Article
Engineering, Electrical & Electronic
Rakesh Kumar Palani, Srikar Bhagavatula, Denny K. Yuen
Summary: This paper proposes a new sub-1V discrete-time CMOS bandgap reference circuit, where a constant voltage is generated by sampling voltages onto a capacitor, eliminating noise and offset concerns in low voltage bandgap designs.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Water Resources
Jacob Staines, John W. Pomeroy
Summary: Vegetation structure is an important factor in shaping the spatial variation of snow accumulation under forest canopies. However, the fine-scale relationships between canopy density, snow interception, wind redistribution, and sub-canopy accumulation are poorly understood. This study analyzed forest structure and sub-canopy snow accumulation to assess the impact of snow-canopy interactions on spatial patterns of sub-canopy snow accumulation.
HYDROLOGICAL PROCESSES
(2023)
Article
Computer Science, Information Systems
Vaibhav Rupapara, Furqan Rustam, Hina Fatima Shahzad, Arif Mehmood, Imran Ashraf, Gyu Sang Choi
Summary: This study introduces an ensemble approach called regression vector voting classifier (RVVC) to identify toxic comments on social media platforms and analyzes its performance through experiments. The results show that RVVC outperforms other models when using TF-IDF features with a SMOTE balanced dataset, achieving an accuracy of 0.97.
Article
Health Care Sciences & Services
Debarshi Datta, Safiya George Dalmida, Laurie Martinez, David Newman, Javad Hashemi, Taghi M. Khoshgoftaar, Connor Shorten, Candice Sareli, Paula Eckardt
Summary: This study aims to develop an AI-driven decision support system to predict the mortality of COVID-19 hospitalized patients. The important features that predict mortality include age, diarrhea, diabetes, hypertension, BMI, early stages of kidney disease, smoking status, sex, pneumonia, and race. The model demonstrates the ability to predict mortality with transparency and reliability.
FRONTIERS IN DIGITAL HEALTH
(2023)
Article
Biotechnology & Applied Microbiology
Zhibin Lv, Jun Zhang, Hui Ding, Quan Zou
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2020)
Article
Computer Science, Artificial Intelligence
Zhibin Lv, Hui Ding, Lei Wang, Quan Zou
Summary: N6-methyladenine (m(6)A) is a crucial epigenetic modification related to the control of various DNA processes. The iRicem6A-CNN protocol, using machine learning, achieved high accuracy in identifying m(6)A sites in the rice genome, outperforming other predictors.
Article
Biochemical Research Methods
Zhibin Lv, Feifei Cui, Quan Zou, Lichao Zhang, Lei Xu
Summary: The study introduced a computational method named iACP-DRLF for identifying anticancer peptides, utilizing light gradient boosting machine algorithm and two sequence embedding technologies. Results showed that deep representation learning features significantly enhanced the models' ability to differentiate anticancer peptides.
BRIEFINGS IN BIOINFORMATICS
(2021)
Editorial Material
Biotechnology & Applied Microbiology
Ni Yan, Zhibin Lv, Wenjing Hong, Xue Xu
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Qian Zhao, Jiaqi Ma, Yu Wang, Fang Xie, Zhibin Lv, Yaoqun Xu, Hua Shi, Ke Han
Summary: SNO is crucial for plant immune response and human disease treatment, with the efficient prediction tool Mul-SNO showing promising results.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Jici Jiang, Xinxu Lin, Yueqi Jiang, Liangzhen Jiang, Zhibin Lv
Summary: This study presents the development of a machine learning prediction method called iBitter-DRLF, based on deep learning techniques, to accurately identify bitter peptides. By utilizing deep representation learning, this method can make accurate predictions solely based on peptide sequence data. This is of significant importance for improving the palatability of peptide therapeutics and dietary supplements.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Genetics & Heredity
Mingxin Li, Yu Fan, Yiting Zhang, Zhibin Lv
Summary: The research focused on the impact of different feature information of miRNA sequences on the relationship between miRNA and disease. It found that a better graph neural network prediction model of miRNA-disease relationship could be built using CKSNAP feature, and the predicted miRNAs related to lung tumors, esophageal tumors, and kidney tumors were consistent with the wet experiment validation database.
Article
Genetics & Heredity
Liangzhen Jiang, Changying Liu, Yu Fan, Qi Wu, Xueling Ye, Qiang Li, Yan Wan, Yanxia Sun, Liang Zou, Dabing Xiang, Zhibin Lv
Summary: - This study assessed the transcriptional dynamics of filling stage Tartary buckwheat seeds and identified key genes related to seed development through RNA sequencing. Phytohormones ABA, AUX, ET, BR and CTK, along with related TFs, were found to substantially regulate seed development by targeting downstream expansin genes and structural starch biosynthetic genes. The transcriptome data could serve as a theoretical basis for improving the yield of Tartary buckwheat.
FRONTIERS IN GENETICS
(2022)
Editorial Material
Genetics & Heredity
Zhibin Lv, Mingxin Li, Yansu Wang, Quan Zou
FRONTIERS IN GENETICS
(2023)
Article
Food Science & Technology
Liangzhen Jiang, Jici Jiang, Xiao Wang, Yin Zhang, Bowen Zheng, Shuqi Liu, Yiting Zhang, Changying Liu, Yan Wan, Dabing Xiang, Zhibin Lv
Summary: This study developed a peptide sequence-based umami peptide predictor, iUP-BERT, using a deep learning pretrained neural network feature extraction method. After optimization, the model showed improved performance compared to existing methods. The built iUP-BERT web server can aid in improving the palatability of dietary supplements.
Article
Chemistry, Multidisciplinary
Hongdi Pei, Jiayu Li, Shuhan Ma, Jici Jiang, Mingxin Li, Quan Zou, Zhibin Lv
Summary: Thermophilic proteins have the potential to be used as biocatalysts in biotechnology. BertThermo, a model using BERT as an automatic feature extraction tool, achieved high accuracy in identifying thermophilic proteins. It outperformed previous predictive algorithms and demonstrated robustness in various datasets.+
APPLIED SCIENCES-BASEL
(2023)
Article
Food Science & Technology
Jici Jiang, Jiayu Li, Junxian Li, Hongdi Pei, Mingxin Li, Quan Zou, Zhibin Lv
Summary: A deep learning method called iUmami-DRLF was developed to identify umami peptides based solely on peptide sequence information. The results show that deep learning significantly improved the capability of models in identifying umami peptides. This method can be used to further enhance the umami flavor of food for a satisfying umami-flavored diet.
Article
Biochemistry & Molecular Biology
Yiting Deng, Shuhan Ma, Jiayu Li, Bowen Zheng, Zhibin Lv
Summary: Anticancer peptides (ACPs) are a promising new therapeutic approach in cancer treatment, as they can selectively target cancer cells. This study utilized machine learning algorithms to predict potential ACP sequences based on physicochemical features extracted from peptide sequences. By using feature selection methods, 19 key amino acid physicochemical properties were identified that can predict the likelihood of a peptide sequence functioning as an ACP. The study aims to enhance the efficiency of designing peptide sequences for cancer treatment.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Chemistry, Multidisciplinary
Jiayu Li, Jici Jiang, Hongdi Pei, Zhibin Lv
Summary: A new IL-10-induced peptide recognition method called IL10-Stack was introduced in this research, which utilized unified deep representation learning and a stacking algorithm. Feature extraction from peptide sequences was done using two approaches, Amino Acid Index (AAindex) and sequence-based unified representation (UniRep). The IL10-Stack model, constructed using a 1900-dimensional UniRep feature vector, demonstrated excellent performance in IL-10-induced peptide recognition with an accuracy of 0.910 and MCC of 0.820. Compared to existing methods, IL-10Pred and ILeukin10Pred, the IL10-Stack approach showed improved accuracy by 12.1% and 2.4% respectively. The IL10-Stack method has the potential to identify IL-10-induced peptides, aiding in the development of immunosuppressive drugs.
APPLIED SCIENCES-BASEL
(2023)
Review
Multidisciplinary Sciences
Jiayu Li, Shuhan Ma, Hongdi Pei, Jici Jiang, Quan Zou, Zhibin Lv
Summary: This review focuses on the development of Tcprs for solid tumor therapy and prognostic prediction, and proposes strategies to enhance CAR-T cells through targeting different Tcprs, which may lead to the development of a new generation of cell therapies.