Integrative approaches based on genomic techniques in the functional studies on enhancers.

With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.

[The collaborative project "Personalized medicine for oncology" (PM4Onco) as part of the Medical Informatics Initiative (MII)]. / Das Verbundprojekt "Personalisierte Medizin für die Onkologie" (PM4Onco) als Teil der Medizininformatik-Initiative (MII).

The collaborative project Personalized Medicine for Oncology (PM4Onco) was launched in 2023 as part of the National Decade against Cancer (NKD) and is executed within the Medical Informatics Initiative (MII). Its aim is to establish a sustainable infrastructure for the integration and use of data from clinical and biomedical research and therefore combines the experience and preliminary work of all four consortia of the MII and the leading oncology centers in Germany. The data provided by PM4Onco will be prepared in a suitable form to support decision making in molecular tumor boards. This concept and infrastructure will be extended to 23 participating partner sites and thus improve access to targeted therapies based on clinical information and analysis of molecular genetic alterations in tumors at different stages of the disease. This will help to improve the treatment and prognosis of tumor diseases.Clinical cancer registries are involved in the project to improve data quality through standardized documentation routines. Clinical experts advise on the expansion of the core datasets for personalized medicine (PM). Information on quality of life and treatment outcomes reported by patients in questionnaires, which is rarely collected outside of clinical trials, will make a significant contribution. Patient representatives are involved from the onset to ensure that the important perspective of patients is taken into account in the decision-making process. PM4Onco thus creates an alliance between the MII, oncological centers of excellence, clinical cancer registries, young scientists, patients, and citizens to strengthen and advance PM in cancer therapy.

Sleepy Mice Case Study: Implementation and Assessment.

Case studies are a valuable teaching tool to engage students in course content using real-world scenarios. As part of the High-throughput Discovery Science & Inquiry-based Case Studies for Today's Students (HITS) Research Coordination Network (RCN), our team has created the Sleepy Mice Case Study for students to engage with RStudio and the Allen Institute for Brain Science's open access high-throughput sleep dataset on mice. Sleep is important for health, a familiar concern to college students, and was a basis for this case study. In this case, students completed an initial homework assignment, in-class work, and a final take-home application assignment. The case study was implemented in synchronous and asynchronous Introductory Neuroscience courses, a Biopsychology course, and a Human Anatomy and Physiology course, reflecting its versatility. The case can be used to teach course-specific learning objectives such as sleep-related content and/or science data processing skills. The case study was successful as shown by gains in student scores and confidence in achieving learning objectives. Most students reported enjoying learning about sleep deprivation course content using the case study. Best practices based on instructor experiences in implementation are also included to facilitate future use so that the Sleepy Mice Case Study can be used to teach content and/or research-related skills in various courses and modalities.

Mapping Human Neuronal Diversity in the Search for New Therapeutics: Using Real Human Neuron Data Sets to Build Student Quantitative Skills.

Case studies are a high impact educational practice that engage students in collaborative problem solving through storytelling. HITS, an NSF funded research coordination network dedicated to exposing students to high-throughput discovery science, drove creation of this case. In this case, students imagine themselves as researchers developing new therapeutic drugs for epilepsy. Specifically, students work with the Allen Cell Types Database, which is the result of collaborative, interdisciplinary open science. Neurosurgeons partnered with the Allen institute to provide living human brain tissue for electrophysiological, morphological, and transcriptomic study. Students collaborate to collect and organize data, investigate a research question they identified, and perform fundamental statistical analyses to address their question. By leveraging the unique Cell Types dataset the case enhances student knowledge of epilepsy, illuminates high-throughput scientific approaches, and builds quantitative and research related skills. The case is also versatile and was implemented in two distinct courses. The case can also be taught in different modalities, in person or remote, with a combination of synchronous and asynchronous work. Indirect and direct measures along with quantitative and qualitative approaches were used for case assessment and improvement. Students performed well on case related exam questions, reported high confidence in their achievement of the learning outcomes, and enjoyed the case's link to neurological disease, real research data and advanced technological approaches. Our assessment findings and instructor implementation experiences are also included to facilitate the adoption or adaptation of the case for a variety of courses and/or modalities in neuroscience and STEM related curricula.

Sequence-dependent RNA helix conformational preferences predictably impact tertiary structure formation.

Structured RNAs and RNA complexes underlie biological processes ranging from control of gene expression to protein translation. Approximately 50% of nucleotides within known structured RNAs are folded into Watson-Crick (WC) base pairs, and sequence changes that preserve these pairs are typically assumed to preserve higher-order RNA structure and binding of macromolecule partners. Here, we report that indirect effects of the helix sequence on RNA tertiary stability are, in fact, significant but are nevertheless predictable from a simple computational model called RNAMake-∆∆G. When tested through the RNA on a massively parallel array (RNA-MaP) experimental platform, blind predictions for >1500 variants of the tectoRNA heterodimer model system achieve high accuracy (rmsd 0.34 and 0.77 kcal/mol for sequence and length changes, respectively). Detailed comparison of predictions to experiments support a microscopic picture of how helix sequence changes subtly modulate conformational fluctuations at each base-pair step, which accumulate to impact RNA tertiary structure stability. Our study reveals a previously overlooked phenomenon in RNA structure formation and provides a framework of computation and experiment for understanding helix conformational preferences and their impact across biological RNA and RNA-protein assemblies.

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis.

BACKGROUND: Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. RESULTS: High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). CONCLUSIONS: High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.

Temporal Splicing Switches in Elements of the TNF-Pathway Identified by Computational Analysis of Transcriptome Data for Human Cell Lines.

Alternative splicing plays an important role in numerous cellular processes and aberrant splice decisions are associated with cancer. Although some studies point to a regulation of alternative splicing and its effector mechanisms in a time-dependent manner, the extent and consequences of such a regulation remains poorly understood. In the present work, we investigated the time-dependent production of isoforms in two Hodgkin lymphoma cell lines of different progression stages (HD-MY-Z, stage IIIb and L-1236, stage IV) compared to a B lymphoblastoid cell line (LCL-HO) with a focus on tumour necrosis factor (TNF) pathway-related elements. For this, we used newly generated time-course RNA-sequencing data from the mentioned cell lines and applied a computational pipeline to identify genes with isoform-switching behaviour in time. We analysed the temporal profiles of the identified events and evaluated in detail the potential functional implications of alterations in isoform expression for the selected top-switching genes. Our data indicate that elements within the TNF pathway undergo a time-dependent variation in isoform production with a putative impact on cell migration, proliferation and apoptosis. These include the genes TRAF1, TNFRSF12A and NFKB2. Our results point to a role of temporal alternative splicing in isoform production, which may alter the outcome of the TNF pathway and impact on tumorigenesis.

Genome-wide identification and characterization of tRNA-derived RNA fragments in land plants.

KEY MESSAGE: The manuscript by Alves et al. entitled "Genome-wide identification and characterization of tRNA-derived RNA fragments in land plants" describes the identification and characterization of tRNAderived sRNA fragments in plants. By combining bioinformatic analysis and genetic and molecular approaches, we show that tRF biogenesis does not rely on canonical microRNA/siRNA processing machinery (i.e., independent of DICER-LIKE proteins). Moreover, we provide evidences that the Arabidopsis S-like Ribonuclease 1 (RNS1) might be involved in the biogenesis of tRFs. Detailed analyses showed that plant tRFs are sorted into different types of ARGONAUTE proteins and that they have potential target candidate genes. Our work advances the understanding of the tRF biology in plants by providing evidences that plant and animal tRFs shared common features and raising the hypothesis that an interplay between tRFs and other sRNAs might be important to fine-tune gene expression and protein biosynthesis in plant cells. Small RNA (sRNA) fragments derived from tRNAs (3'-loop, 5'-loop, anti-codon loop), named tRFs, have been reported in several organisms, including humans and plants. Although they may interfere with gene expression, their biogenesis and biological functions in plants remain poorly understood. Here, we capitalized on small RNA sequencing data from distinct species such as Arabidopsis thaliana, Oryza sativa, and Physcomitrella patens to examine the diversity of plant tRFs and provide insight into their properties. In silico analyzes of 19 to 25-nt tRFs derived from 5' (tRF-5s) and 3'CCA (tRF-3s) tRNA loops in these three evolutionary distant species showed that they are conserved and their abundance did not correlate with the number of genomic copies of the parental tRNAs. Moreover, tRF-5 is the most abundant variant in all three species. In silico and in vivo expression analyses unraveled differential accumulation of tRFs in Arabidopsis tissues/organs, suggesting that they are not byproducts of tRNA degradation. We also verified that the biogenesis of most Arabidopsis 19-25 nt tRF-5s and tRF-3s is not primarily dependent on DICER-LIKE proteins, though they seem to be associated with ARGONAUTE proteins and have few potential targets. Finally, we provide evidence that Arabidopsis ribonuclease RNS1 might be involved in the processing and/or degradation of tRFs. Our data support the notion that an interplay between tRFs and other sRNAs might be important to fine tune gene expression and protein biosynthesis in plant cells.

A three-groups model for high-throughput survival screens.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative condition characterized by the progressive deterioration of motor neurons in the cortex and spinal cord. Using an automated robotic microscope platform that enables the longitudinal tracking of thousands of single neurons, we examine the effects a large library of compounds on modulating the survival of primary neurons expressing a mutation known to cause ALS. The goal of our analysis is to identify the few potentially beneficial compounds among the many assayed, the vast majority of which do not extend neuronal survival. This resembles the large-scale simultaneous inference scenario familiar from microarray analysis, but transferred to the survival analysis setting due to the novel experimental setup. We apply a three-component mixture model to censored survival times of thousands of individual neurons subjected to hundreds of different compounds. The shrinkage induced by our model significantly improves performance in simulations relative to performing treatment-wise survival analysis and subsequent multiple testing adjustment. Our analysis identified compounds that provide insight into potential novel therapeutic strategies for ALS.

WWOX-related encephalopathies: delineation of the phenotypical spectrum and emerging genotype-phenotype correlation.

BACKGROUND: Homozygous mutations in WWOX were reported in eight individuals of two families with autosomal recessive spinocerebellar ataxia type 12 and in two siblings with infantile epileptic encephalopathy (IEE), including one who deceased prior to DNA sampling. METHODS: By combining array comparative genomic hybridisation, targeted Sanger sequencing and next generation sequencing, we identified five further patients from four families with IEE due to biallelic alterations of WWOX. RESULTS: We identified eight deleterious WWOX alleles consisting in four deletions, a four base-pair frameshifting deletion, one missense and two nonsense mutations. Genotype-phenotype correlation emerges from the seven reported families. The phenotype in four patients carrying two predicted null alleles was characterised by (1) little if any psychomotor acquisitions, poor spontaneous motility and absent eye contact from birth, (2) pharmacoresistant epilepsy starting in the 1st weeks of life, (3) possible retinal degeneration, acquired microcephaly and premature death. This contrasted with the less severe autosomal recessive spinocerebellar ataxia type 12 phenotype due to hypomorphic alleles. In line with this correlation, the phenotype in two siblings carrying a null allele and a missense mutation was intermediate. CONCLUSIONS: Our results obtained by a combination of different molecular techniques undoubtedly incriminate WWOX as a gene for recessive IEE and illustrate the usefulness of high throughput data mining for the identification of genes for rare autosomal recessive disorders. The structure of the WWOX locus encompassing the FRA16D fragile site might explain why constitutive deletions are recurrently reported in genetic databases, suggesting that WWOX-related encephalopathies, although likely rare, may not be exceptional.

Probabilistic Assessment of High-Throughput Wireless Sensor Networks.

Structural health monitoring (SHM) using wireless smart sensors (WSS) has the potential to provide rich information on the state of a structure. However, because of their distributed nature, maintaining highly robust and reliable networks can be challenging. Assessing WSS network communication quality before and after finalizing a deployment is critical to achieve a successful WSS network for SHM purposes. Early studies on WSS network reliability mostly used temporal signal indicators, composed of a smaller number of packets, to assess the network reliability. However, because the WSS networks for SHM purpose often require high data throughput, i.e., a larger number of packets are delivered within the communication, such an approach is not sufficient. Instead, in this study, a model that can assess, probabilistically, the long-term performance of the network is proposed. The proposed model is based on readily-available measured data sets that represent communication quality during high-throughput data transfer. Then, an empirical limit-state function is determined, which is further used to estimate the probability of network communication failure. Monte Carlo simulation is adopted in this paper and applied to a small and a full-bridge wireless networks. By performing the proposed analysis in complex sensor networks, an optimized sensor topology can be achieved.

Network-based approaches for drug response prediction and targeted therapy development in cancer.

Signaling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways. This complexity explains frequent failure of one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. To overcome the robustness of cell signaling network, cancer treatment should be extended to a combination therapy approach. Integrating and analyzing patient high-throughput data together with the information about biological signaling machinery may help deciphering molecular patterns specific to each patient and finding the best combinations of candidates for therapeutic targeting. We review state of the art in the field of targeted cancer medicine from the computational systems biology perspective. We summarize major signaling network resources and describe their characteristics with respect to applicability for drug response prediction and intervention targets suggestion. Thus discuss methods for prediction of drug sensitivity and intervention combinations using signaling networks together with high-throughput data. Gradual integration of these approaches into clinical routine will improve prediction of response to standard treatments and adjustment of intervention schemes.

Comparative transcriptome analysis of small noncoding RNAs in different stages of Trypanosoma brucei.

Trypanosoma brucei, a pathogen of human and domestic animals, is an early evolved parasitic protozoan with a complex life cycle. Most genes of this parasite are post-transcriptionally regulated. However, the mechanisms and the molecules involved remain largely unknown. We have deep-sequenced the small RNAs of two life stages of this parasite--the bloodstream form and the procyclic form. Our results show that the small RNAs of T. brucei could derive from multiple sources, including NATs (natural antisense transcripts), tRNAs, and rRNAs. Most of these small RNAs in the two stages were found to share uniform characteristics. However, our results demonstrate that their variety and expression show significant differences between different stages, indicating possible functional differentiation. Dicer-knockdown evidence further proved that some of the small interfering RNAs (siRNAs) could regulate the expression of genes. Based on the genome-wide analysis of the small RNAs in the two stages of T. brucei, our results not only provide evidence to study their differentiation but also shed light on questions regarding the origins and evolution of small RNA-based mechanisms in early eukaryotes.

Gene-disease association with literature based enrichment.

MOTIVATION: Gene set enrichment analysis (GSEA) annotates gene microarray data with functional information from the biomedical literature to improve gene-disease association prediction. We hypothesize that supplementing GSEA with comprehensive gene function catalogs built automatically using information extracted from the scientific literature will significantly enhance GSEA prediction quality. METHODS: Gold standard gene sets for breast cancer (BrCa) and colorectal cancer (CRC) were derived from the literature. Two gene function catalogs (CMeSH and CUMLS) were automatically generated. 1. By using Entrez Gene to associate all recorded human genes with PubMed article IDs. 2. Using the genes mentioned in each PubMed article and associating each with the article's MeSH terms (in CMeSH) and extracted UMLS concepts (in CUMLS). Microarray data from the Gene Expression Omnibus for BrCa and CRC was then annotated using CMeSH and CUMLS and for comparison, also with several pre-existing catalogs (C2, C4 and C5 from the Molecular Signatures Database). Ranking was done using, a standard GSEA implementation (GSEA-p). Gene function predictions for enriched array data were evaluated against the gold standard by measuring area under the receiver operating characteristic curve (AUC). RESULTS: Comparison of ranking using the literature enrichment catalogs, the pre-existing catalogs as well as five randomly generated catalogs show the literature derived enrichment catalogs are more effective. The AUC for BrCa using the unenriched gene expression dataset was 0.43, increasing to 0.89 after gene set enrichment with CUMLS. The AUC for CRC using the unenriched gene expression dataset was 0.54, increasing to 0.9 after enrichment with CMeSH. C2 increased AUC (BrCa 0.76, CRC 0.71) but C4 and C5 performed poorly (between 0.35 and 0.5). The randomly generated catalogs also performed poorly, equivalent to random guessing. DISCUSSION: Gene set enrichment significantly improved prediction of gene-disease association. Selection of enrichment catalog had a substantial effect on prediction accuracy. The literature based catalogs performed better than the MSigDB catalogs, possibly because they are more recent. Catalogs generated automatically from the literature can be kept up to date. CONCLUSION: Prediction of gene-disease association is a fundamental task in biomedical research. GSEA provides a promising method when using literature-based enrichment catalogs. AVAILABILITY: The literature based catalogs generated and used in this study are available from http://www2.chi.unsw.edu.au/literature-enrichment.

BactEXTRACT: an R Shiny app to quickly extract, plot and analyse bacterial growth and gene expression data.

To streamline the analysis and visualization of bacterial growth and gene expression data obtained by microtitre plate readers, we developed BactEXTRACT, an intuitive, easy-to-use R Shiny application. BactEXTRACT simplifies the transition from raw optical density, fluorescence and luminescence measurements to publication-ready plots. This package offers a user-friendly interface that reduces the complexity involved in growth curve and gene expression analysis and is generally applicable. BactEXTRACT is available at https://veeninglab.com/bactextract.

Deep learning with mixup augmentation for improved pore detection during additive manufacturing.

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM's intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

Bioelectronic Medicine: a multidisciplinary roadmap from biophysics to precision therapies.

Bioelectronic Medicine stands as an emerging field that rapidly evolves and offers distinctive clinical benefits, alongside unique challenges. It consists of the modulation of the nervous system by precise delivery of electrical current for the treatment of clinical conditions, such as post-stroke movement recovery or drug-resistant disorders. The unquestionable clinical impact of Bioelectronic Medicine is underscored by the successful translation to humans in the last decades, and the long list of preclinical studies. Given the emergency of accelerating the progress in new neuromodulation treatments (i.e., drug-resistant hypertension, autoimmune and degenerative diseases), collaboration between multiple fields is imperative. This work intends to foster multidisciplinary work and bring together different fields to provide the fundamental basis underlying Bioelectronic Medicine. In this review we will go from the biophysics of the cell membrane, which we consider the inner core of neuromodulation, to patient care. We will discuss the recently discovered mechanism of neurotransmission switching and how it will impact neuromodulation design, and we will provide an update on neuronal and glial basis in health and disease. The advances in biomedical technology have facilitated the collection of large amounts of data, thereby introducing new challenges in data analysis. We will discuss the current approaches and challenges in high throughput data analysis, encompassing big data, networks, artificial intelligence, and internet of things. Emphasis will be placed on understanding the electrochemical properties of neural interfaces, along with the integration of biocompatible and reliable materials and compliance with biomedical regulations for translational applications. Preclinical validation is foundational to the translational process, and we will discuss the critical aspects of such animal studies. Finally, we will focus on the patient point-of-care and challenges in neuromodulation as the ultimate goal of bioelectronic medicine. This review is a call to scientists from different fields to work together with a common endeavor: accelerate the decoding and modulation of the nervous system in a new era of therapeutic possibilities.

The impact of network medicine in gastroenterology and hepatology.

In the footsteps of groundbreaking achievements made by biomedical research, another scientific revolution is unfolding. Systems biology draws from the chaos and complexity theory and applies computational models to predict emerging behavior of the interactions between genes, gene products, and environmental factors. Adaptation of systems biology to translational and clinical sciences has been termed network medicine, and is likely to change the way we think about preventing, predicting, diagnosing, and treating complex human diseases. Network medicine finds gene-disease associations by analyzing the unparalleled digital information discovered and created by high-throughput technologies (dubbed as "omics" science) and links genetic variance to clinical disease phenotypes through intermediate organizational levels of life such as the epigenome, transcriptome, proteome, and metabolome. Supported by large reference databases, unprecedented data storage capacity, and innovative computational analysis, network medicine is poised to find links between conditions that were thought to be distinct, uncover shared disease mechanisms and key drivers of the pathogenesis, predict individual disease outcomes and trajectories, identify novel therapeutic applications, and help avoid off-target and undesirable drug effects. Recent advances indicate that these perspectives are increasingly within our reach for understanding and managing complex diseases of the digestive system.

Matrix Linear Models for connecting metabolite composition to individual characteristics.

High-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how the association of metabolite levels with individual (sample) characteristics such as sex or treatment may depend on metabolite characteristics such as pathway. Typically this is one in a two-step process: In the first step we assess the association of each metabolite with individual characteristics. In the second step an enrichment analysis is performed by metabolite characteristics among significant associations. We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework we have previously developed for high-throughput genetic screens. Our framework can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). We demonstrate how MLM offers flexibility and interpretability by applying our method to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglycerides characteristics, such as the number of double bonds and the number of carbon atoms. The proposed method have been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available.

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types.

Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.