Journal of Integrative Bioinformatics最新文献

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction. MCMVDRP：用于癌症药物反应预测的多通道多视角深度学习框架。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-09-02 DOI: 10.1515/jib-2024-0026

Xiangyu Li, Xiumin Shi, Yuxuan Li, Lu Wang

{"title":"MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.","authors":"Xiangyu Li, Xiumin Shi, Yuxuan Li, Lu Wang","doi":"10.1515/jib-2024-0026","DOIUrl":"https://doi.org/10.1515/jib-2024-0026","url":null,"abstract":"Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often results in divergent therapeutic responses to analogous anti-cancer drug treatments within the same cohort of cancer patients. Hence, predicting the drug response by analysing the genomic profile characteristics of individual patients holds significant research importance. With the notable progress in machine learning and deep learning, many effective methods have emerged for predicting drug responses utilizing features from both drugs and cell lines. However, these methods are inadequate in capturing a sufficient number of features inherent to drugs. Consequently, we propose a representational approach for drugs that incorporates three distinct types of features: the molecular graph, the SMILE strings, and the molecular fingerprints. In this study, a novel deep learning model, named MCMVDRP, is introduced for the prediction of cancer drug responses. In our proposed model, an amalgamation of these extracted features is performed, followed by the utilization of fully connected layers to predict the drug response based on the IC50 values. Experimental results demonstrate that the presented model outperforms current state-of-the-art models in performance.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leonhard Med, a trusted research environment for processing sensitive research data. Leonhard Med，一个用于处理敏感研究数据的可信研究环境。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-08-02 DOI: 10.1515/jib-2024-0021

Michal J Okoniewski, Anna Wiegand, Diana Coman Schmid, Christian Bolliger, Cristian Bovino, Mattia Belluco, Thomas Wüst, Olivier Byrde, Sergio Maffioletti, Bernd Rinn

{"title":"Leonhard Med, a trusted research environment for processing sensitive research data.","authors":"Michal J Okoniewski, Anna Wiegand, Diana Coman Schmid, Christian Bolliger, Cristian Bovino, Mattia Belluco, Thomas Wüst, Olivier Byrde, Sergio Maffioletti, Bernd Rinn","doi":"10.1515/jib-2024-0021","DOIUrl":"https://doi.org/10.1515/jib-2024-0021","url":null,"abstract":"This paper provides an overview of the development and operation of the Leonhard Med Trusted Research Environment (TRE) at ETH Zurich. Leonhard Med gives scientific researchers the ability to securely work on sensitive research data. We give an overview of the user perspective, the legal framework for processing sensitive data, design history, current status, and operations. Leonhard Med is an efficient, highly secure Trusted Research Environment for data processing, hosted at ETH Zurich and operated by the Scientific IT Services (SIS) of ETH. It provides a full stack of security controls that allow researchers to store, access, manage, and process sensitive data according to Swiss legislation and ETH Zurich Data Protection policies. In addition, Leonhard Med fulfills the BioMedIT Information Security Policies and is compatible with international data protection laws and therefore can be utilized within the scope of national and international collaboration research projects. Initially designed as a \"bare-metal\" High-Performance Computing (HPC) platform to achieve maximum performance, Leonhard Med was later re-designed as a virtualized, private cloud platform to offer more flexibility to its customers. Sensitive data can be analyzed in secure, segregated spaces called tenants. Technical and Organizational Measures (TOMs) are in place to assure the confidentiality, integrity, and availability of sensitive data. At the same time, Leonhard Med ensures broad access to cutting-edge research software, especially for the analysis of human -omics data and other personalized health applications.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring animal behaviour multilayer networks in immersive environments - a conceptual framework. 探索身临其境环境中的动物行为多层网络--一个概念框架。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-24 DOI: 10.1515/jib-2024-0022

Stefan Paul Feyer, Bruno Pinaud, Karsten Klein, Etienne Lein, Falk Schreiber

引用次数: 0

Inferences on the evolution of the ascorbic acid synthesis pathway in insects using Phylogenetic Tree Collapser (PTC), a tool for the automated collapsing of phylogenetic trees using taxonomic information. 利用系统发生树折叠器（PTC）推断昆虫抗坏血酸合成途径的进化，PTC 是一种利用分类信息自动折叠系统发生树的工具。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-24 eCollection Date: 2024-06-01 DOI: 10.1515/jib-2023-0051

Daniel Glez-Peña, Hugo López-Fernández, Pedro Duque, Cristina P Vieira, Jorge Vieira

{"title":"Inferences on the evolution of the ascorbic acid synthesis pathway in insects using Phylogenetic Tree Collapser (PTC), a tool for the automated collapsing of phylogenetic trees using taxonomic information.","authors":"Daniel Glez-Peña, Hugo López-Fernández, Pedro Duque, Cristina P Vieira, Jorge Vieira","doi":"10.1515/jib-2023-0051","DOIUrl":"10.1515/jib-2023-0051","url":null,"abstract":"When inferring the evolution of a gene/gene family, it is advisable to use all available coding sequences (CDS) from as many species genomes as possible in order to infer and date all gene duplications and losses. Nowadays, this means using hundreds or even thousands of CDSs, which makes the inferred phylogenetic trees difficult to visualize and interpret. Therefore, it is useful to have an automated way of collapsing large phylogenetic trees according to a taxonomic term decided by the user (family, class, or order, for instance), in order to highlight the minimal set of sequences that should be used to recapitulate the full history of the gene/gene family being studied at that taxonomic level, that can be refined using additional software. Here we present the Phylogenetic Tree Collapser (PTC) program (https://github.com/pegi3s/phylogenetic-tree-collapser), a flexible tool for automated tree collapsing using taxonomic information, that can be easily used by researchers without a background in informatics, since it only requires the installation of Docker, Podman or Singularity. The utility of PTC is demonstrated by addressing the evolution of the ascorbic acid synthesis pathway in insects. A Docker image is available at Docker Hub (https://hub.docker.com/r/pegi3s/phylogenetic-tree-collapser) with PTC installed and ready-to-run.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Specifications of standards in systems and synthetic biology: status, developments, and tools in 2024. 系统和合成生物学标准规范：2024 年的现状、发展和工具。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-22 eCollection Date: 2024-03-01 DOI: 10.1515/jib-2024-0015

Martin Golebiewski, Gary Bader, Padraig Gleeson, Thomas E Gorochowski, Sarah M Keating, Matthias König, Chris J Myers, David P Nickerson, Björn Sommer, Dagmar Waltemath, Falk Schreiber

引用次数: 0

Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction. 检测病例对照队列中的异常值，改进深度学习网络对精神分裂症的预测。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-15 eCollection Date: 2024-06-01 DOI: 10.1515/jib-2023-0042

Daniel Martins, Maryam Abbasi, Conceição Egas, Joel P Arrais

{"title":"Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction.","authors":"Daniel Martins, Maryam Abbasi, Conceição Egas, Joel P Arrais","doi":"10.1515/jib-2023-0042","DOIUrl":"10.1515/jib-2023-0042","url":null,"abstract":"This study delves into the intricate genetic and clinical aspects of Schizophrenia, a complex mental disorder with uncertain etiology. Deep Learning (DL) holds promise for analyzing large genomic datasets to uncover new risk factors. However, based on reports of non-negligible misdiagnosis rates for SCZ, case-control cohorts may contain outlying genetic profiles, hindering compelling performances of classification models. The research employed a case-control dataset sourced from the Swedish populace. A gene-annotation-based DL architecture was developed and employed in two stages. First, the model was trained on the entire dataset to highlight differences between cases and controls. Then, samples likely to be misclassified were excluded, and the model was retrained on the refined dataset for performance evaluation. The results indicate that SCZ prevalence and misdiagnosis rates can affect case-control cohorts, potentially compromising future studies reliant on such datasets. However, by detecting and filtering outliers, the study demonstrates the feasibility of adapting DL methodologies to large-scale biological problems, producing results more aligned with existing heritability estimates for SCZ. This approach not only advances the comprehension of the genetic background of SCZ but also opens doors for adapting DL techniques in complex research for precision medicine in mental health.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Layout of anatomical structures and blood vessels based on the foundational model of anatomy. 根据解剖学基础模型，布局解剖结构和血管。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-12 DOI: 10.1515/jib-2024-0023

Niklas Gröne, Benjamin Grüneisen, Karsten Klein, Bernard de Bono, Tobias Czauderna, Falk Schreiber

引用次数: 0

Transformers meets neoantigen detection: a systematic literature review. 变形金刚与新抗原检测：系统文献综述。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-07-04 eCollection Date: 2024-06-01 DOI: 10.1515/jib-2023-0043

Vicente Machaca, Valeria Goyzueta, María Graciel Cruz, Erika Sejje, Luz Marina Pilco, Julio López, Yván Túpac

引用次数: 0

MakeSBML: a tool for converting between Antimony and SBML. MakeSBML：在 Antimony 和 SBML 之间进行转换的工具。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-06-11 eCollection Date: 2024-03-01 DOI: 10.1515/jib-2024-0002

Bartholomew E Jardine, Lucian P Smith, Herbert M Sauro

引用次数: 0

SBMLToolkit.jl: a Julia package for importing SBML into the SciML ecosystem. SBMLToolkit.jl：将 SBML 导入 SciML 生态系统的 Julia 软件包。

IF 1.5

Journal of Integrative Bioinformatics Pub Date : 2024-05-28 eCollection Date: 2024-03-01 DOI: 10.1515/jib-2024-0003

Paul F Lang, Anand Jain, Christopher Rackauckas

{"title":"SBMLToolkit.jl: a Julia package for importing SBML into the SciML ecosystem.","authors":"Paul F Lang, Anand Jain, Christopher Rackauckas","doi":"10.1515/jib-2024-0003","DOIUrl":"10.1515/jib-2024-0003","url":null,"abstract":"Julia is a general purpose programming language that was designed for simplifying and accelerating numerical analysis and computational science. In particular the Scientific Machine Learning (SciML) ecosystem of Julia packages includes frameworks for high-performance symbolic-numeric computations. It allows users to automatically enhance high-level descriptions of their models with symbolic preprocessing and automatic sparsification and parallelization of computations. This enables performant solution of differential equations, efficient parameter estimation and methodologies for automated model discovery with neural differential equations and sparse identification of nonlinear dynamics. To give the systems biology community easy access to SciML, we developed SBMLToolkit.jl. SBMLToolkit.jl imports dynamic SBML models into the SciML ecosystem to accelerate model simulation and fitting of kinetic parameters. By providing computational systems biologists with easy access to the open-source Julia ecosystevnm, we hope to catalyze the development of further Julia tools in this domain and the growth of the Julia bioscience community. SBMLToolkit.jl is freely available under the MIT license. The source code is available at https://github.com/SciML/SBMLToolkit.jl.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0