Article
Oncology
Linan Cao, Pei Liu, Jialong Chen, Lei Deng
Summary: In this study, we developed an accurate and interpretable attention-based hybrid approach called DeepARC, which combines CNN and RNN to predict TFBS. DeepARC utilizes a positional embedding method to extract hidden embeddings from DNA sequences and uses a CNN-BiLSTM-Attention framework to search for motifs. Our results demonstrate that DeepARC achieves promising performances on multiple cell lines and provides interpretability through attention weight graphs.
FRONTIERS IN ONCOLOGY
(2022)
Article
Biology
Md Faruk Hosen, S. M. Hasan Mahmud, Kawsar Ahmed, Wenyu Chen, Mohammad Ali Moni, Hong-Wen Deng, Watshara Shoombuatong, Md Mehedi Hasan
Summary: In this paper, a novel predictor called DeepDNAbP has been developed to accurately predict DNA-binding proteins (DBPs) using a convolutional neural network model. The predictor achieves superior performance in cross-validation tests and outperforms existing methods, making it a powerful computational resource for predicting DBPs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biology
Yuening Zhang, Li Qiu, Yongyong Ren, Zhiwei Cheng, Leijie Li, Siqiong Yao, Chengdong Zhang, Zhiguo Luo, Hui Lu
Summary: Using the meta-learning framework, we developed an approach to improve individual radiation response prediction. By transferring common knowledge from pan-cancer data to specific cancers, our models demonstrated good performance and biological significance across nine cancer types.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biochemical Research Methods
Lei Xu, Shanshan Jiang, Jin Wu, Quan Zou
Summary: Exploring the function of proteins in protein-nucleic acid interactions is important for understanding related biological events and predicting these interactions. Establishing databases by collecting and identifying protein sequence information helps in predicting protein function, leading to improved prediction accuracy.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Fang Jing, Shao-Wu Zhang, Shihua Zhang
Summary: In this study, a meta learning-based CNN method (MLCNN) was proposed for accurately identifying TFBSs from ChIP-seq data. By being guided by a small amount of unbiased meta-data, MLCNN can adaptively learn a weighting function and overcome the influence of biased training data on the classifier. Experimental results demonstrate that MLCNN outperforms other CNN methods and can detect and suppress noisy samples.
Article
Cell Biology
Zewei Tu, Lei Shu, Jingying Li, Lei Wu, Chuming Tao, Minhua Ye, Xingen Zhu, Kai Huang
Summary: In this study, a risk signature was constructed using 14 prognostic RBP genes to predict the prognosis of glioma patients. The RBP-signature showed robust prognostic value in various cohorts and was able to efficiently identify high-risk gliomas. It was associated with immune cell activation and key signaling pathways, serving as an independent prognostic factor for OS.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2021)
Article
Biochemical Research Methods
Chichi Dai, Pengmian Feng, Lizhen Cui, Ran Su, Wei Chen, Leyi Wei
Summary: The study developed a machine learning-based method (m(7)G-IFL) to identify m(7)G sites, extracting more discriminative features through the iterative feature learning process to improve predictive performance.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Multidisciplinary Sciences
Niraj Thapa, Meenal Chaudhari, Anthony A. Iannetta, Clarence White, Kaushik Roy, Robert H. Newman, Leslie M. Hicks, Dukka B. Kc
Summary: This study presented a novel deep learning approach for predicting protein phosphorylation sites in the model algal phototroph Chlamydomonas reinhardtii. The developed ensemble model showed high accuracy in predicting phosphorylation sites and successfully identified experimentally confirmed sites, demonstrating its robustness and potential as a useful tool for high-throughput phosphorylation site prediction in C. reinhardtii.
SCIENTIFIC REPORTS
(2021)
Article
Biochemistry & Molecular Biology
R. Sanchez-Garcia, J. R. Macias, C. O. S. Sorzano, J. M. Carazo, J. Segura
Summary: Computational approaches for predicting protein-protein interfaces are important for understanding protein assemblies. The performance of these methods can be improved by selecting specific training datasets. BIPSPI+ is an upgraded version trained on carefully curated datasets, providing better predictions and new functionalities.
JOURNAL OF MOLECULAR BIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Ajay Arya, Dana Mary Varghese, Ajay Kumar Verma, Shandar Ahmad
Summary: Prediction of DNA-binding residues in proteins using sequence-based methods have been widely studied. The current primary feature set, Position Specific Substitution Matrix (PSSM), is powerful for identifying conserved binding sites but falls short for residues undergoing binding to non-binding transitions.
JOURNAL OF MOLECULAR BIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Nesrine Baatallah, Ahmad Elbahnsi, Jean-Paul Mornon, Benoit Chevalier, Iwona Pranke, Nathalie Servel, Renaud Zelli, Jean-Luc Decout, Aleksander Edelman, Isabelle Sermet-Gaudelus, Isabelle Callebaut, Alexandre Hinzpeter
Summary: Protein misfolding is associated with various diseases, including cystic fibrosis. Correctors like VX-809, VX-661, and VX-445 have been developed to rescue mutant proteins; through blind docking and molecular dynamics simulations, potential binding sites and mechanisms of action have been identified. These correctors stabilize protein-lipid interfaces and enhance inter-domain assembly, providing novel insights into rescuing misfolded proteins with small molecules.
CELLULAR AND MOLECULAR LIFE SCIENCES
(2021)
Article
Biochemical Research Methods
Zhengfeng Wang, Xiujuan Lei
Summary: A deep learning framework CRPBsites was designed to predict the binding sites of RBPs on circRNAs, showing superior performance in experimental results and discovering highly similar binding motifs. The well-trained model successfully identified the binding sites of IGF2BP1 on circCDYL.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Engineering, Marine
Stefan F. Fahrnholz, Jean-David Caprace
Summary: Naval and ocean engineers estimate the installed propulsion power aboard a boat by assessing hull resistance through the water. Existing models for predicting sailboat resistance face difficulties at low speeds. This study proposes a unique machine learning model that efficiently predicts the total resistance of bare-hull sailboats, including at low speeds.
Article
Mathematical & Computational Biology
Engin Aybey, Ozgur Gumus
Summary: This study introduces a new prediction method for protein-protein interaction sites (PPISs), which combines multiple models and two embedded features to improve prediction performance. Experimental results show that this method outperforms other methods on independent testing datasets, especially with significant improvements in sensitivity, F1, MCC, and AUPRC.
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
(2023)
Article
Biochemical Research Methods
Chenran Wang, Yang Chen, Yuan Zhang, Keqiao Li, Menghan Lin, Feng Pan, Wei Wu, Jinfeng Zhang
Summary: Protein ligand docking is a computational tool for predicting protein functions and screening drug candidates. In this study, a novel reinforcement learning approach called A3C was developed to address the challenging problem of protein ligand docking. The experimental results showed significant improvement in binding site prediction compared to a naive model.
BMC BIOINFORMATICS
(2022)
Meeting Abstract
Oncology
Waqas Azeem, Margrete R. Hellem, Jan R. Olsen, Yaping Hua, Kristo Marvyin, Lisha Li, Yi Qu, Biaoyang Lin, Xisong Ke, Anne M. Oyan, Karl-Henning Kalland
Article
Oncology
Jan Roger Olsen, Waqas Azeem, Margrete Reime Hellem, Kristo Marvyin, Yaping Hua, Yi Qu, Lisha Li, Biaoyang Lin, Xi-Song Ke, Anne Margrete Oyan, Karl-Henning Kalland
Meeting Abstract
Oncology
Jan Roger Olsen, Waqas Azeem, Margrete R. Hellem, Kristo Marvyin, Yaping Hua, Yi Qu, Lisha Li, Biaoyang Lin, XiSong Ke, Anne Margrete Oyan, Karl-Henning Kalland
Meeting Abstract
Oncology
Waqas Azeem, Margrete R. Hellem, Jan R. Olsen, Yaping Hua, Kristo Marvyin, Lisha Li, Yi Qu, Biaoyang Lin, Xisong Ke, Anne M. Oyan, Karl-Henning Kalland
Letter
Biotechnology & Applied Microbiology
Eyup Ilker Saygili, Alaa H. Abou-Zeid, Salih Murat Akkin, Eleni Aklillu, Ibrahim Omer Barlas, Alexander Borda-Rodriguez, Filiz Aydogan Boschele, Zafer Cetin, Enes Coskun, Yavuz Coskun, Guner Dagli, Turkan Ugur Dai, Collet Dandara, Turkay Dereli, Levent Elbeyli, Laszlo Endrenyi, Can Polat Eyigun, Alexandros Georgakilas, Bircan Gunbulut, Kivanc Gungor, Asim Guzelbey, Can Hekim, Farah Huzair, Sabit Kimyon, Umit Karakas, Biaoyang Lin, Adrian LLerena, Collen Masimirembwa, Ruth McNally, Alper Mete, Pesvin Sancar, Sanjeeva Srivastava, Lotte M. Steuten, Oylum Tanriover, David Tyfield, Volkan Ihsan Tore, Deniz Vuruskan, Wei Wang, Louise Warnich, Ambroise Wonkam, Yusuf Ziya Yildirim, Ismet Yilmaz, Ahmet Sinav, Nezih Hekim
OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY
(2016)
Article
Multidisciplinary Sciences
Yi Qu, Naouel Gharbi, Xing Yuan, Jan Roger Olsen, Pernille Blicher, Bjorn Dalhus, Karl A. Brokstad, Biaoyang Lin, Anne Margrete Oyan, Weidong Zhang, Karl-Henning Kalland, Xisong Ke
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2016)
Article
Multidisciplinary Sciences
Waqas Azeem, Margrete Reime Hellem, Jan Roger Olsen, Yaping Hua, Kristo Marvyin, Yi Qu, Biaoyang Lin, Xisong Ke, Anne Margrete Oyan, Karl-Henning Kalland
Article
Oncology
Ruifang Mao, Jie Liu, Guanfeng Liu, Shanshan Jin, Qingzhong Xue, Liang Ma, Yan Fu, Na Zhao, Jinliang Xing, Lanjuan Li, Yunqing Qiu, Biaoyang Lin
Article
Biophysics
Liang Ma, Bin Zhang, Changchun Zhou, Yuting Li, Binjie Li, Mengfei Yu, Yichen Luo, Lei Gao, Duo Zhang, Qian Xue, Qingchong Qiu, Biaoyang Lin, Jun Zou, Huayong Yang
COLLOIDS AND SURFACES B-BIOINTERFACES
(2018)
Article
Multidisciplinary Sciences
Qing-Chong Qiu, Lin Wang, Shan-Shan Jin, Guan-Feng Liu, Jie Liu, Liang Ma, Rui-Fang Mao, Ying-Ying Ma, Na Zhao, Ming Chen, Biao-Yang Lin
SCIENTIFIC REPORTS
(2018)
Review
Biotechnology & Applied Microbiology
Biaoyang Lin, Yingying Ma, ShengJun Wu
Summary: Chronic liver disease (CLD) is a global health burden, and there is a need for improved biomarkers, diagnostic methods, and the integration of multi-omics data with the help of artificial intelligence. This review summarizes the emerging frontiers and challenges in multi-omics data integration in liver pathology research, as well as the impact of digital transformation in this field.
OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY
(2022)
Review
Biotechnology & Applied Microbiology
Biaoyang Lin, Shengjun Wu
Summary: Digital transformation is revolutionizing personalized medicine and systems science by enabling real-time patient feedback and in-depth phenotypic analysis with advanced technologies like artificial intelligence and the Internet of Medical Things.
OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY
(2022)
Review
Biotechnology & Applied Microbiology
Biaoyang Lin, Yingying Ma, Shengjun Wu, Yunhua Liu, Longgen Liu, Lihua Wu
OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY
(2019)
Article
Biotechnology & Applied Microbiology
Jie Liu, Ruifang Mao, Guoping Ren, Xiaoyan Liu, Yanling Zhang, Jili Wang, Yan Wang, Meiling Li, Qingchong Qiu, Lin Wang, Guanfeng Liu, Shanshan Jin, Liang Ma, Yingying Ma, Na Zhao, Jiajun Yan, Hongwei Zhang, Biaoyang Lin
OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY
(2019)
Letter
Biotechnology & Applied Microbiology
Biaoyang Lin
OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY
(2019)