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The pervasive nature of long non-coding RNA (lncRNA) transcription in the mammalian genomes has changed our protein-centric view of genomes. But the identification of lncRNAs is an important task to discover their functional role in species. The rapid development of next-generation sequencing technology leveraged the opportunity to discover many lncRNA transcripts. However, the cost and time-consuming nature of transcriptomics verification techniques barred the research community from focusing on lncRNA identification. To overcome these challenges we developed LNCRI (Long Non-Coding RNA Identifier), a novel machine learning (ML)-based tool for the identification of lncRNA transcripts. We leveraged weighted k-mer, pseudo nucleotide composition, hexamer usage bias, Fickett score, information of open reading frame, UTR regions, and HMMER score as a feature set to develop LNCRI. LNCRI outperformed other existing models in the task of distinguishing lncRNA transcripts from protein-coding mRNA transcripts with high accuracy in human and mouse. LNCRI also outperformed the existing tools for cross-species prediction on chimpanzee, monkey, gorilla, orangutan, cow, pig, frog and zebrafish. We applied the SHAP algorithm to demonstrate the importance of most dominating features that were leveraged in the model. We believe our tool will support the research community to identify the lncRNA transcripts in a highly accurate manner. The benchmark datasets and source code are available in GitHub: http://github.com/smusleh/LNCRI. © 2013 IEEE.

The structure of blood vessels in the retina is a crucial factor in identifying and forecasting various eye diseases like cardiovascular diseases, diabetes, and other diseases. Therefore, detecting the structure of blood vessels from retinal fundus images is a critical field of research in healthcare. This study employed a novel deep learning model to segment vessels for different diseases, including Glaucoma, Diabetic Retinopathy (DR), and Age-related Macular Degeneration (AMD). We considered multiple transfer learning-based models and discovered that the ResNet-based U-Net architecture was the most effective for vessel segmentation, achieving the highest Dice Score above 84% for disease-agnostic, and 82%-84% for disease-specific conditions. We believe the proposed methodology will help to advance retinal vessel segmentation process and enhance the screening process of diseases based on retinal fundus images in clinical settings of Qatar Biobank as well as other biobanks across the globe. © 2023 IEEE.

Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we developed the ALL Detector (ALLD), which is a deep learning-based network to distinguish ALL patients from healthy individuals based on blast cell microscopic images. We evaluated multiple DL-based models and the ResNet-based model performed the best with 98% accuracy in the classification task. We also compared the performance of ALLD against state-of-the-art tools utilized for the same purpose, and ALLD outperformed them all. We believe that ALLD will support pathologists to explicitly diagnose ALL in the early stages and reduce the burden on clinical practice overall. © 2022 The authors and IOS Press.

Promoter regions of long non-coding RNA (lncRNA) genes are crucial to understand their transcriptional regulatory pattern. LncRNA genes, being more cryptic than protein-coding genes in terms of their functionality and biogenesis divergence, are lacking in number of existing studies to elucidate the roles of their promoters compared to their counterparts. Based on the overlap between epigenetic marks and transcription start sites, human lncRNAs were categorized into two broad categories: enhancer-originated lncRNAs (e-lncRNAs) and promoter-originated lncRNAs (p-lncRNAs) and hence these two groups are subject to distinct transcriptional regulatory programs. To understand the difference in the transcriptional regulatory mechanisms that governs p- and e-lncRNAs, we studied the promoter sequences of these two groups of lncRNAs including distinct transcription factor (TF) proteins that favor p-over e-lncRNA (and vice versa). In addition, we developed a convolution neural network (CNN) based deep learning (DL) framework DeePEL (deep p-, e-lncRNA promoter recognizer), to classify the promoter of p- and e-lncRNAs. To the best of our knowledge, this is the first attempt to classify these two groups of lncRNA promoters, using sequence and TF information, based on DL framework. We report several sequence specific signatures in the promoter regions as well as several distinct TFs specific to groups of lncRNAs that will help in understanding the promoter-proximal transcriptional regulation of p-lncRNAs and e-lncRNAs. © 2019 IEEE.

Dual-energy X-ray absorptiometry (DXA) has been traditionally used to assess body composition covering bone, fat and muscle content. Cardiovascular disease (CVD) has deleterious effects on bone health and fat composition. Therefore, early detection of bone health, fat and muscle composition would help to anticipate a proper diagnosis and treatment plan for CVD patients. In this study, we leveraged machine learning (ML)-based models to predict CVD using DXA, demonstrating that it can be considered an innovative approach for early detection of CVD. We leveraged state-of-the-art ML models to classify the CVD group from non-CVD group. The proposed logistic regression-based model achieved nearly 80% accuracy. Overall, the bone mineral density, fat content, muscle mass and bone surface area measurements were elevated in the CVD group compared to non-CVD group. Ablation study revealed a more successful discriminatory power of fat content and bone mineral density than muscle mass and bone areas. To the best of our knowledge, this work is the first ML model to reveal the association between DXA measurements and CVD in the Qatari population. We believe this study will open new avenues of introducing DXA in creating the diagnosis and treatment plan of cardiovascular diseases. © 2022 The authors and IOS Press.

We demonstrate that a nonzero strangeness contribution to the spacelike electromagnetic form factor of the nucleon is evidence for a strange-antistrange asymmetry in the nucleon's light-front wave function, thus implying different nonperturbative contributions to the strange and antistrange quark distribution functions. A recent lattice QCD calculation of the nucleon strange quark form factor predicts that the strange quark distribution is more centralized in coordinate space than the antistrange quark distribution, and thus the strange quark distribution is more spread out in light-front momentum space. We show that the lattice prediction implies that the difference between the strange and antistrange parton distribution functions, s(x)-s(x), is negative at small-x and positive at large-x. We also evaluate the strange quark form factor and s(x)-s(x) using a baryon-meson fluctuation model and a novel nonperturbative model based on light-front holographic QCD. This procedure leads to a Veneziano-like expression of the form factor, which depends exclusively on the twist of the hadron and the properties of the Regge trajectory of the vector meson which couples to the quark current in the hadron. The holographic structure of the model allows us to introduce unambiguously quark masses in the form factors and quark distributions preserving the hard scattering counting rule at large-Q2 and the inclusive counting rule at large-x. Quark masses modify the Regge intercept which governs the small-x behavior of quark distributions, therefore modifying their small-x singular behavior. Both nonperturbative approaches provide descriptions of the strange-antistrange asymmetry and intrinsic strangeness in the nucleon consistent with the lattice QCD result. © 2018 authors. Published by the American Physical Society.