Enhanced analysis of tabular data through Multi-representation DeepInsight.

Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data.

Advances in AI and machine learning for predictive medicine.

The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate prediction.

scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning.

Annotation of cell-types is a critical step in the analysis of single-cell RNA sequencing (scRNA-seq) data that allows the study of heterogeneity across multiple cell populations. Currently, this is most commonly done using unsupervised clustering algorithms, which project single-cell expression data into a lower dimensional space and then cluster cells based on their distances from each other. However, as these methods do not use reference datasets, they can only achieve a rough classification of cell-types, and it is difficult to improve the recognition accuracy further.

Topologically associating domain underlies tissue specific expression of long intergenic non-coding RNAs.

Accumulating evidence indicates that long intergenic non-coding RNAs (lincRNAs) show more tissue-specific expression patterns than protein-coding genes (PCGs). However, although lincRNAs are subject to canonical transcriptional regulation like PCGs, the molecular basis for the specificity of their expression patterns remains unclear. Here, using expression data and coordinates of topologically associating domains (TADs) in human tissues, we show that lincRNA loci are significantly enriched in the more internal region of TADs compared to PCGs and that lincRNAs within TADs have higher tissue specificity than those outside TADs.

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics.

Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcome. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging developments continue to be hampered by very high dimensionality of the datasets in combination with insufficiently large numbers of annotated samples.

Association between high immune activity and worse prognosis in uveal melanoma and low-grade glioma in TCGA transcriptomic data.

Background: Immune status in the tumor microenvironment is an important determinant of cancer progression and patient prognosis. Although a higher immune activity is often associated with a better prognosis, this trend is not absolute and differs across cancer types. We aimed to give insights into why some cancers do not show better survival despite higher immunity by assessing the relationship between different biological factors, including cytotoxicity, and patient prognosis in various cancer types using RNA-seq data collected by The Cancer Genome Atlas.

DeepFeature: feature selection in nonimage data using convolutional neural network.

Artificial intelligence methods offer exciting new capabilities for the discovery of biological mechanisms from raw data because they are able to detect vastly more complex patterns of association that cannot be captured by classical statistical tests. Among these methods, deep neural networks are currently among the most advanced approaches and, in particular, convolutional neural networks (CNNs) have been shown to perform excellently for a variety of difficult tasks. Despite that applications of this type of networks to high-dimensional omics data and, most importantly, meaningful interpretation of the results returned from such models in a biomedical context remains an open problem.

Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer's disease created by integrative analysis of multi-omics data.

Background: Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), but not all MCI patients develop AD. Biomarkers for early detection of individuals at high risk for MCI-to-AD conversion are urgently required.

Methods: We used blood-based microRNA expression profiles and genomic data of 197 Japanese MCI patients to construct a prognosis prediction model based on a Cox proportional hazard model.

Clinical usefulness of multigene screening with phenotype-driven bioinformatics analysis for the diagnosis of patients with monogenic diabetes or severe insulin resistance.

Aims: Monogenic diabetes is clinically heterogeneous and differs from common forms of diabetes (type 1 and 2). We aimed to investigate the clinical usefulness of a comprehensive genetic testing system, comprised of targeted next-generation sequencing (NGS) with phenotype-driven bioinformatics analysis in patients with monogenic diabetes, which uses patient genotypic and phenotypic data to prioritize potentially causal variants.

Methods: We performed targeted NGS of 383 genes associated with monogenic diabetes or common forms of diabetes in 13 Japanese patients with suspected (n = 10) or previously diagnosed (n = 3) monogenic diabetes or severe insulin resistance.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods.

A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.

Background: Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer's disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential biomarkers of prodromal DLB is still challenging. MicroRNAs (miRNAs) have recently received a lot of attention as a source of novel biomarkers.

DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture.

It is critical, but difficult, to catch the small variation in genomic or other kinds of data that differentiates phenotypes or categories. A plethora of data is available, but the information from its genes or elements is spread over arbitrarily, making it challenging to extract relevant details for identification. However, an arrangement of similar genes into clusters makes these differences more accessible and allows for robust identification of hidden mechanisms (e.

Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data.

Alzheimer's disease (AD) is the most common subtype of dementia, followed by Vascular Dementia (VaD), and Dementia with Lewy Bodies (DLB). Recently, microRNAs (miRNAs) have received a lot of attention as the novel biomarkers for dementia. Here, using serum miRNA expression of 1,601 Japanese individuals, we investigated potential miRNA biomarkers and constructed risk prediction models, based on a supervised principal component analysis (PCA) logistic regression method, according to the subtype of dementia.

An integrative machine learning approach for prediction of toxicity-related drug safety.

Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail.

Integrated analysis of human genetic association study and mouse transcriptome suggests LBH and SHF genes as novel susceptible genes for amyloid-β accumulation in Alzheimer's disease.

Alzheimer's disease (AD) is a common neurological disease that causes dementia in humans. Although the reports of associated pathological genes have been increasing, the molecular mechanism leading to the accumulation of amyloid-β (Aβ) in human brain is still not well understood. To identify novel genes that cause accumulation of Aβ in AD patients, we conducted an integrative analysis by combining a human genetic association study and transcriptome analysis in mouse brain.

Publisher Correction: IMSindel: An accurate intermediate-size indel detection tool incorporating de novo assembly and gapped global-local alignment with split read analysis.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

A novel homozygous missense mutation in the SH3-binding motif of STAMBP causing microcephaly-capillary malformation syndrome.

Microcephaly-capillary malformation syndrome is a congenital and neurodevelopmental disorder caused by biallelic mutations in the STAMBP gene. Here we identify the novel homozygous mutation located in the SH3 binding motif of STAMBP (NM_006463.4) (c.

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures.

Genome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.

IMSindel: An accurate intermediate-size indel detection tool incorporating de novo assembly and gapped global-local alignment with split read analysis.

Insertions and deletions (indels) have been implicated in dozens of human diseases through the radical alteration of gene function by short frameshift indels as well as long indels. However, the accurate detection of these indels from next-generation sequencing data is still challenging. This is particularly true for intermediate-size indels (≥50 bp), due to the short DNA sequencing reads.

A novel genetic syndrome with STARD9 mutation and abnormal spindle morphology.

Intellectual disability (ID) is one of neurodevelopmental disorders characterized by serious defects in both intelligence and adaptive behavior. Although it has been suggested that genetic aberrations associated with the process of cell division underlie ID, the cytological evidence for mitotic defects in actual patient's cells is rarely reported. Here, we report a novel mutation in the STARD9 (also known as KIF16A) gene found in a patient with severe ID, characteristic features, epilepsy, acquired microcephaly, and blindness.

Structural Basis and Genotype-Phenotype Correlations of INSR Mutations Causing Severe Insulin Resistance.

The insulin receptor () gene was analyzed in four patients with severe insulin resistance, revealing five novel mutations and a deletion that removed exon 2. A patient with Donohue syndrome (DS) had a novel p.V657F mutation in the second fibronectin type III domain (FnIII-2), which contains the α-β cleavage site and part of the insulin-binding site.

Arete - candidate gene prioritization using biological network topology with additional evidence types.

Background: Refinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the qualitative and semantic complexity of biological data, successfully addressing this challenge requires development of flexible and interoperable solutions for making the best possible use of the largest possible fraction of all available data.

Results: We have developed an easily accessible framework that links two established network-based gene prioritization approaches with a supporting isolation forest-based integrative ranking method.