journal
https://read.qxmd.com/read/38436570/gs-tcga-gene-set-based-analysis-of-the-cancer-genome-atlas
#1
JOURNAL ARTICLE
Tarrion Baird, Rahul Roychoudhuri
Most tools for analyzing large gene expression datasets, including The Cancer Genome Atlas (TCGA), have focused on analyzing the expression of individual genes or inference of the abundance of specific cell types from whole transcriptome information. While these methods provide useful insights, they can overlook crucial process-based information that may enhance our understanding of cancer biology. In this study, we describe three novel tools incorporated into an online resource; gene set-based analysis of The Cancer Genome Atlas (GS-TCGA)...
March 4, 2024: Journal of Computational Biology
https://read.qxmd.com/read/38416637/derna-enables-pareto-optimal-rna-design
#2
JOURNAL ARTICLE
Xinyu Gu, Yuanyuan Qi, Mohammed El-Kebir
The design of an RNA sequence <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mstyle><mml:mi>v</mml:mi></mml:mstyle></mml:math> that encodes an input target protein sequence <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mstyle><mml:mi>w</mml:mi></mml:mstyle></mml:math> is a crucial aspect of messenger RNA (mRNA) vaccine development. There are an exponential number of possible RNA sequences for a single target protein due to codon degeneracy...
February 27, 2024: Journal of Computational Biology
https://read.qxmd.com/read/38394313/unveiling-gene-regulatory-networks-that-characterize-difference-of-molecular-interplays-between-gastric-cancer-drug-sensitive-and-resistance-cell-lines
#3
JOURNAL ARTICLE
Heewon Park
Gastric cancer is a leading cause of cancer-related deaths globally and chemotherapy is widely accepted as the standard treatment for gastric cancer. However, drug resistance in cancer cells poses a significant obstacle to the success of chemotherapy, limiting its effectiveness in treating gastric cancer. Although many studies have been conducted to unravel the mechanisms of acquired drug resistance, the existing studies were based on abnormalities of a single gene, that is, differential gene expression (DGE) analysis...
February 23, 2024: Journal of Computational Biology
https://read.qxmd.com/read/38377572/prediction-of-microrna-disease-potential-association-based-on-sparse-learning-and-multilayer-random-walks
#4
JOURNAL ARTICLE
Hai-Bin Yao, Zhen-Jie Hou, Wen-Guang Zhang, Han Li, Yan Chen
More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations. In this study, we aim to propose a miRNA-disease association prediction model based on sparse learning and multilayer random walks (SLMRWMDA). The miRNA-disease association matrix is decomposed and reconstructed by the sparse learning method to obtain richer association information, and at the same time, the initial probability matrix for the random walk with restart algorithm is obtained...
February 19, 2024: Journal of Computational Biology
https://read.qxmd.com/read/38301204/micid-gui-the-graphical-user-interface-for-micid-a-fast-microorganism-classification-and-identification-workflow-with-accurate-statistics-and-high-recall
#5
JOURNAL ARTICLE
Aleksey Ogurtsov, Gelio Alves, Alex Rubio, Brendan Joyce, Björn Andersson, Roger Karlsson, Edward R B Moore, Yi-Kuo Yu
Although many user-friendly workflows exist for identifications of peptides and proteins in mass-spectrometry-based proteomics, there is a need of easy to use, fast, and accurate workflows for identifications of microorganisms, antimicrobial resistant proteins, and biomass estimation. Identification of microorganisms is a computationally demanding task that requires querying thousands of MS/MS spectra in a database containing thousands to tens of thousands of microorganisms. Existing software can't handle such a task in a time efficient manner, taking hours to process a single MS/MS experiment...
February 2, 2024: Journal of Computational Biology
https://read.qxmd.com/read/38285528/computing-the-bounds-of-the-number-of-reticulations-in-a-tree-child-network-that-displays-a-set-of-trees
#6
JOURNAL ARTICLE
Yufeng Wu, Louxin Zhang
Phylogenetic network is an evolutionary model that uses a rooted directed acyclic graph (instead of a tree) to model an evolutionary history of species in which reticulate events (e.g., hybrid speciation or horizontal gene transfer) occurred. Tree-child network is a kind of phylogenetic network with structural constraints. Existing approaches for tree-child network reconstruction can be slow for large data. In this study, we present several computational approaches for bounding from below the number of reticulations in a tree-child network that displays a given set of rooted binary phylogenetic trees...
January 29, 2024: Journal of Computational Biology
https://read.qxmd.com/read/38271573/the-k-robinson-foulds-dissimilarity-measures-for-comparison-of-labeled-trees
#7
JOURNAL ARTICLE
Elahe Khayatian, Gabriel Valiente, Louxin Zhang
Understanding the mutational history of tumor cells is a critical endeavor in unraveling the mechanisms that drive the onset and progression of cancer. Modeling tumor cell evolution with labeled trees motivates researchers to develop different measures to compare labeled trees. Although the Robinson-Foulds (RF) distance is widely used for comparing species trees, its applicability to labeled trees reveals certain limitations. This study introduces the k -RF dissimilarity measures, tailored to address the challenges of labeled tree comparison...
January 25, 2024: Journal of Computational Biology
https://read.qxmd.com/read/38271572/privacy-preserving-identification-of-cancer-subtype-specific-driver-genes-based-on-multigenomics-data-with-privatedriver
#8
JOURNAL ARTICLE
Junrong Song, Zhiming Song, Jinpeng Zhang, Yuanli Gong
Identifying cancer subtype-specific driver genes from a large number of irrelevant passengers is crucial for targeted therapy in cancer treatment. Recently, the rapid accumulation of large-scale cancer genomics data from multiple institutions has presented remarkable opportunities for identification of cancer subtype-specific driver genes. However, the insufficient subtype samples, privacy issues, and heterogenous of aberration events pose great challenges in precisely identifying cancer subtype-specific driver genes...
January 25, 2024: Journal of Computational Biology
https://read.qxmd.com/read/38227389/comparing-the-performance-of-three-computational-methods-for-estimating-the-effective-reproduction-number
#9
JOURNAL ARTICLE
Zihan Wang, Mengxia Xu, Zonglin Yang, Yu Jin, Yong Zhang
The effective reproduction number <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math> is one of the most important epidemiological parameters, providing suggestions for monitoring the development trend of diseases and also for adjusting the prevention and control policies...
January 16, 2024: Journal of Computational Biology
https://read.qxmd.com/read/38170180/repeated-decision-stumping-distils-simple-rules-from-single-cell-data
#10
JOURNAL ARTICLE
Ivan A Croydon-Veleslavov, Michael P H Stumpf
Single-cell data afford unprecedented insights into molecular processes. But the complexity and size of these data sets have proved challenging and given rise to a large armory of statistical and machine learning approaches. The majority of approaches focuses on either describing features of these data, or making predictions and classifying unlabeled samples. In this study, we introduce repeated decision stumping (ReDX) as a method to distill simple models from single-cell data. We develop decision trees of depth one-hence "stumps"-to identify in an inductive manner, gene products involved in driving cell fate transitions, and in applications to published data we are able to discover the key players involved in these processes in an unbiased manner without prior knowledge...
January 4, 2024: Journal of Computational Biology
https://read.qxmd.com/read/38531050/finding-highly-similar-regions-of-genomic-sequences-through-homomorphic-encryption
#11
JOURNAL ARTICLE
Magsarjav Bataa, Siwoo Song, Kunsoo Park, Miran Kim, Jung Hee Cheon, Sun Kim
Finding highly similar regions of genomic sequences is a basic computation of genomic analysis. Genomic analyses on a large amount of data are efficiently processed in cloud environments, but outsourcing them to a cloud raises concerns over the privacy and security issues. Homomorphic encryption (HE) is a powerful cryptographic primitive that preserves privacy of genomic data in various analyses processed in an untrusted cloud environment. We introduce an efficient algorithm for finding highly similar regions of two homomorphically encrypted sequences, and describe how to implement it using the bit-wise and word-wise HE schemes...
March 2024: Journal of Computational Biology
https://read.qxmd.com/read/38531049/toward-robust-self-training-paradigm-for-molecular-prediction-tasks
#12
JOURNAL ARTICLE
Hehuan Ma, Feng Jiang, Yu Rong, Yuzhi Guo, Junzhou Huang
Molecular prediction tasks normally demand a series of professional experiments to label the target molecule, which suffers from the limited labeled data problem. One of the semisupervised learning paradigms, known as self-training, utilizes both labeled and unlabeled data. Specifically, a teacher model is trained using labeled data and produces pseudo labels for unlabeled data. These labeled and pseudo-labeled data are then jointly used to train a student model. However, the pseudo labels generated from the teacher model are generally not sufficiently accurate...
March 2024: Journal of Computational Biology
https://read.qxmd.com/read/38206790/acknowledgment-of-reviewers-2023
#13
JOURNAL ARTICLE
(no author information available yet)
No abstract text is available yet for this article.
January 2024: Journal of Computational Biology
https://read.qxmd.com/read/38117611/a-fixed-parameter-tractable-algorithm-for-finding-agreement-cherry-reduced-subnetworks-in-level-1-orchard-networks
#14
JOURNAL ARTICLE
Kaari Landry, Olivier Tremblay-Savard, Manuel Lafond
Phylogenetic networks are increasingly being considered better suited to represent the complexity of the evolutionary relationships between species. One class of phylogenetic networks that have received a lot of attention recently is the class of orchard networks, which is composed of networks that can be reduced to a single leaf using cherry reductions. Cherry reductions, also called cherry-picking operations, remove either a leaf of a simple cherry (sibling leaves sharing a parent) or a reticulate edge of a reticulate cherry (two leaves whose parents are connected by a reticulate edge)...
December 20, 2023: Journal of Computational Biology
https://read.qxmd.com/read/38100126/deepppthermo-a-deep-learning-framework-for-predicting-protein-thermostability-combining-protein-level-and-amino-acid-level-features
#15
JOURNAL ARTICLE
Xiaoyang Xiang, Jiaxuan Gao, Yanrui Ding
Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years. However, how to make full use of multiview sequence information to predict thermostability effectively is still a challenge. In this study, we proposed a deep learning-based classifier named DeepPPThermo that fuses features of classical sequence features and deep learning representation features for classifying thermophilic and mesophilic proteins...
December 13, 2023: Journal of Computational Biology
https://read.qxmd.com/read/38054946/a-molecular-generative-model-of-covid-19-main-protease-inhibitors-using-long-short-term-memory-based-recurrent-neural-network
#16
JOURNAL ARTICLE
Arash Mehrzadi, Elham Rezaee, Sajjad Gharaghani, Zeynab Fakhar, Seyed Mohsen Mirhosseini
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a serious threat to public health and prompted researchers to find anti-coronavirus 2019 (COVID-19) compounds. In this study, the long short-term memory-based recurrent neural network was used to generate new inhibitors for the coronavirus. First, the model was trained to generate drug compounds in the form of valid simplified molecular-input line-entry system strings. Then, the structures of COVID-19 main protease inhibitors were applied to fine-tune the model...
December 6, 2023: Journal of Computational Biology
https://read.qxmd.com/read/38016151/igly-idn-identifying-lysine-glycation-sites-in-proteins-based-on-improved-densenet
#17
JOURNAL ARTICLE
Jianhua Jia, Genqiang Wu, Meifang Li
Lysine glycation is one of the most significant protein post-translational modifications, which changes the properties of the proteins and causes them to be dysfunctional. Accurately identifying glycation sites helps to understand the biological function and potential mechanism of glycation in disease treatments. Nonetheless, the experimental methods are ordinarily inefficient and costly, so effective computational methods need to be developed. In this study, we proposed the new model called iGly-IDN based on the improved densely connected convolutional networks (DenseNet)...
November 28, 2023: Journal of Computational Biology
https://read.qxmd.com/read/38010616/consensus-tree-under-the-ancestor-descendant-distance-is-np-hard
#18
JOURNAL ARTICLE
Yuanyuan Qi, Mohammed El-Kebir
Due to uncertainty in tumor phylogeny inference from sequencing data, many methods infer multiple, equally plausible phylogenies for the same cancer. To summarize the solution space <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mstyle/><mml:mi>T</mml:mi></mml:math> of tumor phylogenies, consensus tree methods seek a single best representative tree S under a specified pairwise tree distance function. One such distance function is the ancestor-descendant (AD) distance [Formula: see text] , which equals the size of the symmetric difference of the transitive closures of the edge sets [Formula: see text] and [Formula: see text] ...
November 28, 2023: Journal of Computational Biology
https://read.qxmd.com/read/38010500/quantitative-modeling-of-stemness-in-single-cell-rna-sequencing-data-a-nonlinear-one-class-support-vector-machine-method
#19
JOURNAL ARTICLE
Hao Jiang, Jingxin Liu, You Song, Jinzhi Lei
Intratumoral heterogeneity and the presence of cancer stem cells are challenging issues in cancer therapy. An appropriate quantification of the stemness of individual cells for assessing the potential for self-renewal and differentiation from the cell of origin can define a measurement for quantifying different cell states, which is important in understanding the dynamics of cancer evolution, and might further provide possible targeted therapies aimed at tumor stem cells. Nevertheless, it is usually difficult to quantify the stemness of a cell based on molecular information associated with the cell...
November 28, 2023: Journal of Computational Biology
https://read.qxmd.com/read/38010511/a-gene-selection-method-considering-measurement-errors
#20
JOURNAL ARTICLE
Hajoung Lee, Jaejik Kim
The analysis of gene expression data has made significant contributions to understanding disease mechanisms and developing new drugs and therapies. In such analysis, gene selection is often required for identifying informative and relevant genes and removing redundant and irrelevant ones. However, this is not an easy task as gene expression data have inherent challenges such as ultra-high dimensionality, biological noise, and measurement errors. This study focuses on the measurement errors in gene selection problems...
November 21, 2023: Journal of Computational Biology
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