Artificial Neural Network Prediction of COVID-19 Daily Infection Count
This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output...
Source: Bulletin of Mathematical Biology - April 1, 2024 Category: Bioinformatics Authors: Ning Jiang Charles Kolozsvary Yao Li Source Type: research
PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data
In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and trea...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Chunjiang Yu Hui Zong Yalan Chen Yibin Zhou Xingyun Liu Yuxin Lin Jiakun Li Xiaonan Zheng Hua Min Bairong Shen Source Type: research
SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data
Brief Bioinform. 2024 Mar 27;25(3):bbae129. doi: 10.1093/bib/bbae129.ABSTRACTQuality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such ra...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Jinze Huang Yang Zhao Bo Meng Ao Lu Yaoguang Wei Lianhua Dong Xiang Fang Dong An Xinhua Dai Source Type: research
SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding
In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network-based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Wang Yin You Wan Yuan Zhou Source Type: research
Multilevel superposition for deciphering the conformational variability of protein ensembles
In this study, the author developed a multilevel model for estimating two covariance matrices that represent inter- and intra-ensemble variability in the Cartesian coordinate space. Principal component analysis using the two estimated covariance matrices identified the inter-/intra-enzyme variabilities, which seemed to be important for the enzyme functions, with the illustrative examples of cytochrome P450 family 2 enzymes and class A $\beta$-lactamases. In P450, in which each enzyme has its own active site of a distinct size, an active-site motion shared universally between the enzymes was captured as the first principal ...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Takashi Amisaki Source Type: research
Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology
Brief Bioinform. 2024 Mar 27;25(3):bbae131. doi: 10.1093/bib/bbae131.ABSTRACTUnderstanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Theinmozhi Arulraj Hanwen Wang Alberto Ippolito Shuming Zhang Elana J Fertig Aleksander S Popel Source Type: research
IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability
Brief Bioinform. 2024 Mar 27;25(3):bbae080. doi: 10.1093/bib/bbae080.ABSTRACTLung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Early-stage patients have a 30-50% probability of metastatic recurrence after surgical treatment. Here, we propose a new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), based on pathway hierarchy relationships to predict LUAD recurrence and explore the internal regulatory mechanisms of LUAD. IBPGNET can integrate different omics data efficiently and provide global interpretability. In addition, our experimental results show t...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Zhanyu Xu Haibo Liao Liuliu Huang Qingfeng Chen Wei Lan Shikang Li Source Type: research
Graphormer supervised de novo protein design method and function validation
Brief Bioinform. 2024 Mar 27;25(3):bbae135. doi: 10.1093/bib/bbae135.ABSTRACTProtein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inade...
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Junxi Mu Zhengxin Li Bo Zhang Qi Zhang Jamshed Iqbal Abdul Wadood Ting Wei Yan Feng Hai-Feng Chen Source Type: research
IMPRINTS.CETSA and IMPRINTS.CETSA.app: an R package and a Shiny application for the analysis and interpretation of IMPRINTS-CETSA data
We present a new algorithm to classify modulated proteins in IMPRINTS-CETSA experiments by a robust single-measure scoring. In this way, both the numerical changes and the statistical significances of the IMPRINTS information can be visualized on a single plot. The IMPRINTS.CETSA and IMPRINTS.CETSA.app R packages are freely available on GitHub at https://github.com/nkdailingyun/IMPRINTS.CETSA and https://github.com/mgerault/IMPRINTS.CETSA.app, respectively. IMPRINTS.CETSA.app is also available as an executable program at https://zenodo.org/records/10636134.PMID:38557673 | DOI:10.1093/bib/bbae128 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Marc-Antoine Gerault Samuel Granjeaud Luc Camoin P är Nordlund Lingyun Dai Source Type: research
MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction
Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is parti... (Source: BMC Bioinformatics)
Source: BMC Bioinformatics - April 1, 2024 Category: Bioinformatics Authors: Yunyun Dong, Yunqing Chang, Yuxiang Wang, Qixuan Han, Xiaoyuan Wen, Ziting Yang, Yan Zhang, Yan Qiang, Kun Wu, Xiaole Fan and Xiaoqiang Ren Tags: Research Source Type: research
DeepPLM_mCNN: An approach for enhancing ion channel and ion transporter recognition by multi-window CNN based on features from pre-trained language models
This study illustrates the potential of combining PLMs and deep learning for accurate computational identification of membrane proteins from sequence data alone. Our findings have important implications for membrane protein research and drug development targeting ion channels and transporters. The data and source codes in this study are publicly available at the following link: https://github.com/s1129108/DeepPLM_mCNN.PMID:38555810 | DOI:10.1016/j.compbiolchem.2024.108055 (Source: Computational Biology and Chemistry)
Source: Computational Biology and Chemistry - March 31, 2024 Category: Bioinformatics Authors: Van-The Le Muhammad-Shahid Malik Yi-Hsuan Tseng Yu-Cheng Lee Cheng-I Huang Yu-Yen Ou Source Type: research
DeepPLM_mCNN: An approach for enhancing ion channel and ion transporter recognition by multi-window CNN based on features from pre-trained language models
This study illustrates the potential of combining PLMs and deep learning for accurate computational identification of membrane proteins from sequence data alone. Our findings have important implications for membrane protein research and drug development targeting ion channels and transporters. The data and source codes in this study are publicly available at the following link: https://github.com/s1129108/DeepPLM_mCNN.PMID:38555810 | DOI:10.1016/j.compbiolchem.2024.108055 (Source: Computational Biology and Chemistry)
Source: Computational Biology and Chemistry - March 31, 2024 Category: Bioinformatics Authors: Van-The Le Muhammad-Shahid Malik Yi-Hsuan Tseng Yu-Cheng Lee Cheng-I Huang Yu-Yen Ou Source Type: research
DeepPLM_mCNN: An approach for enhancing ion channel and ion transporter recognition by multi-window CNN based on features from pre-trained language models
This study illustrates the potential of combining PLMs and deep learning for accurate computational identification of membrane proteins from sequence data alone. Our findings have important implications for membrane protein research and drug development targeting ion channels and transporters. The data and source codes in this study are publicly available at the following link: https://github.com/s1129108/DeepPLM_mCNN.PMID:38555810 | DOI:10.1016/j.compbiolchem.2024.108055 (Source: Computational Biology and Chemistry)
Source: Computational Biology and Chemistry - March 31, 2024 Category: Bioinformatics Authors: Van-The Le Muhammad-Shahid Malik Yi-Hsuan Tseng Yu-Cheng Lee Cheng-I Huang Yu-Yen Ou Source Type: research
DeepPLM_mCNN: An approach for enhancing ion channel and ion transporter recognition by multi-window CNN based on features from pre-trained language models
This study illustrates the potential of combining PLMs and deep learning for accurate computational identification of membrane proteins from sequence data alone. Our findings have important implications for membrane protein research and drug development targeting ion channels and transporters. The data and source codes in this study are publicly available at the following link: https://github.com/s1129108/DeepPLM_mCNN.PMID:38555810 | DOI:10.1016/j.compbiolchem.2024.108055 (Source: Computational Biology and Chemistry)
Source: Computational Biology and Chemistry - March 31, 2024 Category: Bioinformatics Authors: Van-The Le Muhammad-Shahid Malik Yi-Hsuan Tseng Yu-Cheng Lee Cheng-I Huang Yu-Yen Ou Source Type: research
Combinatorial Cooperativity in miR200-Zeb Feedback Network can Control Epithelial-Mesenchymal Transition
Bull Math Biol. 2024 Mar 30;86(5):48. doi: 10.1007/s11538-024-01277-1.ABSTRACTCarcinomas often utilize epithelial-mesenchymal transition (EMT) programs for cancer progression and metastasis. Numerous studies report SNAIL-induced miR200/Zeb feedback circuit as crucial in regulating EMT by placing cancer cells in at least three phenotypic states, viz. epithelial (E), hybrid (h-E/M), mesenchymal (M), along the E-M phenotypic spectrum. However, a coherent molecular-level understanding of how such a tiny circuit controls carcinoma cell entrance into and residence in various states is lacking. Here, we use molecular binding data...
Source: Bulletin of Mathematical Biology - March 30, 2024 Category: Bioinformatics Authors: Mubasher Rashid Brasanna M Devi Malay Banerjee Source Type: research
