Hoganholst4907

During animal development, HOX transcription factors determine the fate of developing tissues to generate diverse organs and appendages. The power of these proteins is striking mis-expressing a HOX protein causes homeotic transformation of one body part into another. During development, HOX proteins interpret their cellular context through protein interactions, alternative splicing, and post-translational modifications to regulate cell proliferation, cell death, cell migration, cell differentiation, and angiogenesis. Although mutation and/or mis-expression of HOX proteins during development can be lethal, changes in HOX proteins that do not pattern vital organs can result in survivable malformations. In adults, mutation and/or mis-expression of HOX proteins disrupts their gene regulatory networks, deregulating cell behaviors and leading to arthritis and cancer. On the molecular level, HOX proteins are composed of DNA binding homeodomain, and large regions of unstructured, or intrinsically disordered, protein sequence. The primary roles of HOX proteins in arthritis and cancer suggest that mutations associated with these diseases in both the structured and disordered regions of HOX proteins can have substantial functional effects. These insights lead to new questions critical for understanding and manipulating HOX function in physiological and pathological conditions.Liquid-liquid phase separation (LLPS) brings together functionally related proteins through the intrinsic biophysics of proteins in a process that is driven by reducing free energy and maximizing entropy. The process of LLPS allows proteins to form structures, termed membrane-less organelles. These diverse, dynamic organelles are active in a wide range of processes in the nucleus, cytoplasm, mitochondria and synapse, and ranging from bacteria to plants to eukaryotes. RNA and DNA present long chained charged polymers that promote LLPS. Consequently, many RNA binding proteins (RBPs) and DNA binding proteins form membrane-less organelles. However, the highly concentrated phase separated state creates conditions that also promote formation of irreversible protein aggregates. Mutations in RNA and DNA binding proteins that increase the stability of irreversible aggregates also increase the accumulation of irreversible aggregates directly and from membrane-less organelles. Many of the RBPs that exhibit disease-linked mutations carry out cytoplasmic actions through stress granules, which are a pleiotropic type of RNA granule that regulates the translational response to stress. Phosphorylation and oligomerization of tau facilitates its interactions with RBPs and ribosomal proteins, affecting RNA translation; we propose that this is a major reason that tau becomes phosphorylated with stress. Persistent stress leads to the accumulation of irreversible aggregates composed of RBPs or tau, which then cause toxicity and form many of the hallmark pathologies of major neurodegenerative diseases. This pathophysiology ultimately leads to multiple forms of neurodegenerative diseases, the specific type of which reflects the temporal and spatial accumulation of different aggregating proteins.Directed stabilization of globular proteins via substitution of a minimal number of amino acid residues is one of the most complicated experimental tasks. In this work, we have successfully used algorithms for the evaluation of intrinsic disorder predisposition (such as PONDR® FIT and IsUnstruct) as tools for searching for the weakened regions in structured globular proteins. We have shown that the weakened regions found by these programs as regions with highest levels of predicted intrinsic disorder predisposition are a suitable target for introduction of stabilizing mutations.G protein-coupled receptors (GPCRs) and Nuclear Receptors (NRs) are two signaling machineries that are involved in major physiological processes and, as a consequence, in a substantial number of diseases. Therefore, they actually represent two major targets for drugs with potential applications in almost all public health issues. Full exploitation of these targets for therapeutic purposes nevertheless requires opening original avenues in drug design, and this in turn implies a better understanding of the molecular mechanisms underlying their functioning. However, full comprehension of how these complex systems function and how they are deregulated in a physiopathological context is obscured by the fact that these proteins include a substantial number of disordered regions that are central to their mechanism of action but whose structural and functional properties are still largely unexplored. In this chapter, we describe how these intrinsically disordered regions (IDR) or proteins (IDP) intervene, control and finely modulate the thermodynamics of complexes involved in GPCR and NR regulation, which in turn triggers a multitude of cascade of events that are exquisitely orchestrated to ultimately control the biological output.Intrinsically disordered proteins (IDPs) possess the property of inherent flexibility and can be distinguished from other proteins in terms of lack of any fixed structure. Such dynamic behavior of IDPs earned the name "Dancing Proteins." The exploration of these dancing proteins in viruses has just started and crucial details such as correlation of rapid evolution, high rate of mutation and accumulation of disordered contents in viral proteome at least understood partially. In order to gain a complete understanding of this correlation, there is a need to decipher the complexity of viral mediated cell hijacking and pathogenesis in the host organism. Further there is necessity to identify the specific patterns within viral and host IDPs such as aggregation; Molecular recognition features (MoRFs) and their association to virulence, host range and rate of evolution of viruses in order to tackle the viral-mediated diseases. The current book chapter summarizes the aforementioned details and suggests the novel opportunities for further research of IDPs senses in viruses.Significant progress has been achieved in recent years in the application of artificial intelligence (AI) for medical decision support. However, many AI-based systems often only provide a final prediction to the doctor without an explanation of its underlying decision-making process. In scenarios concerning deadly diseases, such as breast cancer, a doctor adopting an auxiliary prediction is taking big risks, as a bad decision can have very harmful consequences for the patient. We propose an auxiliary decision support system that combines ensemble learning with case-based reasoning to help doctors improve the accuracy of breast cancer recurrence prediction. The system provides a case-based interpretation of its prediction, which is easier for doctors to understand, helping them assess the reliability of the system's prediction and make their decisions accordingly. Our application and evaluation in a case study focusing on breast cancer recurrence prediction shows that the proposed system not only provides reasonably accurate predictions but is also well-received by oncologists.Electronic medical records (EMRs) contain a wealth of knowledge that can be used to assist doctors in making clinical decisions like disease diagnosis. Constructing a medical knowledge network (MKN) to link medical concepts in EMRs is an effective way to manage this knowledge. The quality of the diagnostic result made by MKN-based clinical decision support system depends on the accuracy of medical knowledge and the completeness of the network. However, collecting knowledge is a long-lasting and cumulative process, which means it's hard to construct a complete MKN with limited data. This study was conducted with the objective of developing an expandable EMR-based MKN to enhance capabilities in making an initial clinical diagnosis. selleck chemicals A network of symptom-indicate-disease knowledge in 992 Chinese EMRs (CEMRs) was manually constructed as Original-MKN, and an incremental expansion framework was applied to it to obtain an expandable MKN based on new CEMRs. The framework was composed by (1) integrating external knowledge extracted from the medical information websites and (2) mining potential knowledge with new EMRs. The framework also adopts a diagnosis-driven learning method to estimate the effectiveness of each knowledge in clinical practice. Experimental results indicate that our expanded MKN achieves a precision of 0.837 for a recall of 0.719 in clinical diagnosis, which outperforms Original-MKN and four classical machine learning methods. Furthermore, both external medical knowledge and potential medical knowledge benefit MKN expansion and disease diagnosis. The proposed incremental expansion framework sustains the MKN learning new knowledge.The early detection of Alzheimer's disease can potentially make eventual treatments more effective. This work presents a deep learning model to detect early symptoms of Alzheimer's disease using synchronization measures obtained with magnetoencephalography. The proposed model is a novel deep learning architecture based on an ensemble of randomized blocks formed by a sequence of 2D-convolutional, batch-normalization and pooling layers. An important challenge is to avoid overfitting, as the number of features is very high (25755) compared to the number of samples (132 patients). To address this issue the model uses an ensemble of identical sub-models all sharing weights, with a final stage that performs an average across sub-models. To facilitate the exploration of the feature space, each sub-model receives a random permutation of features. The features correspond to magnetic signals reflecting neural activity and are arranged in a matrix structure interpreted as a 2D image that is processed by 2D convolutional networks. The proposed detection model is a binary classifier (disease/non-disease), which compared to other deep learning architectures and classic machine learning classifiers, such as random forest and support vector machine, obtains the best classification performance results with an average F1-score of 0.92. To perform the comparison a strict validation procedure is proposed, and a thorough study of results is provided.

Lung cancer is the leading cause of cancer death worldwide. Prognosis of lung cancer plays a crucial role in the clinical decision-making process to optimize the treatment for patients. Most of the existing data-driven prognostic prediction models explore the relations between patient's characteristics and outcomes at a specific time interval. Although valuable, they neglect the relations between long-term and short-term prognoses and thus may limit the prediction performance.

In this study, we present a novel prognostic prediction approach for postoperative NSCLC patients. Specifically, we formulate the learning objective function by exploiting the relations between long-term and short-term prognoses via a long short-term relational regularization. The regularization term is composed of two parts, i.e., the similarities between prognoses measured by patients' outcomes and the L

-norms between the corresponding prognoses' weight vectors. Based on this regularization, the proposed method can extract critical risk factors that comprehensively consider the long-term and short-term prognoses to facilitate the estimation of clinical risks.