bluuebunny/crossref_metadata_2025_split huggingface dataset:
Creative Hub Planning in East Jakarta with Contemporary Tropical Architecture Approach
Authors: Wicaksono Dimas , Pratiwi Isna , Wibowo Ardiyan Adhi , Fadlillah Farah 
Published: June 2023

Abstract
The achievements of the world’s greatest economic performance and great prospects due to the rapid development of the Indonesian economy can be realized through the creative economy sector. Creative economy is an economic concept whose products are produced from creativity, ideas, and human knowledge as its main assets. The government also contributed by issuing PERPRES RI No. 6 of 2015, which calls for encouraging, strengthening, and promoting the creative economy in an effort to increase national economic growth. Based on these problems, it is important that there is a creative hub as a center for creative economic activities in each community activity center that is able to lift and develop economic potential in each region, including East Jakarta. As one of the cities with the largest area in DKI Jakarta, East Jakarta has various potentials that should be developed. In planning and designing this architecture, the primary data collection methods were observation and interviews, as well as secondary data collection by document/literature studies. The data obtained will then be processed by descriptive analysis and data synthesis is carried out to develop problem solving in the design of various aspects of the approach. The creative hub plan that will be made in the form of a single building on an area of 17,874 m2 in East Jakarta with a contemporary tropical architectural design approach. Contemporary tropical architecture is oriented to the tropical climate, but has freedom of expression in its design to keep up with the times. This approach aims to achieve user convenience that supports the productivity of the creative community. One application is by using sustainable materials such as wood, natural stone, brick, concrete, bamboo, and so on. Thus, the planned creative hub will be able to provide solutions to all community needs while at the same time having a good impact on the surrounding environment.
The Bottle Caps (Crown Cork) Waste’s Treatment Innovation as a Solonese Bridal Accessories Product to Supports the Creative Industry
Authors: Setyowati Erna , Nafisatun  
Published: June 2023

Abstract
Bottle cap waste’s (crown/cork) pollutes the environment, a breakthrough solution is needed for recycling innovation to become a Solonese women’s bridal accessories product that has aesthetic value and adds its selling value. The objective of the study was to determine the innovation of bottle cap waste treatment as a product of the Solonese women’s bridal accessories. Data collection techniques using observation, documentation and online questionnaires. The design of this research use the R&D the descriptive analysis with product validity and feasibility tests. The results of the validity test of all accessories innovation products in the form of (cunduk mentul, cunduk comb, centung, earrings, brooches, rings, men’s necklaces, tretes bracelets, panetep, tretes necklaces) show an average of 98.46% with very valid criteria, and the preference test results for ten accessories products is obtained by an average of 96.61% with the criteria of very much likeness. The Solonese women’s bridal accessories product as an innovation for bottle cap’s treatment waste is very feasible and can be developed as a home industry to support the creative craft commodities.
Simultaneous imaging of luminescence and prompt x-rays during irradiation with spot-scanning proton beams at clinical dose level
Authors: Yamamoto Seiichi , Yamashita Tomohiro , Kobashi Yusuke , Yabe Takuya , Akagi Takashi , Yamaguchi Mitsutaka , Kawachi Naoki , Kamada Kei , Yoshikawa Akira , Kataoka Jun 
Published: June 2023

Abstract
Prompt x-ray imaging is a promising method for observing the beam shape from outside a subject. However, its distribution is different from dose distribution, and thus a comparison with the dose is required. Meanwhile, luminescence imaging of water is a possible method for imaging the dose distribution. Consequently, we performed simultaneous imaging of luminescence and prompt x-rays during irradiation by proton beams to compare the distributions between these two different imaging methods. Optical imaging of water was conducted with spot-scanning proton beams at clinical dose level during irradiation to a fluorescein (FS) water phantom set in a black box. Prompt x-ray imaging was also conducted simultaneously from outside the black box using a developed x-ray camera during proton beam irradiation to the phantom. We measured images of the luminescence of FS water and prompt x-rays for various types of proton beams, including pencil beams, spread-out Bragg peak (SOBP) beams, and clinically used therapy beams. After the imaging, ranges were estimated from FS water and prompt x-rays and compared with those calculated with a treatment planning system (TPS). We could measure the prompt x-ray and FS water images simultaneously for all types of proton beams. The ranges estimated from the FS water and those calculated with the TPS closely matched, within a difference of several mm. Similar range difference was found between the results estimated from prompt x-ray images and those calculated with the TPS. We confirmed that the simultaneous imaging of luminescence and prompt x-rays were possible during irradiation with spot-scanning proton beams at a clinical dose level. This method can be applied to range estimation as well as comparison with the dose for prompt x-ray imaging or other imaging methods used in therapy with various types of proton beams at a clinical dose level.
HMMPolish: a coding region polishing tool for TGS-sequenced RNA viruses
Authors: Yu Runzhou , Abdullah Syed Muhammad Umer , Sun Yanni 
Published: June 2023

Abstract
Access to accurate viral genomes is important to downstream data analysis. Third-generation sequencing (TGS) has recently become a popular platform for virus sequencing because of its long read length. However, its per-base error rate, which is higher than next-generation sequencing, can lead to genomes with errors. Polishing tools are thus needed to correct errors either before or after sequence assembly. Despite promising results of available polishing tools, there is still room to improve the error correction performance to perform more accurate genome assembly. The errors, particularly those in coding regions, can hamper analysis such as linage identification and variant monitoring. In this work, we developed a novel pipeline, HMMPolish, for correcting (polishing) errors in protein-coding regions of known RNA viruses. This tool can be applied to either raw TGS reads or the assembled sequences of the target virus. By utilizing profile Hidden Markov Models of protein families/domains in known viruses, HMMPolish can correct errors that are ignored by available polishers. We extensively validated HMMPolish on 34 datasets that covered four clinically important viruses, including HIV-1, influenza-A, norovirus, and severe acute respiratory syndrome coronavirus 2. These datasets contain reads with different properties, such as sequencing depth and platforms (PacBio or Nanopore). The benchmark results against popular/representative polishers show that HMMPolish competes favorably on error correction in coding regions of known RNA viruses.
COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data
Authors: Ditz Jonas C , Reuter Bernhard , Pfeifer Nico 
Published: June 2023

Abstract

Motivation
The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample size can be used to improve the performance of relevant prediction tasks in healthcare, models that are optimized for large datasets usually operate as black boxes. In high-stakes scenarios, like healthcare, using a black-box model poses safety and security issues. Without an explanation about molecular factors and phenotypes that affected the prediction, healthcare providers are left with no choice but to blindly trust the models. We propose a new type of artificial neural network, named Convolutional Omics Kernel Network (COmic). By combining convolutional kernel networks with pathway-induced kernels, our method enables robust and interpretable end-to-end learning on omics datasets ranging in size from a few hundred to several hundreds of thousands of samples. Furthermore, COmic can be easily adapted to utilize multiomics data.

Results
We evaluated the performance capabilities of COmic on six different breast cancer cohorts. Additionally, we trained COmic models on multiomics data using the METABRIC cohort. Our models performed either better or similar to competitors on both tasks. We show how the use of pathway-induced Laplacian kernels opens the black-box nature of neural networks and results in intrinsically interpretable models that eliminate the need for post hoc explanation models.

Availability and implementation
Datasets, labels, and pathway-induced graph Laplacians used for the single-omics tasks can be downloaded at https://ibm.ent.box.com/s/ac2ilhyn7xjj27r0xiwtom4crccuobst/folder/48027287036. While datasets and graph Laplacians for the METABRIC cohort can be downloaded from the above mentioned repository, the labels have to be downloaded from cBioPortal at https://www.cbioportal.org/study/clinicalData?id=brca\_metabric. COmic source code as well as all scripts necessary to reproduce the experiments and analysis are publicly available at https://github.com/jditz/comics.
PlasmoFAB: a benchmark to foster machine learning for Plasmodium falciparum protein antigen candidate prediction
Authors: Ditz Jonas C , Wistuba-Hamprecht Jacqueline , Maier Timo , Fendel Rolf , Pfeifer Nico , Reuter Bernhard 
Published: June 2023

Abstract

Motivation
Machine learning methods can be used to support scientific discovery in healthcare-related research fields. However, these methods can only be reliably used if they can be trained on high-quality and curated datasets. Currently, no such dataset for the exploration of Plasmodium falciparum protein antigen candidates exists. The parasite P.falciparum causes the infectious disease malaria. Thus, identifying potential antigens is of utmost importance for the development of antimalarial drugs and vaccines. Since exploring antigen candidates experimentally is an expensive and time-consuming process, applying machine learning methods to support this process has the potential to accelerate the development of drugs and vaccines, which are needed for fighting and controlling malaria.

Results
We developed PlasmoFAB, a curated benchmark that can be used to train machine learning methods for the exploration of P.falciparum protein antigen candidates. We combined an extensive literature search with domain expertise to create high-quality labels for P.falciparum specific proteins that distinguish between antigen candidates and intracellular proteins. Additionally, we used our benchmark to compare different well-known prediction models and available protein localization prediction services on the task of identifying protein antigen candidates. We show that available general-purpose services are unable to provide sufficient performance on identifying protein antigen candidates and are outperformed by our models that were trained on this tailored data.

Availability and implementation
PlasmoFAB is publicly available on Zenodo with DOI 10.5281/zenodo.7433087. Furthermore, all scripts that were used in the creation of PlasmoFAB and the training and evaluation of machine learning models are open source and publicly available on GitHub here: https://github.com/msmdev/PlasmoFAB.
ArkDTA: attention regularization guided by non-covalent interactions for explainable drug–target binding affinity prediction
Authors: Gim Mogan , Choe Junseok , Baek Seungheun , Park Jueon , Lee Chaeeun , Ju Minjae , Lee Sumin , Kang Jaewoo 
Published: June 2023

Abstract

Motivation
Protein–ligand binding affinity prediction is a central task in drug design and development. Cross-modal attention mechanism has recently become a core component of many deep learning models due to its potential to improve model explainability. Non-covalent interactions (NCIs), one of the most critical domain knowledge in binding affinity prediction task, should be incorporated into protein–ligand attention mechanism for more explainable deep drug–target interaction models. We propose ArkDTA, a novel deep neural architecture for explainable binding affinity prediction guided by NCIs.

Results
Experimental results show that ArkDTA achieves predictive performance comparable to current state-of-the-art models while significantly improving model explainability. Qualitative investigation into our novel attention mechanism reveals that ArkDTA can identify potential regions for NCIs between candidate drug compounds and target proteins, as well as guiding internal operations of the model in a more interpretable and domain-aware manner.

Availability
ArkDTA is available at https://github.com/dmis-lab/ArkDTA

Contact
kangj@korea.ac.kr
Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function
Authors: Boadu Frimpong , Cao Hongyuan , Cheng Jianlin 
Published: June 2023

Abstract

Motivation
Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently.

Results
We developed TransFun—a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with 3D structures of proteins predicted by AlphaFold2 through equivariant graph neural networks. Benchmarked on the CAFA3 test dataset and a new test dataset, TransFun outperforms several state-of-the-art methods, indicating that the language model and 3D-equivariant graph neural networks are effective methods to leverage protein sequences and structures to improve protein function prediction. Combining TransFun predictions and sequence similarity-based predictions can further increase prediction accuracy.

Availability and implementation
The source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.
SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing
Authors: Pan Shaojun , Zhao Xing-Ming , Coelho Luis Pedro 
Published: June 2023

Abstract

Motivation
Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental samples have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process.

Results
We propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3–21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing samples. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1–26.3% more high-quality genomes than the second best binner for long-read data.

Availability and implementation
SemiBin2 is available as open source software at https://github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at https://github.com/BigDataBiology/SemiBin2_benchmark.
Scalable sequence database search using partitioned aggregated Bloom comb trees
Authors: Marchet Camille , Limasset Antoine 
Published: June 2023

Abstract

Motivation
The Sequence Read Archive public database has reached 45 petabytes of raw sequences and doubles its nucleotide content every 2 years. Although BLAST-like methods can routinely search for a sequence in a small collection of genomes, making searchable immense public resources accessible is beyond the reach of alignment-based strategies. In recent years, abundant literature tackled the task of finding a sequence in extensive sequence collections using k-mer-based strategies. At present, the most scalable methods are approximate membership query data structures that combine the ability to query small signatures or variants while being scalable to collections up to 10 000 eukaryotic samples. Results. Here, we present PAC, a novel approximate membership query data structure for querying collections of sequence datasets. PAC index construction works in a streaming fashion without any disk footprint besides the index itself. It shows a 3–6 fold improvement in construction time compared to other compressed methods for comparable index size. A PAC query can need single random access and be performed in constant time in favorable instances. Using limited computation resources, we built PAC for very large collections. They include 32 000 human RNA-seq samples in 5 days, the entire GenBank bacterial genome collection in a single day for an index size of 3.5 TB. The latter is, to our knowledge, the largest sequence collection ever indexed using an approximate membership query structure. We also showed that PAC’s ability to query 500 000 transcript sequences in less than an hour.

Availability and implementation
PAC’s open-source software is available at https://github.com/Malfoy/PAC.
PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer
Authors: Shang Jiayu , Peng Cheng , Tang Xubo , Sun Yanni 
Published: June 2023

Abstract

Motivation
As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages’ functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins, such as major tail, baseplate, etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein (PVP) classification.

Results
In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos game representation, we can leverage Vision Transformer to learn both local and global features from sequence “images”. Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results showed the benefit of using classified proteins over all proteins.

Availability and implementation
The web server of PhaVIP is available via: https://phage.ee.cityu.edu.hk/phavip. The source code of PhaVIP is available via: https://github.com/KennthShang/PhaVIP.
SVJedi-graph: improving the genotyping of close and overlapping structural variants with long reads using a variation graph
Authors: Romain Sandra , Lemaitre Claire 
Published: June 2023

Abstract

Motivation
Structural variation (SV) is a class of genetic diversity whose importance is increasingly revealed by genome resequencing, especially with long-read technologies. One crucial problem when analyzing and comparing SVs in several individuals is their accurate genotyping, that is determining whether a described SV is present or absent in one sequenced individual, and if present, in how many copies. There are only a few methods dedicated to SV genotyping with long-read data, and all either suffer of a bias toward the reference allele by not representing equally all alleles, or have difficulties genotyping close or overlapping SVs due to a linear representation of the alleles.

Results
We present SVJedi-graph, a novel method for SV genotyping that relies on a variation graph to represent in a single data structure all alleles of a set of SVs. The long reads are mapped on the variation graph and the resulting alignments that cover allele-specific edges in the graph are used to estimate the most likely genotype for each SV. Running SVJedi-graph on simulated sets of close and overlapping deletions showed that this graph model prevents the bias toward the reference alleles and allows maintaining high genotyping accuracy whatever the SV proximity, contrary to other state of the art genotypers. On the human gold standard HG002 dataset, SVJedi-graph obtained the best performances, genotyping 99.5% of the high confidence SV callset with an accuracy of 95% in less than 30 min.

Availability and implementation
SVJedi-graph is distributed under an AGPL license and available on GitHub at https://github.com/SandraLouise/SVJedi-graph and as a BioConda package.
Finding phylogeny-aware and biologically meaningful averages of metagenomic samples: L2UniFrac
Authors: Wei Wei , Millward Andrew , Koslicki David 
Published: June 2023

Abstract

Motivation
Metagenomic samples have high spatiotemporal variability. Hence, it is useful to summarize and characterize the microbial makeup of a given environment in a way that is biologically reasonable and interpretable. The UniFrac metric has been a robust and widely used metric for measuring the variability between metagenomic samples. We propose that the characterization of metagenomic environments can be improved by finding the average, a.k.a. the barycenter, among the samples with respect to the UniFrac distance. However, it is possible that such a UniFrac-average includes negative entries, making it no longer a valid representation of a metagenomic community.

Results
To overcome this intrinsic issue, we propose a special version of the UniFrac metric, termed L2UniFrac, which inherits the phylogenetic nature of the traditional UniFrac and with respect to which one can easily compute the average, producing biologically meaningful environment-specific “representative samples.” We demonstrate the usefulness of such representative samples as well as the extended usage of L2UniFrac in efficient clustering of metagenomic samples, and provide mathematical characterizations and proofs to the desired properties of L2UniFrac.

Availability and implementation
A prototype implementation is provided at https://github.com/KoslickiLab/L2-UniFrac.git. All figures, data, and analysis can be reproduced at https://github.com/KoslickiLab/L2-UniFrac-Paper