scDrug+: predicting drug-responses using single-cell transcriptomics and molecular structure.

Predicting drug responses based on individual transcriptomic profiles holds promise for refining prognosis and advancing precision medicine. Although many studies have endeavored to predict the responses of known drugs to novel transcriptomic profiles, research into predicting responses for newly discovered drugs remains sparse. In this study, we introduce scDrug+, a comprehensive pipeline that seamlessly integrates single-cell analysis with drug-response prediction.

Macrophages as determinants and regulators of systemic sclerosis-related interstitial lung disease.

Background: Interstitial lung disease (ILD) is the primary cause of mortality in systemic sclerosis (SSc), an autoimmune disease characterized by tissue fibrosis. SSc-related ILD (SSc-ILD) occurs more frequently in females aged 30-55 years, whereas idiopathic pulmonary fibrosis (IPF) is more prevalent in males aged 60-75 years. SSc-ILD occurs earlier than IPF and progresses rapidly.

Exploiting Machine Learning Technologies to Study the Compound Effects of Serum Creatinine and Electrolytes on the Risk of Acute Kidney Injury in Intensive Care Units.

Assessing the risk of acute kidney injury (AKI) has been a challenging issue for clinicians in intensive care units (ICUs). In recent years, a number of studies have been conducted to investigate the associations between several serum electrolytes and AKI. Nevertheless, the compound effects of serum creatinine, blood urea nitrogen (BUN), and clinically relevant serum electrolytes have yet to be comprehensively investigated.

BIC: a database for the transcriptional landscape of bacteria in cancer.

Microbial communities are massively resident in the human body, yet dysbiosis has been reported to correlate with many diseases, including various cancers. Most studies focus on the gut microbiome, while the bacteria that participate in tumor microenvironments on site remain unclear. Previous studies have acquired the bacteria expression profiles from RNA-seq, whole genome sequencing, and whole exon sequencing in The Cancer Genome Atlas (TCGA).

A Single-Cell Network-Based Drug Repositioning Strategy for Post-COVID-19 Pulmonary Fibrosis.

Post-COVID-19 pulmonary fibrosis (PCPF) is a long-term complication that appears in some COVID-19 survivors. However, there are currently limited options for treating PCPF patients. To address this problem, we investigated COVID-19 patients' transcriptome at single-cell resolution and combined biological network analyses to repurpose the drugs treating PCPF.

Stratification of lncRNA modulation networks in breast cancer.

Background: Recently, non-coding RNAs are of growing interest, and more scientists attach importance to research on their functions. Long non-coding RNAs (lncRNAs) are defined as non-protein coding transcripts longer than 200 nucleotides. We already knew that lncRNAs are related to cancers and will be dysregulated in them.

Machine learning for emerging infectious disease field responses.

Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients.

Comparing machine learning with case-control models to identify confirmed dengue cases.

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests.

Risk assessment of dementia after hysterectomy: Analysis of 14-year data from the National Health Insurance Research Database in Taiwan.

Background: Anesthesia and surgery may increase the risk of dementia in the elderly, but the higher prevalence of dementia in women and other evidence suggest that dementia risk increases in younger women undergoing hysterectomy. In this study, we assessed the risk of dementia after hysterectomy.

Methods: Hysterectomies registered in the National Health Insurance Research Database from 2000 to 2013 were evaluated using a retrospective generational research method.

GenEpi: gene-based epistasis discovery using machine learning.

Background: Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD).

Author Correction: Effect of de novo transcriptome assembly on transcript quantification.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Effect of de novo transcriptome assembly on transcript quantification.

Correct quantification of transcript expression is essential to understand the functional elements in different physiological conditions. For the organisms without the reference transcriptome, de novo transcriptome assembly must be carried out prior to quantification. However, a large number of erroneous contigs produced by the assemblers might result in unreliable estimation.

Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery.

Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies.

Therapeutic Targeting of Non-oncogene Dependencies in High-risk Neuroblastoma.

Purpose: Neuroblastoma is a pediatric malignancy of the sympathetic nervous system with diverse clinical behaviors. Genomic amplification of oncogene has been shown to drive neuroblastoma pathogenesis and correlate with aggressive disease, but the survival rates for those high-risk tumors carrying no amplification remain equally dismal. The paucity of mutations and molecular heterogeneity has hindered the development of targeted therapies for most advanced neuroblastomas.

HAHap: a read-based haplotyping method using hierarchical assembly.

Background: The need for read-based phasing arises with advances in sequencing technologies. The minimum error correction (MEC) approach is the primary trend to resolve haplotypes by reducing conflicts in a single nucleotide polymorphism-fragment matrix. However, it is frequently observed that the solution with the optimal MEC might not be the real haplotypes, due to the fact that MEC methods consider all positions together and sometimes the conflicts in noisy regions might mislead the selection of corrections.

Biomarker identification of hepatocellular carcinoma using a methodical literature mining strategy.

Hepatocellular carcinoma (HCC), one of the most common causes of cancer-related deaths, carries a 5-year survival rate of 18%, underscoring the need for robust biomarkers. In spite of the increased availability of HCC related literatures, many of the promising biomarkers reported have not been validated for clinical use. To narrow down the wide range of possible biomarkers for further clinical validation, bioinformaticians need to sort them out using information provided in published works.

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Emergence of differentially regulated pathways associated with the development of regional specificity in chicken skin.

Background: Regional specificity allows different skin regions to exhibit different characteristics, enabling complementary functions to make effective use of the integumentary surface. Chickens exhibit a high degree of regional specificity in the skin and can serve as a good model for when and how these regional differences begin to emerge.

Results: We used developing feather and scale regions in embryonic chickens as a model to gauge the differences in their molecular pathways.

MicroRNA-mediated networks underlie immune response regulation in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is a common endocrine malignancy with low death rate but increased incidence and recurrence in recent years. MicroRNAs (miRNAs) are small non-coding RNAs with diverse regulatory capacities in eukaryotes and have been frequently implied in human cancer. Despite current progress, however, a panoramic overview concerning miRNA regulatory networks in PTC is still lacking.

Application of density estimation algorithms in analyzing co-morbidities of migraine.

In this study, we will propose a density estimation based data analysis procedure to investigate the co-morbid associations between migraine and the suspected diseases. The primary objective of this study has aimed to develop a novel analysis procedure that can discover insightful knowledge from large medical databases. The entire analysis procedure consists of two stages.

Risk of dementia after anaesthesia and surgery.

Background: The potential relationship between anaesthesia, surgery and onset of dementia remains elusive.

Aims: To determine whether the risk of dementia increases after surgery with anaesthesia, and to evaluate possible associations among age, mode of anaesthesia, type of surgery and risk of dementia.

Method: The study cohort comprised patients aged 50 years and older who were anaesthetised for the first time since 1995 between 1 January 2004 and 31 December 2007, and a control group of randomly selected patients matched for age and gender.

MicroRNA-regulated protein-protein interaction networks and their functions in breast cancer.

MicroRNAs, which are small endogenous RNA regulators, have been associated with various types of cancer. Breast cancer is a major health threat for women worldwide. Many miRNAs were reported to be associated with the progression and carcinogenesis of breast cancer.

Risk of dementia in patients with insomnia and long-term use of hypnotics: a population-based retrospective cohort study.

Background: Hypnotics have been reported to be associated with dementia. However, the relationship between insomnia, hypnotics and dementia is still controversial. We sought to examine the risk of dementia in patients with long-term insomnia and the contribution of hypnotics.

Women with endometriosis are more likely to suffer from migraines: a population-based study.

Previous research suggests that a co-morbid relationship exists between migraine and endometriosis; however, results have been inconsistent. In addition, female hormones, which are important in the pathogenesis and management of endometriosis, have been reported to precipitate migraine attacks and may confound the results. The aim of this population-based cohort study was to explore the relationship between migraine and endometriosis in women of reproductive age (18-51 years).