As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness th...
详细信息
As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco -derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy.
Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how f...
详细信息
Pantanal is the largest tropical wetland in the world and has been extremely affected by these wildfires recently, such as in 2020 and 2024. The largest rivers that supply the Pantanal originate in the Brazilian highl...
详细信息
Pantanal is the largest tropical wetland in the world and has been extremely affected by these wildfires recently, such as in 2020 and 2024. The largest rivers that supply the Pantanal originate in the Brazilian highlands (“plateau”), so the availability of surface water in the biome (“plain”) is strongly associated with the hydrological dynamics and land use and cover of surrounding biomes such as the Amazon, Cerrado, and Atlantic Forest. Although the hydrological connections between these biomes and the local factors contributing to wildfires have been well explored, there appears to be a lack of comprehensive studies at a macro-scale that demonstrate the associations between historical above-average fires in the Pantanal and the climatic and biospheric conditions of its hydrologically interconnected biomes. Therefore, this study aimed to analyze a 32-year historical series (1989–2020) of burned areas in the Pantanal and identify the drivers of above-average wildfires. We explored local environmental variables as well as those of surrounding biomes. We found that 13 out of 32 analyzed years stand out for a total burned area above normal, and only 1999 showed a larger burned area than 2020. The results of this study demonstrate that water dynamics from soil to atmosphere, such as decreased soil moisture, vegetation moisture and evapotranspiration, and increased drought, including during previous years, in the Pantanal and the surrounding forested biomes (Amazon and Atlantic Forest) are associated with the occurrence of above-average wildfires in the Pantanal wetland. In addition, the 32-year trend analysis revealed that widespread drought has increased in the Pantanal and all analyzed interconnected biomes, indicating that the world’s largest tropical wetland will likely see more above-average wildfires. Our results have implications for macro-scale adaptive management, integrated watershed management, fire risk early detection, and the preparedness of teams respon
Numerous research on stunting supplementation interventions in Indonesia have been published. The information can be extracted through data mining, especially from academic research databases. In this paper, we presen...
详细信息
Numerous research on stunting supplementation interventions in Indonesia have been published. The information can be extracted through data mining, especially from academic research databases. In this paper, we presented a text mining-based literature review strategy to create a pipeline that researchers can use to accelerate the development of stunting supplementation intervention research in Indonesia. Utilizing various NLP (Natural Language Processing) techniques, data were crawled, processed, and visualized using Python. The crawling dataset used a module from the Pubmed API (Application programming Interface) to collect literature papers. The NLTK (Natural Language Toolkit) module and itertools were used to process text data. The n-grams model was applied to process tokens into bigrams and trigrams. Text information was visualized using Matplotlib and Word cloud packages. There is an increasing number of publication in stunting supplementation intervention according to our result, which was observed from 2015 to 2021. West Java was the province where most of the stunting research has been conducted, as determined by research abstracts. Top occurrences obtained from the bigram and trigrams models calculation produced different terms. The word pairings that occurred the most frequently in the bigram and trigram model analyses were "child-aged" and "iron-folic-acid," respectively. The findings of this study are expected to help researchers to obtain the latest research topics related to stunting supplementation interventions in Indonesia.
With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous st...
详细信息
Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the t...
详细信息
An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of ...
详细信息
Understanding drug-drug interactions (DDIs) can benefit us by enhancing drug effectiveness and preventing side effects that arise from consuming multiple drugs administered together. However, as the number of novel dr...
详细信息
ISBN:
(数字)9798331519643
ISBN:
(纸本)9798331519650
Understanding drug-drug interactions (DDIs) can benefit us by enhancing drug effectiveness and preventing side effects that arise from consuming multiple drugs administered together. However, as the number of novel drugs increases, predicting potential DDIs becomes more challenging. Previous studies have used the molecular language model ChemBERTa to predict potential DDIs, but the predictions were only tested on unseen drugs and not dissimilar drugs, while also performing poorly. In this study, we demonstrate that a simple molecular preprocessing method using BRICS decomposition can enhance ChemBERTa robustness in predicting potential DDIs for unseen-dissimilar drugs. Specifically, we employ fragmentation with a minimum fragment size of 5 (BRICS-5), which balances the number of fragments and decomposition speed. Using this method, we were able to improve ChemBERTa robustness, reflected by an approximate increase of 1.5% in recall and 2.2% in the f1-score compared to non-decomposed molecules. Our findings suggest that this preprocessing method could be useful in the development of future AI-based approaches for solving challenging molecular tasks across different settings and various model architectures.
Many smoking-related diseases are difficult to treat and often fatal. Rather than treating diseased smokers, preventing the diseased is more achievable, though, many of them deny to being smokers, leading to another p...
详细信息
ISBN:
(数字)9798331519643
ISBN:
(纸本)9798331519650
Many smoking-related diseases are difficult to treat and often fatal. Rather than treating diseased smokers, preventing the diseased is more achievable, though, many of them deny to being smokers, leading to another problem. Thus, this study aims to detect important aspects that can detect that the person is a smoker or not through their bio-signals through using SHAP, along with a comprehensive analysis of the used methods, gradient-boosting algorithms XGBoost, LightGBM, and CatBoost, known for their efficiency in handling complex datasets and non-linear relationships. The study then found that triglyceride, Gtp, hemoglobins significantly affect the body's responses to smoking, based on the CatBoosts’ results, having an AUC score of up to 0.8612 and an accuracy score of up to 0.7776 with the selected features.
Enzymes are biocatalysts with vital roles in biological functions and many industrial applications. Diverse enzymes are classified using Enzyme Commission (EC) nomenclature, making differentiation challenging. On the ...
详细信息
ISBN:
(数字)9798331520311
ISBN:
(纸本)9798331520328
Enzymes are biocatalysts with vital roles in biological functions and many industrial applications. Diverse enzymes are classified using Enzyme Commission (EC) nomenclature, making differentiation challenging. On the other hand, another biological information, gene ontology (GO), can describe the biological aspects of enzymes, covering related biological processes (BP), molecular functions (MF), and their locations within cells (CC). This study proposes a novel EC class and subclass classification of enzymes within the ontology subclass based on their GO semantics using a Bidirectional Encoder Representation of Transformer (BERT). The BERT model is first fine-tuned using the preprocessed GO term name and definition, with the enzymes in each ontology class (BP, MF, or CC) are also divided based on how the GO assigned, either through manual annotation (NONIEA) or electronically inferred (IEA). BERT successfully obtained 0.93, 0.60, 0.99, 0.90, 0.40, and 0.35 F1 scores during fine-tuning for BP IEA, BP NONIEA, MF IEA, MF NONIEA, CC IEA, and CC NONIEA, respectively. On the test set, the fine-tuned BERT significantly outperformed GOntoSim, a framework to calculate semantic similarity based on classical information theory, in EC class classification across all metrics with less inference time in all ontology subclass. Expanded further to the EC subclass, BERT can classify the enzyme on the EC subclass level in BP IEA and MF IEA ontology subclass. However, longer epochs are needed in fine-tuning. This result shows that the names and definitions of GO terms are distinguishable features in classifying enzymes as an alternative to the information content approach.
暂无评论