Review
Biochemical Research Methods
Mohammad Reza Karimi, Amir Hossein Karimi, Shamsozoha Abolmaali, Mehdi Sadeghi, Ulf Schmitz
Summary: Holistic perspectives are crucial in understanding the complexity of tumors and current single-layer analysis has limitations. Integrative multilayer approaches are emerging as effective tools in achieving systemic views on cancer biology.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Oncology
Mahnoor Naseer Gondal, Rida Nasir Butt, Osama Shiraz Shah, Muhammad Umer Sultan, Ghulam Mustafa, Zainab Nasir, Risham Hussain, Huma Khawar, Romena Qazi, Muhammad Tariq, Amir Faisal, Safee Ullah Chaudhary
Summary: This study utilizes Drosophila as a model organism to develop personalized cancer therapeutic strategies by combining patient-specific gene expression data, identifying synergistic combinations of drugs, and validating their efficacy in colorectal cancer patients.
FRONTIERS IN ONCOLOGY
(2021)
Article
Biochemical Research Methods
Hai Yang, Yawen Liu, Yijing Yang, Dongdong Li, Zhe Wang
Summary: Cancer driver genes are crucial for tumor cell growth, and accurately identifying them is important for cancer research and targeted drug development. The InDEP framework, based on cascade forests, provides an interpretable machine learning approach for identifying cancer driver genes accurately.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Genetics & Heredity
Felipe Mateus Pellenz, Daisy Crispim, Tais Silveira Assmann
Summary: This study used a systems biology approach to identify a set of highly interconnected genes associated with childhood obesity, providing comprehensive information on the genetic and molecular factors involved in the pathogenesis of this disease.
Article
Biology
Mahdie Mortezapour, Leili Tapak, Fatemeh Bahreini, Rezvan Najafi, Saeid Afshar
Summary: This study used bioinformatics tools to identify biomarkers and molecular factors involved in the diagnosis of colorectal cancer. Differential expression genes (DEGs) related to colorectal cancer (CRC) were determined using data from the GEO database. A weighted gene co-expression network analysis (WGCNA) was performed to explore co-expression modules related to CRC diagnosis. The study identified several hub genes and potential therapeutic agents for targeting CRC.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Oncology
Morteza Gholami, Marziyeh Zoughi, Roobic Behboo, Reza Taslimi, Alireza Kazemeini, Milad Bastami, Shirin Hasani-Ranjbar, Bagher Larijani, Mahsa M. Amoli
Summary: This study found that variants of rs7473 and rs1547715 are associated with the risk of obesity and colorectal cancer respectively, based on their interactions with miRNAs.
Article
Cell Biology
Rigel J. Kishton, Shashank J. Patel, Amy E. Decker, Suman K. Vodnala, Maggie Cam, Tori N. Yamamoto, Yogin Patel, Madhusudhanan Sukumar, Zhiya Yu, Michelle Ji, Amanda N. Henning, Devikala Gurusamy, Douglas C. Palmer, Roxana A. Stefanescu, Andrew T. Girvin, Winifred Lo, Anna Pasetto, Parisa Malekzadeh, Drew C. Deniger, Kris C. Wood, Neville E. Sanjana, Nicholas P. Restifo
Summary: A three-step approach was used to identify cancer genes that inhibit T cell immunity, and BIRC2 was identified as a potential target for combination therapy with adoptive T cell therapies to increase efficacy.
Review
Oncology
James H. Park, Adrian Lopez Garcia de Lomana, Diego M. Marzese, Tiffany Juarez, Abdullah Feroze, Parvinder Hothi, Charles Cobbs, Anoop P. Patel, Santosh Kesari, Sui Huang, Nitin S. Baliga
Summary: Pronounced differences between individuals and cell-cell heterogeneity within tumors are major obstacles in effective brain tumor treatment, necessitating a personalized precision medicine approach. A systems biology approach is crucial for developing a multiscale understanding of disease mechanisms. Integrating patient tumor analysis with clinical medicine can optimize treatment strategies.
Article
Oncology
Faddy Kamel, Nathalie Schneider, Pasha Nisar, Mikhail Soloviev
Summary: This manuscript presents a novel 'bottom-up' approach to discovering putative cancer biomarkers using a small set of known markers as a starting point. By analyzing transcriptional programs and biological pathways, the method predicts an expanded set of potential markers, which was validated experimentally in colorectal cancer.
Article
Biochemical Research Methods
Stephen Kotiang, Ali Eslami
Summary: This study introduces a computational framework that combines Boolean networks and factor graphs to explore the dynamic features of biological systems. It utilizes a message-passing algorithm to simulate network state evolution and conducts density evolution analysis to study error propagation in gene regulatory networks. Simulation results show model predictions matching experimental data, and reveal the robustness and consistency of the sample network with real data.
BMC BIOINFORMATICS
(2021)
Article
Oncology
Haozhe Huang, Dezhong Zheng, Hong Chen, Chao Chen, Ying Wang, Lichao Xu, Yaohui Wang, Xinhong He, Yuanyuan Yang, Wentao Li
Summary: In this study, a novel multimodal data fusion model based on radiomics features and clinical variables was developed to objectively and accurately assess the immediate efficacy of radiofrequency ablation (RFA) on colorectal cancer (CRC) lung metastases. The model was constructed using a random forest classifier and achieved high accuracy, AUC value, sensitivity, and specificity in predicting the immediate efficacy of RFA. The results demonstrated the efficiency of this multimodal data fusion model for evaluating the efficacy of RFA in CRC lung metastases.
FRONTIERS IN ONCOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Saeid Parvandeh, Lawrence A. Donehower, Katsonis Panagiotis, Teng-Kuei Hsu, Jennifer K. Asmussen, Kwanghyuk Lee, Olivier Lichtarge
Summary: Discovering rare cancer driver genes is challenging due to their low mutational frequency. EPIMUTESTR is a machine learning algorithm that identifies such genes by modeling the fitness of their mutations, and it has shown better performance than existing methods.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Taisuke Sato, Katsumi Inoue
Summary: In this study, a explainable neural network Mat_DNF is proposed to learn Boolean functions as Boolean formulas in disjunctive normal form (DNFs) and applied to learning Boolean networks (BNs). DNFs are learned by representing them as binary matrices and minimizing a logically inspired non-negative cost function. It is proven that learning DNFs using this approach is equivalent to inferring interpolants in logic. The effectiveness of the proposed method was confirmed through experiments, and generalization for scarce data was examined.
Article
Biochemistry & Molecular Biology
Shahram Parvin, Hamid Sedighian, Ehsan Sohrabi, Mahdieh Mahboobi, Milad Rezaei, Dariush Ghasemi, Ehsan Rezaei
Summary: This study reviewed candidate genes for lung cancer from 120 published articles, identifying potential genes like PLK1 that could be survival markers for patients. Through network and pathway analysis, new potential biomarkers for lung cancer were introduced, suggesting the importance of further studies and confirmatory tests for these genes.
BIOCHEMICAL GENETICS
(2022)
Article
Chemistry, Multidisciplinary
Jae Il Joo, Hwa-Jeong Park, Kwang-Hyun Cho
Summary: Accumulated genetic alterations in cancer cells disrupt the stimulus-response relationships, resulting in uncontrolled proliferation. However, the complex molecular interaction network within cells suggests that it may be possible to restore these relationships by controlling hidden molecular switches. A system framework is presented for analyzing cellular input-output relationships and identifying molecular switches that can normalize the distorted relationships based on Boolean network modeling and dynamics analysis. The potential for reversion is demonstrated using cancer molecular networks and a case study on bladder cancer.
Article
Agriculture, Dairy & Animal Science
Pamela A. Alexandre, Antonio Reverter, Sigrid A. Lehnert, Laercio R. Porto-Neto, Sonja Dominik
JOURNAL OF ANIMAL SCIENCE
(2020)
Article
Genetics & Heredity
Pamela A. Alexandre, Nicholas J. Hudson, Sigrid A. Lehnert, Marina R. S. Fortes, Marina Naval-Sanchez, Loan T. Nguyen, Laercio R. Porto-Neto, Antonio Reverter
Article
Agriculture, Dairy & Animal Science
Brad C. Hine, Amy M. Bell, Dominic D. O. Niemeyer, Christian J. Duff, Nick M. Butcher, Sonja Dominik, Laercio R. Porto-Neto, Yutao Li, Antonio Reverter, Aaron B. Ingham, Ian G. Colditz
Summary: The study suggests that selecting for improved immune competence in beef production systems can reduce mortality rates during feedlot finishing, leading to improved animal health and welfare, as well as decreased health-associated costs for operators.
JOURNAL OF ANIMAL SCIENCE
(2021)
Article
Agriculture, Dairy & Animal Science
Hector Marina, Antonio Reverter, Beatriz Gutierrez-Gil, Pamela Almeida Alexandre, Rocio Pelayo, Aroa Suarez-Vega, Cristina Esteban-Blanco, Juan Jose Arranz
JOURNAL OF ANIMAL SCIENCE
(2020)
Article
Agriculture, Dairy & Animal Science
Hector Marina, Aroa Suarez-Vega, Rocio Pelayo, Beatriz Gutierrez-Gil, Antonio Reverter, Cristina Esteban-Blanco, Juan Jose Arranz
Summary: Parentage misassignments can impact genetic gain in traditional breeding programs. Genetic markers like microsatellites and SNPs are used for parentage verification in sheep. Transitioning from microsatellites to SNP markers can be cost-effective for parentage testing. This study aims to develop a low-density SNP chip for accurate parentage verification in Assaf sheep by imputing microsatellite markers from SNPs with high accuracy.
Article
Agriculture, Dairy & Animal Science
Muhammad S. Tahir, Laercio R. Porto-Neto, Toni Reverter-Gomez, Babatunde S. Olasege, Mirza R. Sajid, Kimberley B. Wockner, Andre W. L. Tan, Marina R. S. Fortes
Summary: Biologically informed SNP set did not show significantly better performance in predicting fertility traits in Tropical Composites compared to other SNP sets, but it captured all observed genetic variance when modeled together with biologically uninformed SNP set.
JOURNAL OF ANIMAL SCIENCE
(2022)
Article
Agriculture, Dairy & Animal Science
H. Marina, R. Pelayo, B. Gutierrez-Gil, A. Suarez-Vega, C. Esteban-Blanco, A. Reverter, J. J. Arranz
Summary: The present study investigates different strategies for improving the accuracy of estimated breeding values and the application of a low-density SNP chip. Through analyzing milk samples from dairy ewes and using various multi-trait methods, the study finds that genomic selection can enhance the accuracy of estimated breeding values, and the low-density SNP chip is a cost-effective and accurate tool.
JOURNAL OF DAIRY SCIENCE
(2022)
Article
Multidisciplinary Sciences
Laercio R. Porto-Neto, Pamela A. Alexandre, Nicholas J. Hudson, John Bertram, Sean M. McWilliam, Andre W. L. Tan, Marina R. S. Fortes, Michael R. McGowan, Ben J. Hayes, Antonio Reverter
Summary: Worldwide, the identification and selection of fertile bulls plays a crucial role in beef production. In this study, fertility-related phenotypes and high-density DNA genotypes were collected for thousands of bulls from six tropical breeds, revealing both genetic correlations and the importance of breed representation for accurate genomic estimated breeding values. Several candidate genes associated with bull fertility traits were identified, contributing to early-life selection and genetic improvement strategies. This study also enhances our knowledge of the molecular basis of male fertility in mammals.
Article
Agriculture, Dairy & Animal Science
Antonio Reverter, Laercio Porto-Neto, Brad C. Hine, Pamela A. Alexandre, Malshani Samaraweera, Andrew I. Byrne, Aaron B. Ingham, Christian J. Duff
Summary: Incorporating commercial data into the reference population of Angus SteerSELECT can improve the accuracy and effectiveness of the genomic tool.
ANIMAL PRODUCTION SCIENCE
(2023)
Correction
Agriculture, Dairy & Animal Science
Pamela A. Alexandre, Yutao Li, Brad C. Hine, Christian J. Duff, Aaron B. Ingham, Laercio R. Porto-Neto, Antonio Reverter
GENETICS SELECTION EVOLUTION
(2021)
Article
Agriculture, Dairy & Animal Science
Pamela A. Alexandre, Yutao Li, Brad C. Hine, Christian J. Duff, Aaron B. Ingham, Laercio R. Porto-Neto, Antonio Reverter
Summary: The study focused on improving feedlot performance and carcase traits in Australian Angus cattle through genomic predictions. Results showed potential for accurate genomic selection in these areas, with the linear regression method outperforming traditional methods and providing better ability to differentiate between extreme GEBV quartiles.
GENETICS SELECTION EVOLUTION
(2021)
Article
Agriculture, Dairy & Animal Science
S. Dominik, A. Reverter, L. R. Porto-Neto, J. C. Greeff, J. L. Smith
Summary: This study found that genomic prediction of breeding values for breech flystrike resistance is a feasible tool for applying genomic technology in the Merino industry. A reference population of appropriate size is needed for this trait, and a dispersed reference population could be an effective option.
ANIMAL PRODUCTION SCIENCE
(2021)
Article
Agriculture, Dairy & Animal Science
Antonio Reverter, Brad C. Hine, Laercio Porto-Neto, Pamela A. Alexandre, Yutao Li, Christian J. Duff, Sonja Dominik, Aaron B. Ingham
Summary: The study analyzed immune competence phenotypes and genomic predictions of breeding values in 3715 Australian Angus cattle, showing that immune competence phenotypes are moderately heritable and accurate genomic breeding values can be generated to select cattle with an improved ability to mount a general immune response.
ANIMAL PRODUCTION SCIENCE
(2021)
Article
Agriculture, Dairy & Animal Science
Brad C. Hine, Christian J. Duff, Andrew Byrne, Peter Parnell, Laercio Porto-Neto, Yutao Li, Aaron B. Ingham, Antonio Reverter
Summary: The study confirmed the ability of Angus SteerSELECT genomic product to predict differences in carcass weight, marbling score, ossification score, and carcass value in Australian Angus steers under both short-fed and long-fed conditions.
ANIMAL PRODUCTION SCIENCE
(2021)
Article
Agriculture, Dairy & Animal Science
S. Dominik, C. J. Duff, A. Byrne, H. Daetwyler, A. Reverter
Summary: This study demonstrates that ultra-small SNP panels with 20-23 SNPs can generate unique genotypes for up to approximately 80,000 individuals, providing an efficient method for beef product traceability through the supply chain.
ANIMAL PRODUCTION SCIENCE
(2021)