Article
Biotechnology & Applied Microbiology
Stephanie LaHaye, James R. Fitch, Kyle J. Voytovich, Adam C. Herman, Benjamin J. Kelly, Grant E. Lammi, Jeremy A. Arbesfeld, Saranga Wijeratne, Samuel J. Franklin, Kathleen M. Schieffer, Natalie Bir, Sean D. McGrath, Anthony R. Miller, Amy Wetzel, Katherine E. Miller, Tracy A. Bedrosian, Kristen Leraas, Elizabeth A. Varga, Kristy Lee, Ajay Gupta, Bhuvana Setty, Daniel R. Boue, Jeffrey R. Leonard, Jonathan L. Finlay, Mohamed S. Abdelbaki, Diana S. Osorio, Selene C. Koo, Daniel C. Koboldt, Alex H. Wagner, Ann-Kathrin Eisfeld, Krzysztof Mrozek, Vincent Magrini, Catherine E. Cottrell, Elaine R. Mardis, Richard K. Wilson, Peter White
Summary: The EnFusion pipeline utilizes seven fusion calling algorithms to increase the accuracy of identifying clinically relevant fusions in pediatric cancer. By combining ensemble fusion-calling pipeline with a knowledge-based filtering strategy, it accurately identifies driver fusions in pediatric cancer, contributing evidence to diagnosis and guiding targeted therapies where appropriate.
Review
Cell Biology
Martin Giera, Oscar Yanes, Gary Siuzdak
Summary: Metabolite identification is a significant challenge and opportunity in biochemistry. The characterization and quantification of metabolites in living organisms has resulted in a valuable biochemical knowledgebase and serves as the foundation for metabolism research in the 21st century. However, the characterization of newly discovered metabolites has remained a persistent obstacle. Crystallography, NMR spectroscopy, and mass spectrometry are important techniques in this field, with the latter being particularly useful when coupled with high-performance separation technologies and database solutions. Furthermore, the development of artificial intelligence technologies is rapidly advancing to help overcome the challenges in metabolite identification.
Article
Medicine, Research & Experimental
Vu Viet Hoang Pham, Lin Liu, Cameron Bracken, Gregory Goodall, Jiuyong Li, Thuc Duy Le
Summary: Researchers conducted a comprehensive review of computational methods for discovering cancer drivers, categorizing them into three groups and evaluating their performance in identifying biologically significant cancer drivers to provide readers with information.
Review
Biochemical Research Methods
Stefano Castellana, Tommaso Biagini, Luca Parca, Francesco Petrizzelli, Salvatore Daniele Bianco, Angelo Luigi Vescovi, Massimo Carella, Tommaso Mazza
Summary: This study found that hundreds of human proteins interact with degenerated DNA sequences, and identifying these motifs and genomic sites is a challenging research goal in modern molecular biology and bioinformatics. Over the past twenty years, there has been an explosion of computational tools for this task, and sixteen of them were evaluated for their ability to identify known motifs in simulated sequence datasets.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Genetics & Heredity
Adam C. Gunning, Verity Fryer, James Fasham, Andrew H. Crosby, Sian Ellard, Emma L. Baple, Caroline F. Wright
Summary: This study validated the performance of several new meta-predictors in a clinically relevant dataset, and found that while the new meta-predictors outperformed traditional tools, all pathogenicity predictors showed lower performance in the clinically relevant dataset, with REVEL performing the best.
JOURNAL OF MEDICAL GENETICS
(2021)
Article
Biotechnology & Applied Microbiology
Volundur Hafstao, Jari Hakkinen, Malin Larsson, Johan Staaf, Johan Vallon-Christersson, Helena Persson
Summary: A process for validating fusion transcripts and a machine learning classifier were developed to improve identification accuracy, extending the detection of potential targetable kinase fusions. Machine learning can be utilized for clinically relevant fusion events identification for targeted therapy, with the generated dataset serving as a valuable resource for fusion transcript detection algorithm development and evaluation.
Article
Oncology
Michela Lupia, Valentina Melocchi, Francesca Bizzaro, Pietro Lo Riso, Elisa Dama, Micol Baronio, Alberto Ranghiero, Massimo Barberis, Loris Bernard, Giovanni Bertalot, Raffaella Giavazzi, Giuseppe Testa, Fabrizio Bianchi, Ugo Cavallaro
Summary: High-grade serous ovarian carcinoma (HGSOC) is a highly aggressive and intractable tumor with unknown molecular alterations. Researchers identified molecular changes associated with HGSOC and identified potential vulnerabilities associated with the tumor. The PI3K signaling pathway was identified as a novel druggable target in OCSC.
INTERNATIONAL JOURNAL OF CANCER
(2022)
Article
Medicine, Research & Experimental
Xiang-Yu Wang, Wen-Wei Zhu, Zheng Wang, Jian-Bo Huang, Sheng-Hao Wang, Fu-Mao Bai, Tian-En Li, Ying Zhu, Jing Zhao, Xin Yang, Lu Lu, Ju-Bo Zhang, Hu-Liang Jia, Qiong-Zhu Dong, Jin-Hong Chen, Jesper B. Andersen, Dan Ye, Lun-Xiu Qin
Summary: By analyzing genomic data, we established a clinically applicable genomic clustering system for intrahepatic cholangiocarcinoma (ICC), which can be used for prognostic prediction, molecular classification, and therapeutic optimization.
Review
Oncology
Madison Snyder, Susana Iraola-Guzman, Ester Saus, Toni Gabaldon
Summary: Recent biomedical research has focused on identifying molecular biomarkers to improve the diagnosis, prognosis, and treatment of common human diseases, including cancer. Long non-coding RNAs (lncRNAs) have emerged as promising molecules with diagnostic and prognostic potential in cancer, particularly colorectal cancer (CRC). This review provides an overview of the approaches for discovering and validating lncRNA candidates in CRC and presents a curated list of clinically relevant lncRNAs associated with this malignancy.
Article
Biochemistry & Molecular Biology
Markus Wild, Jintawee Kicuntod, Lisa Seyler, Christina Wangen, Luca D. Bertzbach, Andele M. Conradie, Benedikt B. Kaufer, Sabrina Wagner, Detlef Michel, Jan Eickhoff, Svetlana B. Tsogoeva, Tobias Bauerle, Friedrich Hahn, Manfred Marschall
Summary: Through experiments on various PKIs, this study demonstrated the potential efficacy of these kinase inhibitors not only against HCMV but also potentially against other herpesviruses. Drug combination experiments revealed a strong synergistic effect between LDC4297 and maribavir, opening up new possibilities for future clinical treatments.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Oncology
Ania Alay, David Cordero, Sara Hijazo-Pechero, Elisabet Aliagas, Adriana Lopez-Doriga, Raul Marin, Ramon Palmero, Roger Llatjos, Ignacio Escobar, Ricard Ramos, Susana Padrones, Victor Moreno, Ernest Nadal, Xavier Sole
Summary: This study identifies T-helper 2 (T-H2) and cytotoxic T (T-C) cells as key factors associated with overall survival in malignant pleural mesothelioma (MPM). Three immune clusters were defined based on the levels of T-H2 and T-C immune infiltration, with the IG1 and IG3 groups showing associations with worse and better overall survival, respectively. Integration of gene expression with functional signatures of drug response showed that IG3 patients might be more likely to respond to immune-based therapies.
JOURNAL FOR IMMUNOTHERAPY OF CANCER
(2021)
Article
Engineering, Biomedical
Bruno Rego, Alison M. Pouch, Joseph H. Gorman, Robert C. Gorman, Michael S. Sacks
Summary: The present study utilized a noninvasive computational pipeline and patient-specific real-time three-dimensional echocardiographic imaging data to quantify the functional deformation differences between tricuspid aortic valve (TAV) and bicuspid aortic valve (BAV) leaflets for the first time.
ANNALS OF BIOMEDICAL ENGINEERING
(2022)
Article
Biochemical Research Methods
Ziying Yang, Guoxian Yu, Maozu Guo, Jiantao Yu, Xiangliang Zhang, Jun Wang
Summary: The study introduces a new approach called CDPath to discover cooperative driver pathways, which can identify driver genes and pathways related to target cancer, involved in carcinogenesis and key biological processes. CDPath can uncover more potential biological associations and cooperative driver pathways compared to competitive approaches.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Review
Infectious Diseases
Theo H. M. Smits, Lavinia N. V. S. Arend, Sofia Cardew, Erika Tang-Hallback, Marcelo T. Mira, Edward R. B. Moore, Jorge L. M. Sampaio, Fabio Rezzonico, Marcelo Pillonetto
Summary: With improvements in genomics, it is now possible to accurately identify clinical isolates at the species level and analyze past occurrences of specific organisms. This approach can aid in the classification of clinically relevant taxa that have previously gone unrecognized in clinical diagnostics.
EUROPEAN JOURNAL OF CLINICAL MICROBIOLOGY & INFECTIOUS DISEASES
(2022)
Article
Genetics & Heredity
Petr Taus, Sarka Pospisilova, Karla Plevova
Summary: This study aimed to reduce the heterogeneity of somatic mutation data in chronic lymphocytic leukemia (CLL) patients by analyzing mutated genes in specific biological processes. By utilizing sequencing data from 506 CLL patients, pathway mutation scores were generated, and abnormal molecular pathways were identified using machine learning techniques. The study identified four clusters with different pathway mutational profiles and time to first treatment, suggesting potential subtypes in CLL based on mutation profiles. This research is an important step towards understanding mutational patterns in CLL.
FRONTIERS IN GENETICS
(2021)