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AAontology: An ontology of amino acid scales for...

PaperPlayer biorxiv bioinformatics ·

2023/08/05

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551768v1?rss=1 Authors: Breimann, S., Kamp, F., Steiner, H., Frishman, D. Abstract: Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems

A dose-response based model for statistical analysis...

PaperPlayer biorxiv bioinformatics ·

2023/08/05

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551759v1?rss=1 Authors: Choudhery, S., DeJesus, M., Srinivasan, A., Rock, J., Schnappinger, D., Ioerger, T. Abstract: An important application of CRISPR interference (CRISPRi) technology is for identifying chemical-genetic interactions (CGIs). Discovery of genes that interact with exposure to antibiotics can yield insights to drug targets and mechanisms of action or resistance. The premise is to look for CRISPRi mutants whose

PxBLAT: An Ergonomic and Efficient Python Binding...

PaperPlayer biorxiv bioinformatics ·

2023/08/05

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551686v1?rss=1 Authors: Li, Y., Yang, R. Abstract: Summary: We introduce PxBLAT, a Python library designed to enhance usability and efficiency in interacting with the BLAST-like alignment tool (BLAT). PxBLAT provides an intuitive application programming interface (API) design, allowing the incorporation of its functionality directly into Python-based bioinformatics workflows. Besides, it integrates seamlessly with Biopython

CellGO: A novel deep learning-based framework and...

PaperPlayer biorxiv bioinformatics ·

2023/08/05

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551654v1?rss=1 Authors: Li, P., Wei, J., Zhu, Y. Abstract: Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to account for complex interactions across genes and pathways under specific tissues and cell types. We introduce CellGO, a tool for cell type-specific gene functional analysis. CellGO employs a deep learning model to

Chrombus-XMBD: A Graph Generative Model Predicting...

PaperPlayer biorxiv bioinformatics ·

2023/08/05

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551072v1?rss=1 Authors: Zeng, Y., You, Z., Guo, J., Zhao, J., Zhou, Y., Huang, J., Lyu, X., Chen, L., Li, Q. Abstract: The landscape of 3D-genome is crucial for transcription regulation. But capturing the dynamics of chromatin conformation is costly and technically challenging. Here we described Chrombus-XMBD, a graph generative model capable of predicting chromatin interactions ab inito based on available chromatin features

Bioinformatics and next generation data analysis...

PaperPlayer biorxiv bioinformatics ·

2023/08/05

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551653v1?rss=1 Authors: Vastrad, B. M., Vastrad, C. M. Abstract: Sepsis is the leading systemic inflammatory response syndrome in worldwide, yet relatively little is known about the genes and signaling pathways involved in sepsis progression. The current investigation aimed to elucidate potential key candidate genes and pathways in sepsis and its associated complications. Next generation sequencing (NGS) dataset (GSE185263)

Compound models and Pearson residuals for...

PaperPlayer biorxiv bioinformatics ·

2023/08/05

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551637v1?rss=1 Authors: Lause, J., Ziegenhain, C., Hartmanis, L., Berens, P., Kobak, D. Abstract: Before downstream analysis can reveal biological signals in single-cell RNA sequencing data, normalization and variance stabilization are required to remove technical noise. Recently, Pearson residuals based on negative binomial models have been suggested as an efficient normalization approach. These methods were developed for

Multi-representation DeepInsight: an improvement on...

PaperPlayer biorxiv bioinformatics ·

2023/08/05

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551620v1?rss=1 Authors: Sharma, A., Lopez, Y., JIA, S., Lysenko, A., Boroevich, K., Tsunoda, T. Abstract: Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods have been employed for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies

Can a Sparse 29 x 29 Pixel Chaos Game Representation...

PaperPlayer biorxiv bioinformatics ·

2023/08/04

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.04.410498v1?rss=1 Authors: Dick, K., Green, J. R. Abstract: No. While our experiments ultimately failed, this work was motivated by the seemingly reasonable hypothesis that encoding protein sequences as a fractal-based image in combination with a binary mask identifying those pixels representative of the protein binding interface could effectively be used to fine-tune a semantic segmentation model. We were wrong. Despite the

Single-linkage molecular clustering of viral pathogens

PaperPlayer biorxiv bioinformatics ·

2023/08/04

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551813v1?rss=1 Authors: Soto Miranda, M., Narvaez Romo, R., Moshiri, N. Abstract: Introduction: Public health faces the ongoing mission of safeguarding the population's health against various infectious diseases caused by a great number of pathogens. Epidemiology is an essential discipline in this field. With the rise of more advanced technologies, new tools are emerging to enhance the capability to intervene and control

memerna: Sparse RNA Folding Including Coaxial Stacking

PaperPlayer biorxiv bioinformatics ·

2023/08/04

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.04.551958v1?rss=1 Authors: Courtney, E., Datta, A., Mathews, D. H., Ward, M. Abstract: Determining RNA secondary structure is a core problem in computational biology. Fast algorithms for predicting secondary structure are fundamental to this task. We describe a modified formulation of the Zuker-Stiegler algorithm with coaxial stacking, a stabilizing interaction in which the ends of multi-loops are stacked. In particular, optimal

Interface-guided phenotyping of coding variants in...

PaperPlayer biorxiv bioinformatics ·

2023/08/04

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551876v1?rss=1 Authors: Ozturk, K., Panwala, R., Sheen, J., Ford, K., Payne, N., Zhang, D.-E., Hutter, S., Haferlach, T., Ideker, T., Mali, P., Carter, H. Abstract: Understanding the consequences of single amino acid substitutions in cancer driver genes remains an unmet need. Perturb-seq provides a tool to investigate the effects of individual mutations on cellular programs. Here we deploy SEUSS, a Perturb-seq like approach,

Comparing full variation profile analysis with the...

PaperPlayer biorxiv bioinformatics ·

2023/08/04

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551784v1?rss=1 Authors: Fiam, R. N., Csabai, I., Solymosi, N. Abstract: This study proposes a novel approach to studying SARS-CoV-2 virus mutations through sequencing data comparison. Traditional consensus-based methods, which focus on the most common nucleotide at each position, might overlook or obscure the presence of low-frequency variants. Our method, in contrast, retains all sequenced nucleotides at each position,

Neo-epitope identification by weakly-supervised...

PaperPlayer biorxiv bioinformatics ·

2023/08/03

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.550128v1?rss=1 Authors: Liu, Q., Gao, Y., Gao, Y., Li, W., Wu, S. Abstract: The identification of T cell neo-epitopes is fundamental and computational challenging in tumor immunotherapy study. As the binding of pMHC - T cell receptor (TCR) is the essential condition for neo-epitopes to trigger the cytotoxic T cell reactivity, several computational studies have been proposed to predict neo-epitopes from the perspective of

A versatile framework for drug-target interaction...

PaperPlayer biorxiv bioinformatics ·

2023/08/03

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551396v1?rss=1 Authors: Liu, S., Yu, J., Ni, N., Wang, Z., Chen, M., Li, Y., Xu, C., Bai, Q., Ding, Y., Wang, C., Zhang, J., Yao, X., Liu, H. Abstract: Predicting drug-target interaction (DTI) is a critical and rate-limiting step in drug discovery. Traditional wet-lab experiments are reliable but expensive and time-consuming. Recently, deep learning has revealed itself as a new and promising tool for accelerating the DTI

An AI-assisted Investigation of Tumor-Associated...

PaperPlayer biorxiv bioinformatics ·

2023/08/03

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551559v1?rss=1 Authors: Dadlani, E., Dash, T., Sahoo, D. Abstract: Tumor-associated Macrophages (or TAMs) are amongst the most common cells that play a significant role in the initiation and progression of colorectal cancer (CRC). Recently, Ghosh et al. proposed distinguishing signatures for identifying macrophage polarization states, namely, immuno-reactive and immuno-tolerant, using the concept of Boolean implications and

FiTMuSiC: Leveraging structural and (co)evolutionary...

PaperPlayer biorxiv bioinformatics ·

2023/08/03

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551497v1?rss=1 Authors: Tsishyn, M., Cia, G., Hermans, P., Kwasigroch, J., Rooman, M., Pucci, F. Abstract: Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC,

From Proteins to Ligands: Decoding Deep Learning...

PaperPlayer biorxiv bioinformatics ·

2023/08/03

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551483v1?rss=1 Authors: Gorantla, R., Kubincova, A., Weisse, A. Y., Mey, A. S. J. S. Abstract: Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalisability. To improve generalizability we need to understand what these

Continuous lifelong learning for modeling of gene...

PaperPlayer biorxiv bioinformatics ·

2023/08/03

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551575v1?rss=1 Authors: Yuan, Q., Duren, Z. Abstract: Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell

SAMPLER: Empirical distribution representations for...

PaperPlayer biorxiv bioinformatics ·

2023/08/03

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551468v1?rss=1 Authors: Mukashyaka, P., Sheridan, T. B., Foroughi pour, A., Chuang, J. H. Abstract: Deep learning has revolutionized digital pathology, allowing for automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. In such analyses, WSIs are typically broken into smaller images called tiles, and a neural network backbone encodes each tile in a feature space. Many recent

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