 4199 reliable new inferred triplets from RDKG-115. Finally, we calculated potential drugs and small molecules for each of the 115 RDs, taking multiple sclerosis as a case study. This study provides a paradigm for large-scale screening of drug repurposing and discovery for RDs, which will speed up the drug development process and ultimately benefit patients with RDs. The source code and data are available at https://github.com/ZhuChaoY/RDKG-115.
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Affiliation(s)
- Chaoyu Zhu
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Xiaoqiong Xia
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Nan Li
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Fan Zhong
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Zhihao Yang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
| | - Lei Liu
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China; Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, 200120, China.
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9
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Aradhya S, Facio FM, Metz H, Manders T, Colavin A, Kobayashi Y, Nykamp K, Johnson B, Nussbaum RL. Applications of artificial intelligence in clinical laboratory genomics. AMERICAN JOURNAL OF MEDICAL GENETICS. PART C, SEMINARS IN MEDICAL GENETICS 2023; 193:e32057. [PMID: 37507620 DOI: 10.1002/ajmg.c.32057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023]
Abstract
The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.
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Affiliation(s)
- Swaroop Aradhya
- Invitae Corporation, San Francisco, California, USA
- Adjunct Clinical Faculty, Department of Pathology, Stanford University School of Medicine, Stanford, California, USA
| | | | - Hillery Metz
- Invitae Corporation, San Francisco, California, USA
| | - Toby Manders
- Invitae Corporation, San Francisco, California, USA
| | | | | | - Keith Nykamp
- Invitae Corporation, San Francisco, California, USA
| | | | - Robert L Nussbaum
- Invitae Corporation, San Francisco, California, USA
- Volunteer Faculty, School of Medicine, University of California San Francisco, San Francisco, California, USA
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10
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Visibelli A, Roncaglia B, Spiga O, Santucci A. The Impact of Artificial Intelligence in the Odyssey of Rare Diseases. Biomedicines 2023; 11:887. [PMID: 36979866 PMCID: PMC10045927 DOI: 10.3390/biomedicines11030887] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/28/2023] [Accepted: 03/08/2023] [Indexed: 03/16/2023] Open
Abstract
Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.
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Affiliation(s)
- Anna Visibelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Bianca Roncaglia
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
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11
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DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning. Molecules 2023; 28:molecules28052284. [PMID: 36903531 PMCID: PMC10005629 DOI: 10.3390/molecules28052284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 02/02/2023] [Accepted: 02/10/2023] [Indexed: 03/06/2023] Open
Abstract
The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques.
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12
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Zhao C, Tan T, Zhang E, Wang T, Gong H, Jia Q, Liu T, Yang X, Zhao J, Wu Z, Wei H, Xiao J, Yang C. A chronicle review of new techniques that facilitate the understanding and development of optimal individualized therapeutic strategies for chordoma. Front Oncol 2022; 12:1029670. [PMID: 36465398 PMCID: PMC9708744 DOI: 10.3389/fonc.2022.1029670] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 10/19/2022] [Indexed: 09/01/2023] Open
Abstract
Chordoma is a rare malignant bone tumor that mainly occurs in the sacrum and the clivus/skull base. Surgical resection is the treatment of choice for chordoma, but the local recurrence rate is high with unsatisfactory prognosis. Compared with other common tumors, there is not much research and individualized treatment for chordoma, partly due to the rarity of the disease and the lack of appropriate disease models, which delay the discovery of therapeutic strategies. Recent advances in modern techniques have enabled gaining a better understanding of a number of rare diseases, including chordoma. Since the beginning of the 21st century, various chordoma cell lines and animal models have been reported, which have partially revealed the intrinsic mechanisms of tumor initiation and progression with the use of next-generation sequencing (NGS) techniques. In this study, we performed a systematic overview of the chordoma models and related sequencing studies in a chronological manner, from the first patient-derived chordoma cell line (U-CH1) to diverse preclinical models such as the patient-derived organoid-based xenograft (PDX) and patient-derived organoid (PDO) models. The use of modern sequencing techniques has discovered mutations and expression signatures that are considered potential treatment targets, such as the expression of Brachyury and overactivated receptor tyrosine kinases (RTKs). Moreover, computational and bioinformatics techniques have made drug repositioning/repurposing and individualized high-throughput drug screening available. These advantages facilitate the research and development of comprehensive and personalized treatment strategies for indicated patients and will dramatically improve their prognoses in the near feature.
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Affiliation(s)
- Chenglong Zhao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Tao Tan
- Department of Orthopedics, 905 Hospital of People’s Liberation Army Navy, Shanghai, China
| | - E. Zhang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Ting Wang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Haiyi Gong
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Qi Jia
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Tielong Liu
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Xinghai Yang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Jian Zhao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Zhipeng Wu
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Haifeng Wei
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Jianru Xiao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Cheng Yang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
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13
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Korn D, Thieme AJ, Alves VM, Yeakey M, V V B Borba J, Capuzzi SJ, Fecho K, Bizon C, Edwards SW, Chirkova R, Colvis CM, Southall NT, Austin CP, Muratov EN, Tropsha A. Defining clinical outcome pathways. Drug Discov Today 2022; 27:1671-1678. [PMID: 35182735 DOI: 10.1016/j.drudis.2022.02.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/07/2022] [Accepted: 02/14/2022] [Indexed: 12/23/2022]
Abstract
Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: molecular initiating event (MIE) → intermediate event(s) → clinical outcome. We illustrate the concept with COP examples both for primary and alternative (i.e., drug repurposing) therapeutic applications. We also describe the elucidation of COPs for several drugs of interest using the publicly accessible Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP) biomedical knowledge graph-mining tool. We propose that broader use of COP uncovered with the help of biomedical knowledge graph mining will likely accelerate drug discovery and repurposing efforts.
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Affiliation(s)
- Daniel Korn
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA; UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Andrew J Thieme
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Vinicius M Alves
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Michael Yeakey
- Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA
| | - Joyce V V B Borba
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Stephen J Capuzzi
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Karamarie Fecho
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, USA
| | - Chris Bizon
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, USA
| | | | - Rada Chirkova
- Department of Computer Science, North Carolina State University, Raleigh, NC, USA
| | - Christine M Colvis
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Noel T Southall
- Department of Computer Science, North Carolina State University, Raleigh, NC, USA
| | - Christopher P Austin
- Department of Computer Science, North Carolina State University, Raleigh, NC, USA
| | - Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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