Abstract We present a new de novo transcriptome assembler, Bridger, which takes advantage of techniques employed in Cufflinks to overcome limitations the existing assemblers. When tested on dog, human, and mouse RNA-seq data, Bridger assembled more full-length reference transcripts while reporting considerably fewer candidate transcripts, hence greatly reducing false positive comparison with state-of-the-art It runs substantially faster requires much less memory space than most More...

Abstract Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which shown improve noninvasive early diagnosis of cancer. It remains challenging for computational approaches achieve performance comparable experienced radiologists. Here we present NoduleX, a systematic approach from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, analyze >1000 nodules images the LIDC/IDRI cohort. All were...

High-throughput RNA-seq technology has provided an unprecedented opportunity to reveal the very complex structures of transcriptomes. However, it is important and highly challenging task assemble vast amounts short reads into transcriptomes with alternative splicing isoforms. In this study, we present a novel de novo assembler, BinPacker, by modeling transcriptome assembly problem as tracking set trajectories items their sizes representing coverage corresponding isoforms solving series...

Raman spectra have been widely used in biology, physics, and chemistry become an essential tool for the studies of macromolecules. Nevertheless, raw signal is often obscured by a broad background curve (or baseline) due to intrinsic fluorescence organic molecules, which leads unpredictable negative effects quantitative analysis spectra. Therefore, it correct this baseline before analyzing Polynomial fitting has proven be most convenient simplest method high accuracy. In polynomial fitting,...

We develop new techniques for deriving very strong computational lower bounds a class of well-known NP-hard problems, including weighted satisfiability, dominating set, hitting set cover, clique, and independent set. For example, although trivial enumeration can easily test in time O(nk) if given graph n vertices has clique size k, we prove that unless an unlikely collapse occurs parameterized complexity theory, the problem is not solvable f(k) no(k) any function f, even restrict parameter...

Abstract Biclustering algorithms, which aim to provide an effective and efficient way analyze gene expression data by finding a group of genes with trend-preserving patterns under certain conditions, have been widely developed since Morgan et al. pioneered work about partitioning matrix into submatrices approximately constant values. However, the identification general biclusters are most meaningful substructures hidden in remains highly challenging problem. We found elementary method...

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis UAV-derived data advanced machine learning models could improve real-time management in agricultural systems, but guidance this integration is currently limited. Here we compare two deep learning-based strategies warning detection stress, using multitemporal throughout the growing season to predict...

Lung cancer is the leading cause of deaths. Low-dose computed tomography (CT) screening has been shown to significantly reduce lung mortality but suffers from a high false positive rate that leads unnecessary diagnostic procedures. The development deep learning techniques potential help improve technology. Here we present algorithm, DeepScreener, which can predict patient's status volumetric CT scan. DeepScreener based on our model Spatial Pyramid Pooling, ranked 16th 1972 teams (top 1%) in...

The relationship among the large amount of biological data has become a hot research topic. It is desirable to have clustering methods group similar together so that, when lot needed, all are easily found in close proximity some search result. Here we study popular method, k-means clustering, for clustering. We implement two different algorithms and compare results. Lloyd's progressive greedy Our experimentation compares running times distance efficiency.

Abstract Currently, there are still problems of cost overruns and resource squandering in engineering management, which cannot ensure that project schedules, quality, costs up to standard. This paper focuses on the analysis critical path management projects using earned value method, focusing schedule deviations activities identifying improve profits reduce costs. Applying methodology this a bridge project, we find root causes by drawing network diagrams, calculating Earned Value parameters...

In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced. The method called EBIC aims to detect biologically meaningful, order-preserving patterns in complex data. proposed probably the first one capable of discovering with accuracy exceeding 50% multiple real gene expression datasets. It also very few methods designed for parallel environments graphics processing units (GPUs). We demonstrate that outperforms state-of-the-art methods, terms recovery...

We present here the Arkansas AI-Campus solution method for 2019 Kidney Tumor Segmentation Challenge (KiTS19). Our team participated KiTS19 four months, from March to July of 2019. This paper provides a summary our methods, training, testing and validation results this grand challenge in biomedical imaging analysis. deep learning model is an ensemble U-Net models developed after many variations. has consistent performance on local test dataset final competition independent dataset. The...

Abstract Background The increasing adoption of intestinal ultrasound (IUS) for monitoring inflammatory bowel diseases (IBD) by IBD providers has uncovered new challenges regarding standardized image interpretation and limitations as a research tool. Artificial intelligence approaches can help address these challenges. We aim to determine the feasibility radiomic analysis IUS images if radiomics-based classification model accurately differentiate between normal abnormal images. will also...

Accurate high-resolution three-dimensional (3D) models are essential for a non-invasive analysis of phenotypic characteristics plants. Previous limitations in 3D computer vision algorithms have led to reliance on volumetric methods or expensive hardware record plant structure. We present an image-based reconstruction system that can be achieved by using single camera and rotation stand. Our method is based the structure from motion method, with SIFT image feature descriptor. In order improve...

Abstract Background Maize is one of the most important crops in world. With exponentially increasing population and need for ever increased food feed production, an yield maize grain (as well as rice, wheat other grains) will be critical. development understood from perspective morphology, hormone responses, storage reserve accumulation. This includes various studies on gene expression during embryo maturation but a global study has not been possible until recently. Transcriptome analysis...

Computational alignment of a biopolymer sequence (e.g., an RNA or protein) to structure is effective approach predict and search for the new sequences. To identify remote homologs, structure-sequence has consider not only similarity, but also spatially conserved conformations caused by residue interactions and, consequently, computationally intractable. It difficult cope with inefficiency without compromising accuracy, especially in genomes large databases. This paper introduces novel method...

Biclustering is an unsupervised technique of simultaneous clustering rows and columns input matrix. With multiple biclustering algorithms proposed, UniBic remains one the most accurate methods developed so far.In this paper we introduce a Bioconductor package called runibic with parallel implementation UniBic. For convenience algorithm was reimplemented, parallelized wrapped within R runibic. The includes: (i) couple times faster version original sequential algorithm, (ii) much more...

The authors emphasize diversity, equity, and inclusion in STEM education artificial intelligence (AI) research, focusing on LGBTQ+ representation. They discuss the challenges faced by queer scientists, educational resources, implementation of National AI Campus, notion intersectionality. hope to ensure supportive respectful engagement across all communities.

Based on the framework of parameterized complexity theory, we derive tight lower bounds computational for a number well-known NP-hard problems. We start by proving general result, namely that weighted satisfiability problem depth-t circuits cannot be solved in time n/sup o(k)/poly(m), where n is circuit input length, m size, and k parameter, unless (t - l)-st level W[t $1] W-hierarchy collapses to FPT. By refining this technique, prove group problems, including SAT, dominating set, hitting...