Article
Biochemical Research Methods
Xiaoyu Guan, Zhongnian Li, Yueying Zhou, Wei Shao, Daoqiang Zhang
Summary: Nanopore sequencing, as a high-throughput sequencing technology for DNA, RNA, and proteins, faces the challenge of high labeling costs for the enormous generated data. This study introduces active learning to select samples that need to be labeled, reducing the labeling costs significantly. Experimental results demonstrate that active learning can greatly reduce the labeling amount while achieving the best baseline performance for nanopore data analysis.
Article
Plant Sciences
Mengping Li, Keun Pyo Lee, Tong Liu, Vivek Dogra, Jianli Duan, Mengshuang Li, Weiman Xing, Chanhong Kim
Summary: Salicylic acid-related transcription coregulators, SIB1 and LSD1, have antagonistic effects on the activity of GLK1/2 transcription factors, thereby regulating chloroplast development and stress response. SIB1 and LSD1 interact with GLK1/2 to enhance or repress the expression of PhANGs, modulating the ROS homeostasis and cell death in chloroplasts.
Article
Multidisciplinary Sciences
Biwei Cao, Krupal B. Patel, Tingyi Li, Sijie Yao, Christine H. Chung, Xuefeng Wang
Summary: This study presents a subnetwork-based framework that combines hypothesis-driven, data-driven, and literature-based methods with informative visualization to prioritize candidate genes for cancer prognosis prediction. By applying this framework to a head and neck squamous cell cancer (HNSCC) transcriptome dataset, multiple HNSCC-specific gene modules with improved prognostic values and mechanism information were successfully identified compared to standard gene panel selection methods. The proposed framework is general and can be applied to any type of omics data.
Article
Biochemical Research Methods
Jonas Meisner, Anders Albrechtsen, Kristian Hanghoj
Summary: The study focuses on identifying selection signatures in populations with similar ancestries using genotype likelihood data from low-coverage sequencing. Two selection statistics implemented in the PCAngsd framework are able to control the false positive rate and identify selection signals in continuous populations without the need for ad-hoc filtering. The study shows that selection scans of low-coverage sequencing data perform comparably to high quality genotype data.
BMC BIOINFORMATICS
(2021)
Article
Endocrinology & Metabolism
Michal Olejarz, Ewelina Szczepanek-Parulska, Anna Ostalowska-Klockiewicz, Patrycja Antosik, Nadia Sawicka-Gutaj, Celina Helak-Lapaj, Marcin Stopa, Marek Ruchala
Summary: This study aimed to evaluate the differences in clinical profile, laboratory parameters, and ophthalmological signs and symptoms between patients with high IgG4 Graves orbitopathy and patients with normal IgG4 Graves orbitopathy. A total of 60 patients with Graves orbitopathy were enrolled and underwent ophthalmological assessment, MRI of the orbits, and laboratory tests. The results showed that patients in the high IgG4 group had a higher prevalence of active orbitopathy, a higher eosinophil count, a lower bilirubin level, a more frequent lower eyelid retraction, and a lower prevalence of glaucoma.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Lu Zheng, Zhen He, Shuguang He
Summary: Researchers propose a method for product defect discovery based on social media data, using the integrated BERT and Random Forest techniques to identify defect-related texts and the Defect Analysis Model (DAM) for defect discovery. They further prioritize the discovered defects using the Two-Phased Quality Function Deployment for Defect (TPQFDD) method to gain more inspired managerial insights.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Genetics & Heredity
Johann Kaspar Lieberwirth, Benjamin Buettner, Chiara Kloeckner, Konrad Platzer, Bernt Popp, Rami Abou Jamra
Summary: This study developed an automated evaluation method named AutoCaSc based on a candidate scoring scheme, which showed high accuracy and reliability in unsolved NDD cases. The method can quickly screen and identify candidate genes in NDD, helping to determine potential new NDD genes.
Article
Materials Science, Multidisciplinary
Zhigang Ding, Junjun Zhou, Piao Yang, Haoran Sun, Ji-Chang Ren, Yonghao Zhao, Wei Liu
Summary: We propose an active learning guided density functional theory calculation framework for rapid screening of multi-principal element alloys (MPEAs) with superior mechanical properties. Using this framework, datasets of mechanical properties for 12,698 noble metal MPEAs were constructed with high prediction accuracy based on active learning guided DFT calculated data. The analysis of the dataset revealed that Ni and Au could enhance the yield strength and toughness of these noble MPEAs, respectively.
MATERIALS RESEARCH LETTERS
(2023)
Article
Biochemical Research Methods
Peifeng Ruan, Shuang Wang
Summary: Biological network-based strategies are useful in prioritizing genes associated with diseases, but general networks may not accurately reflect gene interactions for a specific disease. By proposing the DiSNEP framework, which enhances disease-specific gene networks, more true disease-associated genes can be identified compared to other methods.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Engineering, Chemical
Jonathan A. Ouimet, Xinhong Liu, David J. Brown, Elvis A. Eugene, Tylar Popps, Zachary W. Muetzel, Alexander W. Dowling, William A. Phillip
Summary: This study emphasizes the importance of improved characterization techniques in advancing membrane processes. By developing a diafiltration apparatus guided by data science, membrane performance can be rapidly characterized over a broad range of feed solution compositions. The synergy between data analytics and instrumentation, including an inline conductivity probe, allows for accurate determination of membrane transport coefficients and identification of governing phenomena.
JOURNAL OF MEMBRANE SCIENCE
(2022)
Article
Biochemical Research Methods
Ekta Shah, Pradipta Maji
Summary: In the past few decades, research has extensively studied gene expression data and protein-protein interaction networks for their ability to depict important characteristics of disease-associated genes. This paper introduces a new gene prioritization algorithm that integrates information from both data sources to identify and prioritize cancer-causing genes. The proposed algorithm aims to maximize the importance of selected genes and their functional similarity, combining differential expression patterns and network connectivity as key features for potential biomarker discovery. Additionally, a scalable non-linear graph fusion technique, ScaNGraF, is proposed to learn disease-dependent functional similarity networks efficiently and with lower computational cost.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Ege Ulgen, O. Ugur Sezerman
Summary: The study developed a personalized/batch analysis approach, driveR, which accurately prioritizes cancer driver genes by combining genomics information and prior biological knowledge. Testing showed that driveR performs well and outperforms existing methods. The proposed method is user-friendly and plays a significant role in cancer genomics.
BMC BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Shuaiqun Wang, Kai Zheng, Wei Kong, Ruiwen Huang, Lulu Liu, Gen Wen, Yaling Yu
Summary: Currently, the study on the pathogenesis of Alzheimer's disease (AD) using multimodal data fusion analysis has attracted wide attention. However, existing models have limitations in considering the non-linear relationship between brain regions and genes as well as the local optimal solution problem of genetic evolution algorithm. To address these issues, this study proposed an improved genetic evolution random neural network cluster (IGERNNC) model, which showed better performance in identifying AD patients and extracting relevant pathogenic factors through multiple independent experimental comparisons.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Chemistry, Analytical
Gerjen H. Tinnevelt, Kristiaan Wouters, Geert J. Postma, Rita Folcarelli, Jeroen J. Jansen
Summary: White blood cells play a crucial role in protecting the body from diseases, but can also be related to chronic inflammation, auto immune diseases, or leukemia. High-throughput analytical instruments have been developed to measure multiple proteins on millions of single cells to study the identity and function of different white blood cell types. Multivariate statistics are essential for fully extracting the information-rich biochemistry data. The process involves analyzing the study design, formulating a research question, preparing the data, converting single cells into a cellular distribution, and using clustering methods or models for analysis to differentiate cell (sub)types between groups.
ANALYTICA CHIMICA ACTA
(2021)
Article
Pathology
Saffet Ulutas, Mehmet Mutaf, Mustafa Nihat Koc, Tarik Oztuzcu, Mustafa Ulasli, Serdar Oztuzcu
Summary: This study aimed to investigate the role of autophagy in basal cell carcinoma. A high-throughput qPCR approach was used to screen 72 autophagy-related genes, and it was found that IFNA2 expression was significantly altered in basal cell carcinoma and may play a significant role in the development and progression of the disease.
PATHOLOGY RESEARCH AND PRACTICE
(2023)