Article
Biochemical Research Methods
Delora Baptista, Pedro G. Ferreira, Miguel Rocha
Summary: This article critically reviews recent studies that have used deep learning methods to predict drug response in cancer cell lines and introduces the characteristics of DL and the architectures used in these studies. It also provides an overview of publicly available drug screening data resources and discusses the limitations of these methods.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Betul Guvenc Paltun, Hiroshi Mamitsuka, Samuel Kaski
Summary: Predicting the response of cancer cell lines to specific drugs is a central problem in personalized medicine. Choosing informative data sources and methods that can efficiently incorporate multiple sources is a challenging part of successful analysis in personalized medicine.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
B. Zagidullin, Z. Wang, Y. Guan, E. Pitkanen, J. Tang
Summary: This study compares rule-based and data-driven molecular representations in predicting drug combination sensitivity and synergy scores, using standardized results from high-throughput screening studies. The research highlights the importance of supplementing quantitative benchmark results with qualitative considerations for identifying the optimal molecular representation type.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Multidisciplinary Sciences
Francesco Piccialli, Francesco Calabro, Danilo Crisci, Salvatore Cuomo, Edoardo Prezioso, Roberta Mandile, Riccardo Troncone, Luigi Greco, Renata Auricchio
Summary: Potential Celiac Patients (PCD) with genetic predisposition may not develop clinical symptoms or small intestine mucosal damage progression for several years. Machine Learning (ML) techniques can be used to predict the natural history of PCD patients.
SCIENTIFIC REPORTS
(2021)
Review
Medicine, General & Internal
Mireya Martinez-Garcia, Enrique Hernandez-Lemus
Summary: The main goal of Precision Medicine is to integrate databases on disease origins into analytic frameworks for personalized diagnostics and therapeutics. Artificial intelligence and machine learning can be used to build analytical models for predicting individual health conditions. Challenges in data management, confidentiality, and bioethics need to be addressed in order for computational intelligence to be successfully applied to medicine.
FRONTIERS IN MEDICINE
(2022)
Review
Cardiac & Cardiovascular Systems
Evangelos K. Oikonomou, Rohan Khera
Summary: Artificial intelligence and machine learning have the potential to revolutionize healthcare, particularly in the management of diabetes and its cardiovascular complications. This review provides an overview of the various data-driven methods and their application in personalized care for diabetes patients at increased cardiovascular risk. The article discusses the role of artificial intelligence in diagnosis, prognostication, phenotyping, and treatment, as well as the challenges and ethical considerations that arise. It also emphasizes the need for regulatory standards to ensure the effectiveness and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.
CARDIOVASCULAR DIABETOLOGY
(2023)
Review
Biochemical Research Methods
Chun-Chun Wang, Yan Zhao, Xing Chen
Summary: Efforts are needed to develop effective drugs for complex diseases. Traditional drug discovery methods are time-consuming and costly, leading to the proposal of pathway-based drug discovery. Computational models have been established to predict drug-pathway associations, facilitating the development of new drugs.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Clinical Neurology
Giuseppe Maria Della Pepa, Valerio Maria Caccavella, Grazia Menna, Tamara Ius, Anna Maria Auricchio, Giovanni Sabatino, Giuseppe La Rocca, Silvia Chiesa, Simona Gaudino, Enrico Marchese, Alessandro Olivi
Summary: This study successfully utilized a machine learning (ML) model to identify a subset of patients with GBM who were at high risk for early recurrence, using a random forest prediction model with high discriminative ability. By optimizing the predictive value derived from the selected input features, the ML-based model outperformed traditional multivariable logistic regression across all performance metrics.
Review
Biochemical Research Methods
Xin An, Xi Chen, Daiyao Yi, Hongyang Li, Yuanfang Guan
Summary: This article focuses on the application of machine learning in drug response prediction, and specifically discusses the implementation and application examples of molecular representation methods.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Computer Science, Information Systems
Caio Gustavo Rodrigues da Cruz, Rodrigo Rocha Silva, Mauricio Goncalves Vieira Ferreira, Jorge Bernardino
Summary: This paper presents an automated planning process with restrictions to prevent invalid states, ensuring higher quality plans and better performance during execution. The method involves implementing a validator to match plan generation with imposed domain restrictions, contributing to reduced computational resources.
Review
Biochemistry & Molecular Biology
Fangyoumin Feng, Bihan Shen, Xiaoqin Mou, Yixue Li, Hong Li
Summary: The response rate of most anti-cancer drugs is limited due to cancer heterogeneity and complex drug mechanisms. Personalized treatment using molecular biomarkers shows promise. Advances in pharmacogenomics, especially computational methods in drug response prediction, have been successful and increasingly utilized in personalized cancer medicine.
JOURNAL OF GENETICS AND GENOMICS
(2021)
Review
Biotechnology & Applied Microbiology
Sarah J. MacEachern, Nils D. Forkert
Summary: Precision medicine utilizes multi-modal data to make individualized treatment decisions, requiring advanced computer techniques such as machine learning to process and analyze large-scale complex datasets, ultimately enhancing understanding of human health and disease.
Review
Biotechnology & Applied Microbiology
Yihao Liu, Minghua Wu
Summary: Deep learning has been successfully applied to various tasks in different fields, including disease diagnosis in medicine. By extracting multilevel features from medical data, deep learning helps doctors automatically assess diseases and monitor patients' physical health.
BIOENGINEERING & TRANSLATIONAL MEDICINE
(2023)
Review
Biochemical Research Methods
Marian Gimeno, Katyna Sada del Real, Angel Rubio
Summary: This study compared six different machine learning methods and found that tree-based methods are the most interpretable.
BRIEFINGS IN BIOINFORMATICS
(2023)
Review
Biochemistry & Molecular Biology
Elettra Barberis, Shahzaib Khoso, Antonio Sica, Marco Falasca, Alessandra Gennari, Francesco Dondero, Antreas Afantitis, Marcello Manfredi
Summary: This review discusses the application of recent technological innovations in mass spectrometry to metabolomics analysis, with a focus on the use of artificial intelligence (AI) strategies. The article also explores the challenges and limitations of implementing metabolomics-AI systems, as well as recent tools and studies in disease classification and biomarker identification.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Oncology
Minh-Phuong Huynh-Le, Roshan Karunamuni, Chun Chieh Fan, Lui Asona, Wesley K. Thompson, Maria Elena Martinez, Rosalind A. Eeles, Zsofia Kote-Jarai, Kenneth R. Muir, Artitaya Lophatananon, Johanna Schleutker, Nora Pashayan, Jyotsna Batra, Henrik Groenberg, David E. Neal, Borge G. Nordestgaard, Catherine M. Tangen, Robert J. MacInnis, Alicja Wolk, Demetrius Albanes, Christopher A. Haiman, Ruth C. Travis, William J. Blot, Janet L. Stanford, Lorelei A. Mucci, Catharine M. L. West, Sune F. Nielsen, Adam S. Kibel, Olivier Cussenot, Sonja Berndt, Stella Koutros, Karina Dalsgaard Sorensen, Cezary Cybulski, Eli Marie Grindedal, Florence Menegaux, Jong Y. Park, Sue A. Ingles, Christiane Maier, Robert J. Hamilton, Barry S. Rosenstein, Yong-Jie Lu, Stephen Watya, Ana Vega, Manolis Kogevinas, Fredrik Wiklund, Kathryn L. Penney, Chad D. Huff, Manuel R. Teixeira, Luc Multigner, Robin J. Leach, Hermann Brenner, Esther M. John, Radka Kaneva, Christopher J. Logothetis, Susan L. Neuhausen, Kim De Ruyck, Piet Ost, Azad Razack, Lisa F. Newcomb, Jay H. Fowke, Marija Gamulin, Aswin Abraham, Frank Claessens, Jose Esteban Castelao, Paul A. Townsend, Dana C. Crawford, Gyorgy Petrovics, Ron H. N. van Schaik, Marie-Elise Parent, Jennifer J. Hu, Wei Zheng, Ian G. Mills, Ole A. Andreassen, Anders M. Dale, Tyler M. Seibert
Summary: This study evaluates the effect of using single-nucleotide polymorphisms (SNPs) for prostate cancer risk stratification, and finds that including additional SNPs improves the association with clinically significant prostate cancer in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.
PROSTATE CANCER AND PROSTATIC DISEASES
(2022)
Article
Biochemistry & Molecular Biology
Rebecca C. Jones, Kevin M. Lawrence, Scott M. Higgins, Stephen M. Richardson, Paul A. Townsend
Summary: Post-traumatic osteoarthritis (PTOA) is characterized by rapid chondrocyte cell death and a shift in cell phenotype towards a more catabolic state. This study shows that urocortin-1 (Ucn) can protect chondrocytes from death and prevent cartilage degradation in a PTOA model. The protective effect of Ucn is mediated through the CRF-R1 receptor and involves blocking of intracellular calcium accumulation through the Piezo1 channel. These findings suggest that Ucn could be a novel disease-modifying osteoarthritis drug (DMOAD) for preventing impact overload-induced PTOA.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Review
Biochemistry & Molecular Biology
Rebecca C. Fitzgerald, Antonis C. Antoniou, Ljiljana Fruk, Nitzan Rosenfeld
Summary: Proactively detecting cancer at an early stage is crucial, but distinguishing inconsequential changes from life-threatening lesions can be challenging. Advancements in technology will aid real-time detection of personalized cancer signals. Risk-based detection and prevention should be cost effective and widely accessible.
Review
Multidisciplinary Sciences
David Crosby, Sangeeta Bhatia, Kevin M. Brindle, Lisa M. Coussens, Caroline Dive, Mark Emberton, Sadik Esener, Rebecca C. Fitzgerald, Sanjiv S. Gambhir, Peter Kuhn, Timothy R. Rebbeck, Shankar Balasubramanian
Summary: Early detection of cancer is crucial for improving survival rates, but unfortunately, a significant number of cases are diagnosed at an advanced stage. Overcoming various challenges is essential to achieve early detection for all cancers, including understanding high-risk individuals, elucidating the biology and trajectory of precancer and early cancer, and developing sensitive and specific detection technologies.
Article
Oncology
Xiaoyu Wang, Puya Gharahkhani, David M. Levine, Rebecca C. Fitzgerald, Ines Gockel, Douglas A. Corley, Harvey A. Risch, Leslie Bernstein, Wong-Ho Chow, Lynn Onstad, Nicholas J. Shaheen, Jesper Lagergren, Laura J. Hardie, Anna H. Wu, Paul D. P. Pharoah, Geoffrey Liu, Lesley A. Anderson, Prasad G. Iyer, Marilie D. Gammon, Carlos Caldas, Weimin Ye, Hugh Barr, Paul Moayyedi, Rebecca Harrison, R. G. Peter Watson, Stephen Attwood, Laura Chegwidden, Sharon B. Love, David MacDonald, John DeCaestecker, Hans Prenen, Katja Ott, Susanne Moebus, Marino Venerito, Hauke Lang, Rupert Mayershofer, Michael Knapp, Lothar Veits, Christian Gerges, Josef Weismueller, Matthias Reeh, Markus M. Noethen, Jakob R. Izbicki, Hendrik Manner, Horst Neuhaus, Thomas Roesch, Anne C. Boehmer, Arnulf H. Hoelscher, Mario Anders, Oliver Pech, Brigitte Schumacher, Claudia Schmidt, Thomas Schmidt, Tania Noder, Dietmar Lorenz, Michael Vieth, Andrea May, Timo Hess, Nicole Kreuser, Jessica Becker, Christian Ell, Ian Tomlinson, Claire Palles, Janusz A. Jankowski, David C. Whiteman, Stuart MacGregor, Johannes Schumacher, Thomas L. Vaughan, Matthew F. Buas, James Y. Dai
Summary: This study identified novel genetic susceptibility loci for esophageal adenocarcinoma and Barrett esophagus using an eQTL set-based genetic association approach, expanding the pool of genetic susceptibility loci.
CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION
(2022)
Article
Biochemistry & Molecular Biology
Daiana Drehmer, Joao Paulo Mesquita Luiz, Cesar Augusto Speck Hernandez, Jose Carlos Alves-Filho, Tracy Hussell, Paul Andrew Townsend, Salvador Moncada
Summary: This study demonstrates that nitric oxide (NO) produced by colon and prostate cancer cells affects tumor-associated macrophages (TAMs), leading to changes in TAMs' phenotype and impairing their antigen-presenting and wound healing capacities.
Article
Chemistry, Analytical
Holly-May Lewis, Priyanka Gupta, Kyle D. G. Saunders, Shazneil Briones, Johanna von Gerichten, Paul A. Townsend, Eirini Velliou, Dany J. V. Beste, Olivier Cexus, Roger Webb, Melanie J. Bailey
Summary: This work presents a new approach for measuring drug levels and lipid fingerprints in individual living mammalian cells. The method combines nanocapillary sampling with liquid chromatography to improve precision in drug analysis. By optimizing the transfer of analytes from the sampling capillary, the researchers achieved efficient measurement of drug analytes in 30 living cells. They also discovered cell-to-cell heterogeneity in drug molecule uptake and identified lipid features associated with bedaquiline uptake.
Article
Oncology
Colin Y. C. Lee, Adriaan Olivier, Judith Honing, Anne-Marie Lydon, Susan Richardson, Maria O'Donovan, Marc Tischkowitz, Rebecca C. Fitzgerald, Massimiliano di Pietro
Summary: Hereditary diffuse gastric cancer, caused by CDH1 gene mutations, is characterized by early-onset signet ring cell carcinoma. Prophylactic total gastrectomy is the recommended treatment. This study assessed different sampling strategies for detecting signet ring cell carcinoma and identified criteria for characterizing endoscopic lesions in hereditary diffuse gastric cancer.
Article
Genetics & Heredity
Burcu F. Darst, Jiayi Shen, Ravi K. Madduri, Alexis A. Rodriguez, Yukai Xiao, Xin Sheng, Edward J. Saunders, Tokhir Dadaev, Mark N. Brook, Thomas J. Hoffmann, Kenneth Muir, Peggy Wan, Loic Le Marchand, Lynne Wilkens, Ying Wang, Johanna Schleutker, Robert J. MacInnis, Cezary Cybulski, David E. Neal, Borge G. Nordestgaard, Sune F. Nielsen, Jyotsna Batra, Judith A. Clements, Henrik Gronberg, Nora Pashayan, Ruth C. Travis, Jong Y. Park, Demetrius Albanes, Stephanie Weinstein, Lorelei A. Mucci, David J. Hunter, Kathryn L. Penney, Catherine M. Tangen, Robert J. Hamilton, Marie-Elise Parent, Janet L. Stanford, Stella Koutros, Alicja Wolk, Karina D. Sorensen, William J. Blot, Edward D. Yeboah, James E. Mensah, Yong-Jie Lu, Daniel J. Schaid, Stephen N. Thibodeau, Catharine M. West, Christiane Maier, Adam S. Kibel, Geraldine Cancel-Tassin, Florence Menegaux, Esther M. John, Eli Marie Grindedal, Kay-Tee Khaw, Sue A. Ingles, Ana Vega, Barry S. Rosenstein, Manuel R. Teixeira, Manoli Kogevinas, Lisa Cannon-Albright, Chad Huff, Luc Multigner, Radka Kaneva, Robin J. Leach, Hermann Brenner, Ann W. Hsing, Rick A. Kittles, Adam B. Murphy, Christopher J. Logothetis, Susan L. Neuhausen, William B. Isaacs, Barbara Nemesure, Anselm J. Hennis, John Carpten, Hardev Pandha, Kim De Ruyck, Jianfeng Xu, Azad Razack, Soo-Hwang Teo, Lisa F. Newcomb, Jay H. Fowke, Christine Neslund-Dudas, Benjamin A. Rybicki, Marija Gamulin, Nawaid Usmani, Frank Claessens, Manuela Gago-Dominguez, Jose Esteban Castelao, Paul A. Townsend, Dana C. Crawford, Gyorgy Petrovics, Graham Casey, Monique J. Roobol, Jennifer F. Hu, Sonja I. Berndt, Stephen K. van den Eeden, Douglas F. Easton, Stephen J. Chanock, Michael B. Cook, Fredrik Wiklund, John S. Witte, Rosalind A. Eeles, Zsofia Kote-Jarai, Stephen Watya, John M. Gaziano, Amy C. Justice, David V. Conti, Christopher A. Haiman
Summary: In this study, the predictive ability of several genome-wide polygenic risk score (GW-PRS) approaches was compared to a recently developed PRS of established prostate cancer-risk variants. The findings suggest that the PRS269 developed from multi-ancestry GWASs and fine-mapping has better predictive ability for prostate cancer risk compared to current GW-PRS approaches.
AMERICAN JOURNAL OF HUMAN GENETICS
(2023)
Article
Oncology
Judith Honing, Rebecca C. Fitzgerald
Summary: Barrett's esophagus is a precancerous condition that can progress to esophageal adenocarcinoma. Surveillance is needed to detect progression, but current methods are limited. This commentary proposes incorporating new risk factors and tools, such as nonendoscopic triage and commercial biomarker panels, into clinical practice for better risk stratification.
CANCER PREVENTION RESEARCH
(2023)
Article
Oncology
Ammara Muazzam, Matt Spick, Olivier N. F. Cexus, Bethany Geary, Fowz Azhar, Hardev Pandha, Agnieszka Michael, Rachel Reed, Sarah Lennon, Lee A. Gethings, Robert S. Plumb, Anthony D. Whetton, Nophar Geifman, Paul A. Townsend
Summary: Despite ongoing research, effective tools for detection and monitoring of prostate cancer are still lacking. This study aims to identify non-invasive biomarkers that can complement PSA in clinical decision-making. Novel prostate cancer protein signatures were discovered using an advanced analytical technique, and the results were validated using a second cohort of patients. The study provides a proteomic signature that can enhance the diagnosis and risk assessment of localized prostate cancer.
Article
Biochemistry & Molecular Biology
Aphrodite Daskalopoulou, Sotiria G. Giotaki, Konstantina Toli, Angeliki Minia, Vaia Pliaka, Leonidas G. Alexopoulos, Gerasimos Deftereos, Konstantinos Iliodromitis, Dimitrios Dimitroulis, Gerasimos Siasos, Christos Verikokos, Dimitrios Iliopoulos
Summary: This study aimed to identify potential biomarkers for ascending thoracic aneurysm (ATAA) using targeted proteomic analysis. CCL5, HBD1, and ICAM1 were found to be promising biomarkers with satisfying sensitivity and specificity, which could be helpful in the diagnosis and follow-up of ATAA patients. Further studies are warranted to investigate the role of these biomarkers in the pathogenesis of ATAA.
Article
Cardiac & Cardiovascular Systems
Naoko Yamaguchi, Ernest W. Chang, Ziyan Lin, Akshay Shekhar, Lei Bu, Alireza Khodadadi-Jamayran, Aristotelis Tsirigos, Yiyun Cen, Colin K. L. Phoon, Ivan P. Moskowitz, David S. Park
Summary: The study revealed the essential role of GATA6 and NKX2-5 in OFT development using mouse models, demonstrating the dependency between GATA6 and NKX2-5, and highlighting the impact of GATA6 on cardiac enhancers.
Article
Hematology
Jue Feng, Pei-Feng Hsu, Eduardo Esteva, Rossella Labella, Yueyang Wang, Alireza Khodadadi-Jamayran, Joseph Pucella, Cynthia Z. Liu, Arnaldo A. Arbini, Aristotelis Tsirigos, Stavroula Kousteni, Boris Reizis
Summary: Deletion of the 9p21 locus is associated with reduced survival in cancer patients and can lead to myelodysplastic syndrome/myeloproliferative neoplasm and aberrant bone formation in the bone marrow.
Article
Biochemistry & Molecular Biology
Kieran Foley, David Shorthouse, Eric Rahrmann, Lizhe Zhuang, Ginny Devonshire, Rebecca C. OCCAMS Consortium, Rebecca C. Fitzgerald, Benjamin A. Hall
Summary: Metastasis in oesophageal adenocarcinoma (OAC) is a crucial factor affecting survival. Radiological staging is commonly used to assess metastases, but its accuracy is limited. This study analyzed lymph node metastases and identified new roles of genes SMAD4 and KCNQ3 in metastasis. The findings suggest that both genes could serve as novel biomarkers for metastatic risk and offer potential new targets for drug treatment.
BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE
(2024)