Computer-aided Prediction Research Articles

Prediction of Autopsy Verified Neuropathological Change of Alzheimer's Disease Using Machine Learning and MRI.

Background: Alzheimer’s disease (AD) is the most common form of dementia. While neuropathological changes pathognomonic for AD have been defined, early detection of AD prior to cognitive impairment in the clinical setting is still lacking. Pioneer studies applying machine learning to magnetic-resonance imaging (MRI) data to predict mild cognitive impairment (MCI) or AD have yielded high accuracies, however, an algorithm predicting neuropathological change is still lacking. The objective of this study was to compute a prediction model supporting a more distinct diagnostic criterium for AD compared to clinical presentation, allowing identification of hallmark changes even before symptoms occur.Methods: Autopsy verified neuropathological changes attributed to AD, as described by a combined score for Aβ-peptides, neurofibrillary tangles and neuritic plaques issued by the National Institute on Aging – Alzheimer’s Association (NIAA), the ABC score for AD, were predicted from structural MRI data with RandomForest (RF). MRI scans were performed at least 2 years prior to death. All subjects derive from the prospective Vienna Trans-Danube Aging (VITA) study that targeted all 1750 inhabitants of the age of 75 in the starting year of 2000 in two districts of Vienna and included irregular follow-ups until death, irrespective of clinical symptoms or diagnoses. For 68 subjects MRI as well as neuropathological data were available and 49 subjects (mean age at death: 82.8 ± 2.9, 29 female) with sufficient MRI data quality were enrolled for further statistical analysis using nested cross-validation (CV). The decoding data of the inner loop was used for variable selection and parameter optimization with a fivefold CV design, the new data of the outer loop was used for model validation with optimal settings in a fivefold CV design. The whole procedure was performed ten times and average accuracies with standard deviations were reported.Results: The most informative ROIs included caudal and rostral anterior cingulate gyrus, entorhinal, fusiform and insular cortex and the subcortical ROIs anterior corpus callosum and the left vessel, a ROI comprising lacunar alterations in inferior putamen and pallidum. The resulting prediction models achieved an average accuracy for a three leveled NIAA AD score of 0.62 within the decoding sets and of 0.61 for validation sets. Higher accuracies of 0.77 for both sets, respectively, were achieved when predicting presence or absence of neuropathological change.Conclusion: Computer-aided prediction of neuropathological change according to the categorical NIAA score in AD, that currently can only be assessed post-mortem, may facilitate a more distinct and definite categorization of AD dementia. Reliable detection of neuropathological hallmarks of AD would enable risk stratification at an earlier level than prediction of MCI or clinical AD symptoms and advance precision medicine in neuropsychiatry.

Computer-aided Drug Design and Drug Pharmacokinetic Prediction: A Mini-review.

Prediction of pharmacokinetics and drug targeting is a challenge in drug design. There are different types of software that can help to predict the pharmacokinetic profile of a drug. Quantitative structure-activity relationship (QSAR) modeling is used for drug design with less cost. Drug-excipient interactions are predicted by docking tools. Computerized drug target prediction and docking programs offer additional options to predict potential effects and adverse reactions of a given candidate as well as the best orientation of the compound on the receptor active site. Information on the absorption, distribution, metabolism and excretion of the drug in the body can enhance prediction of drug release and distribution in the blood and central nervous system (CNS). Computer- aided drug design and delivery can help to save the time and cost in the process of rational drug development.

Radiomics: a critical step towards integrated healthcare

Medical imaging is a vital part of the clinical decision-making process, especially in an oncological setting. Radiology has experienced a great wave of change, and the advent of quantitative imaging has provided a unique opportunity to analyse patient images objectively. Leveraging radiomics and deep learning, there is increased potential for synergy between physicians and computer networks—via computer-aided diagnosis (CAD), computer-aided prediction of response (CARP), and computer-aided biological profiling (CABP). The ongoing digitalization of other specialties further opens the door for even greater multidisciplinary integration. We envision the development of an integrated system composed of an aggregation of sub-systems interoperating with the aim of achieving an overarching functionality (in this case‚ better CAD, CARP, and CABP). This will require close multidisciplinary cooperation among the clinicians, biomedical scientists, and (bio)engineers as well as an administrative framework where the departments will operate not in isolation but in successful harmony.Key Points• The advent of quantitative imaging provides a unique opportunity to analyse patient images objectively.• Radiomics and deep learning allow for a more detailed overview of the tumour (i.e., CAD, CARP, and CABP) from many different perspectives.• As it currently stands, different medical disciplines have developed different stratification methods, primarily based on their own field—often to the exclusion of other departments.• The digitalization of other specialties further opens the door for multidisciplinary integration.• The long-term vision for precision medicine should focus on the development of integration strategies, wherein data derived from the patients themselves (via multiple disciplines) can be used to guide clinical decisions.

Computer-aided design and prediction of the functional properties of double-layered thermal barrier coatings NiAl-YSZ

An approach to the stochastic simulation of the formation process of the lamellar structure of double-layered thermal barrier coatings (TBCs), including high-velocity spraying of NiCrAlY bond layer and atmosphere plasma spraying of YSZ top layer, is proposed. The approach is based on the developed and experimentally verified theoretical foundations which could be used for rapid and sufficiently accurate prediction of thickness and diameter of metal and metal oxide splats depending on the key physical parameters (KPPs): temperature, velocity and size of particle, and substrate temperature. The results are presented, which concern the development of the computational algorithm and the program code for simulation the process of stochastic stacking of splats in the coating with regard for the topography of its surface, which varies dynamically at the spraying, as well as the formation of lamellar structure and porosity of the coating. Results of stochastic simulation of lamellar structure TBCs and its characteristics (microstructure, porosity, roughness) are given in conditions typical for atmospheric plasma spraying (APS) of YSZ top layer and for detonation (D-Gun Spraying) and supersonic flame spraying (HVOF) of bond layer. Comparison of characteristics of coatings for two model splats morphologies at their stochastic stacking on the sprayed surface is presented. The results of numerical experiments are presented through the examples of TBCs thermal spraying, illustrating the performance of the developed computational technology.

Interactome Mapping Uncovers a General Role for Numb in Protein Kinase Regulation

Cellular functions are frequently regulated by protein-protein interactions involving the binding of a modular domain in one protein to a specific peptide sequence in another. This mechanism may be explored to identify binding partners for proteins harboring a peptide-recognition domain. Here we report a proteomic strategy combining peptide and protein microarray screening with biochemical and cellular assays to identify modular domain-mediated protein-protein interactions in a systematic manner. We applied this strategy to Numb, a multi-functional protein containing a phosphotyrosine-binding (PTB) domain. Through the screening of a protein microarray, we identified >100 protein kinases, including both Tyr and Ser/Thr kinases, that could potentially interact with the Numb PTB domain, suggesting a general role for Numb in regulating kinase function. The putative interactions between Numb and several tyrosine kinases were subsequently validated by GST pull-down and/or co-immunoprecipitation assays. Furthermore, using the Oriented Peptide Array Library approach, we defined the specificity of the Numb PTB domain which, in turn, allowed us to predict binding partners for Numb at the genome level. The combination of the protein microarray screening with computer-aided prediction produced the most expansive interactome for Numb to date, implicating Numb in regulating phosphorylation signaling through protein kinases and phosphatases. Not only does the data generated from this study provide an important resource for hypothesis-driven research to further define the function of Numb, the proteomic strategy described herein may be employed to uncover the interactome for other peptide-recognition domains whose consensus motifs are known or can be determined.

Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis.

This study aimed to evaluate a new computational histology prediction system based on colorectal polyp textural surface patterns using high definition white light images. Textural elements (textons) were characterized according to their contrast with respect to the surface, shape, and number of bifurcations, assuming that dysplastic polyps are associated with highly contrasted, large tubular patterns with some degree of bifurcation. Computer-aided diagnosis (CAD) was compared with pathological diagnosis and the diagnosis made by endoscopists using Kudo and Narrow-Band Imaging International Colorectal Endoscopic classifications. Images of 225 polyps were evaluated (142 dysplastic and 83 nondysplastic). The CAD system correctly classified 205 polyps (91.1 %): 131/142 dysplastic (92.3 %) and 74/83 (89.2 %) nondysplastic. For the subgroup of 100 diminutive polyps (≤ 5 mm), CAD correctly classified 87 polyps (87.0 %): 43/50 (86.0 %) dysplastic and 44/50 (88.0%) nondysplastic. There were no statistically significant differences in polyp histology prediction between the CAD system and endoscopist assessment. A computer vision system based on the characterization of the polyp surface in white light accurately predicted colorectal polyp histology.

Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants

BackgroundEvidences in literature strongly advocate the potential of immunomodulatory peptides for use as vaccine adjuvants. All the mechanisms of vaccine adjuvants ensuing immunostimulatory effects directly or indirectly stimulate antigen presenting cells (APCs). While numerous methods have been developed in the past for predicting B cell and T-cell epitopes; no method is available for predicting the peptides that can modulate the APCs.MethodsWe named the peptides that can activate APCs as A-cell epitopes and developed methods for their prediction in this study. A dataset of experimentally validated A-cell epitopes was collected and compiled from various resources. To predict A-cell epitopes, we developed support vector machine-based machine learning models using different sequence-based features.ResultsA hybrid model developed on a combination of sequence-based features (dipeptide composition and motif occurrence), achieved the highest accuracy of 95.71% with Matthews correlation coefficient (MCC) value of 0.91 on the training dataset. We also evaluated the hybrid models on an independent dataset and achieved a comparable accuracy of 95.00% with MCC 0.90.ConclusionThe models developed in this study were implemented in a web-based platform VaxinPAD to predict and design immunomodulatory peptides or A-cell epitopes. This web server available at http://webs.iiitd.edu.in/raghava/vaxinpad/ will facilitate researchers in designing peptide-based vaccine adjuvants.

Computer-aided Prediction of Airborne Sound Transmission through the Front Car End

Computer-aided Prediction of Airborne Sound Transmission through the Front Car End

Computer-aided prediction model for axillary lymph node metastasis in breast cancer using tumor morphological and textural features on ultrasound

Background and objectivesAxillary lymph node (ALN) status is a key indicator in assessing and determining the treatment strategy for patients with newly diagnosed breast cancer. Previous studies suggest that sonographic features of a primary tumor have the potential to predict ALN status in the preoperative staging of breast cancer. In this study, a computer-aided prediction (CAP) model as well as the tumor features for ALN metastasis in breast cancers were developed using breast ultrasound (US) images. MethodsA total of 249 malignant tumors were acquired from 247 female patients (ages 20–84 years; mean 55 ± 11 years) to test the differences between the non-metastatic (130) and metastatic (119) groups based on various features. After applying semi-automatic tumor segmentation, 69 quantitative features were extracted. The features included morphology and texture of tumors inside a ROI of breast US image. By the backward feature selection and linear logistic regression, the prediction model was constructed and established to estimate the likelihood of ALN metastasis for each sample collected. ResultsIn the experiments, the texture features showed higher performance for predicting ALN metastasis compared to morphology (Az, 0.730 vs 0.667). The difference, however, was not statistically significant (p-values > 0.05). Combining the textural and morphological features, the accuracy, sensitivity, specificity, and Az value achieved 75.1% (187/249), 79.0% (94/119), 71.5% (93/130), and 0.757, respectively. ConclusionsThe proposed CAP model, which combines textural and morphological features of primary tumor, may be a useful method to determine the ALN status in patients with breast cancer.

ПОРІВНЯЛЬНИЙ АНАЛІЗ РЕЗУЛЬТАТІВ IN VIVO СКРИНІНГУ ТА IN SILICO ПРОГНОЗУВАННЯ ПСИХО- ТА НЕЙРОТРОПНИХ ВЛАСТИВОСТЕЙ 3-(N-R,R′-АМІНОМЕТИЛ)-2-МЕТИЛ-1Н-ХІНОЛІН-4-ОНІВ

Мета роботи. Провести аналіз результатів скринінгових in vivo досліджень та ретроспективного in silico прогнозування психо- та нейротропних властивостей 3-(N-R,R′-амінометил)-2-метил-1Н-хінолін-4-онів за допомогою програми PASS.Матеріали і методи. Аналіз результатів попередніх скринінгових in vivo досліджень психо- та нейротропних властивостей 3-(N-R,R′-амінометил)-2-метил-1Н-хінолін-4-онів ґрунтувався на опублікованих нами даних. Комп’ютерне прогнозування спектра біологічних властивостей 3-(N-R,R′-амінометил)-2-метил-1Н-хінолін-4-онів проведено ретроспективно за допомогою системи PASS Online.Результати й обговорення. Систематизація та аналіз результатів проведених раніше експериментальних фармакологічних досліджень показали, що більшість 3-(N-R,R′-амінометил)-2-метил-1Н-хінолін-4-онів має виразні ноотропні властивості, які у деяких похідних поєднуються з високою антидепресивною активністю, протитривожним ефектом, седативними або, навпаки, стимулювальними властивостями. Аналіз результатів для окремих підгруп 3-(N-R,R′-амінометил)-2-метил-1Н-хінолін-4-онів дозволив виявити певні зв’язки між хімічною будовою та характером біологічної дії сполук.Результати PASS-прогнозу показали, що найбільш імовірними біологічними ефектами 3-(N-R,R′-амінометил)-2-метил-1Н-хінолін-4-онів є здатність виступати інгібіторами убіхінол-цитохром с редуктази, глюконат 2-дегідрогенази, пластохінол-пластоціанін редуктази, кінази рецепторів тромбоцитарного фактора росту та енхансерами експресії 3-гідрокси-3-метилглутарил-КоА синтази 2. Показники для ефектів, які хоча б опосередковано можуть впливати на функції ЦНС (активатори потенціалзалежних кальцієвих каналів та інгібітори вивільнення серотоніну), мають значно нижчі значення.Висновки. За умови використання комп’ютерного прогнозування спектра фармакологічної активності 3-(N-R,R′-амінометил)-2-метил-1Н-хінолін-4-онів за допомогою програми PASS як основного підґрунтя для подальших скринінгових досліджень цей клас сполук було б втрачено як перспективний в аспекті пошуку нових речовин, здатних впливати на функції ЦНС.

Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitation

An essential characteristic of chemical compounds is their biological activity since its presence can become the basis for the use of the substance for therapeutic purposes, or, on the contrary, limit the possibilities of its practical application due to the manifestation of side action and toxic effects. Computer assessment of the biological activity spectra makes it possible to determine the most promising directions for the study of the pharmacological action of particular substances, and to filter out potentially dangerous molecules at the early stages of research. For more than 25 years, we have been developing and improving the computer program PASS (Prediction of Activity Spectra for Substances), designed to predict the biological activity spectrum of substance based on the structural formula of its molecules. The prediction is carried out by the analysis of structure-activity relationships for the training set, which currently contains information on structures and known biological activities for more than one million molecules. The structure of the organic compound is represented in PASS using Multilevel Neighborhoods of Atoms descriptors; the activity prediction for new compounds is performed by the naive Bayes classifier and the structure-activity relationships determined by the analysis of the training set. We have created and improved both local versions of the PASS program and freely available web resources based on PASS (http://www.way2drug.com). They predict several thousand biological activities (pharmacological effects, molecular mechanisms of action, specific toxicity and adverse effects, interaction with the unwanted targets, metabolism and action on molecular transport), cytotoxicity for tumor and non-tumor cell lines, carcinogenicity, induced changes of gene expression profiles, metabolic sites of the major enzymes of the first and second phases of xenobiotics biotransformation, and belonging to substrates and/or metabolites of metabolic enzymes. The web resource Way2Drug is used by over 18,000 researchers from more than 90 countries around the world, which allowed them to obtain over 600,000 predictions and publish about 500 papers describing the obtained results. The analysis of the published works shows that in some cases the interpretation of the prediction results presented by the authors of these publications requires an adjustment. In this work, we provide the theoretical basis and consider, on particular examples, the opportunities and limitations of computer-aided prediction of biological activity spectra.

Validating a prediction modeling tool for left ventricular outflow tract (LVOT) obstruction after transcatheter mitral valve replacement (TMVR).

Demonstrate proof-of-concept validation of a computed tomography (CT) computer-aided design prediction modeling tool to identify patients at risk for left ventricular outflow tract (LVOT) obstruction in transcatheter mitral valve replacement (TMVR). LVOT obstruction is a significant and even fatal consequence of TMVR. From August 2013 to August 2017, 38 patients in 5 centers underwent TMVR with compassionate use of balloon-expandable valves for severe mitral valve dysfunction because of degenerative surgical mitral ring, bioprosthesis, or severe native mitral stenosis from to severe mitral annular calcification. All patients had preprocedural CT scans performed for anatomic screening, intraprocedural TEE and invasive hemodynamics performed. Preprocedural prediction modeling was performed utilizing computer-aided design (CAD) of the neo-LVOT post-TMVR. Post-TMVR CT scans were obtained and compared to pre-TMVR LVOT modeling datasets for validation. All patients underwent successful TMVR without device embolization. Seven of the 38 patients experienced LVOT obstruction, defined as an increase of ≥10 mmHg LVOT peak gradient post-TMVR. Anatomic screening using CT was validated in 20/38 patients as preprocedural predicted neo-LVOT surface area correlated well with post-TMVR measurements (R2 = 0.8169, P < 0.0001). A receiver operating curve curve found a predicted neo-LVOT surface area of ≤ 189.4 mm2 to have 100% sensitivity and 96.8% specificity for predicting TMVR-induced LVOT obstruction. CAD design and CT postprocessing are indispensable tools in predicting LVOT obstruction and necessary for anatomic screening in percutaneous TMVR.

E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score

Machine Learning is a scientific discipline that addresses learning in context is not learning by heart but recognizing complex patterns and makes intelligent decisions based on data. Currently, students have to face the problem of selecting the best suitable university for admission in engineering. There is no predictor system that recommends the students to select the specific category which is best to its academic career. Students have to first appear in the entry test and can’t predict whether he/she can pass the entry test to get admitted in University. To tackle this problem the field of Machine Learning develops algorithms that discover knowledge from specific data and experience, based on sound statistical and computational principles. After going through the entry test students have to face problems for selecting the preferences among different categories due to the lack of knowledge of intake merits of preceding years. Another problem arises when students are waiting for admission in specific university, meanwhile, other universities finish their admission processes and select the students, but some students can’t take admission in any university due to no prediction system for admission in universities. In this work, we would like to develop an E-Assessment and Computer-Aided Prediction online system that enables the student to predict the entry test numbers by giving the Metric and Intermediate marks and other academic numbers. The suggested scheme has been demonstrated to perform at the maximum speed under MATLAB setup.

Computer-aided prediction of axillary lymph node status in breast cancer using tumor surrounding tissue features in ultrasound images

Background and objectiveThe presence or absence of axillary lymph node (ALN) metastasis is the most important prognostic factor for patients with early-stage breast cancer. In this study, a computer-aided prediction (CAP) system using the tumor surrounding tissue features in ultrasound (US) images was proposed to determine the ALN status in breast cancer. MethodsThe US imaging database used in this study contained 114 cases of invasive breast cancer and 49 of them were ALN metastasis. After the tumor region segmentation by the level set method, image matting method was used to extract surrounding abnormal tissue of tumor from the acquired images. Then, 21 features composed of 2 intensity, 3 morphology, and 16 textural features are extracted from the surrounding tissue and processed by a logistic regression model. Finally, the prediction model is trained and tested from the selected features. ResultsIn the experiments, the textural feature set extracted from surrounding tissue showed higher performance than intensity and morphology feature sets (Az, 0.7756 vs 0.7071 and 0.6431). The accuracy, sensitivity, specificity and the area index Az under the receiver operating characteristic (ROC) curve for the CAP system were 81.58% (93/114), 81.63% (40/49), 81.54% (53/65), and 0.8269 for using combined feature set. ConclusionsThese results indicated that the proposed CAP system can be helpful to determine the ALN status in patients with breast cancer.

Reproducibility of quantitative high-throughput BI-RADS features extracted from ultrasound images of breast cancer.

Digital Breast Imaging Reporting and Data System (BI-RADS) features extracted from ultrasound images are essential in computer-aided diagnosis, prediction, and prognosis of breast cancer. This study focuses on the reproducibility of quantitative high-throughput BI-RADS features in the presence of variations due to different segmentation results, various ultrasound machine models, and multiple ultrasound machine settings. Dataset 1 consists of 399 patients with invasive breast cancer and is used as the training set to measure the reproducibility of features, while dataset 2 consists of 138 other patients and is a validation set used to evaluate the diagnosis performances of the final reproducible features. Four hundred and sixty high-throughput BI-RADS features are designed and quantized according to BI-RADS lexicon. Concordance Correlation Coefficient (CCC) and Deviation (Dev) are used to assess the effect of the segmentation methods and Between-class Distance (BD) is used to study the influences of the machine models. In addition, the features jointly shared by two methodologies are further investigated on their effects with multiple machine settings. Subsequently, the absolute value of Pearson Correlation Coefficient (Rabs ) is applied for redundancy elimination. Finally, the features that are reproducible and not redundant are preserved as the stable feature set. A 10-fold Support Vector Machine (SVM) classifier is employed to verify the diagnostic ability. One hundred and fifty-three features were found to have high reproducibility (CCC > 0.9 & Dev < 0.1) within the manual and automatic segmentation. Three hundred and thirty-nine features were stable (BD < 0.2) at different machine models. Two feature sets shared the same 102 features, in which nine features were highly sensitive to the machine settings. Forty-six features were finally preserved after redundancy elimination. For the validation in dataset 2, the area under curve (AUC) of the 10-fold SVM classifier was 0.915. Three factors, segmentation results, machine models, and machine settings may affect the reproducibility of high-throughput BI-RADS features to various degrees. Our 46 reproducible features were robust to these factors and were capable of distinguishing benign and malignant breast tumors.

Computer-aided prediction and cytotoxicity evaluation of dithiocarbamates of 9,10-anthracenedione as new anticancer agents

Anticancer activity as an associated action for a series of dithiocarbamates of 9,10-anthracenedione was predicted using the PASS computer program and analysed with PharmaExpert software. The predicted cytotoxic activity of the dithiocarbamate derivatives of 9,10-anthracenedione was evaluated in vitro on cancer cells of the human lung (A549), prostate (PC3), colon (HT29) and human breast (MCF7) using the sulforhodamine B (SRB) cell viability assay. Among these compounds, 9,10-dioxo-9,10-dihydroanthracen-1-yl pyrrolidin-1-carbodithioate and 9,10-dioxo-9,10-dihydroanthracen-2-yl pyrrolidin-1-carbodithioate were identified as the most potent anticancer agents with cytotoxic activity against the MCF-7 human breast cell line with GI50 values of 1.40 μM and 1.52 μM, whereas the GI50 value for the reference anticancer drug mitoxantrone was 3.93 μM. Thus, anticancer activity predicted by PASS with a probability Pa > 30% was confirmed by the experiment. Relatively small Pa values estimated by PASS indicated the novelty of the considered derivatives comparing to the compounds from the PASS training set.

Morphology-based non-invasive quantitative prediction of the differentiation status of neural stem cells

Neural stem cells (NSCs) are multipotent and are considered ideal source for regenerating damaged neural cells for neurological disorders. During culture of NSCs, both the measurement and the evaluation of their differentiation potential are important to maintain stable quality-assured NSCs for regenerative treatments since the rate of differentiation into certain lineages from NSCs is still not fully controllable. However, conventional cell evaluation techniques using biological molecular are still invasive, costly, and time-consuming. Therefore, a non-invasive, low-cost, and rapid cell evaluation method is required to expand the possibilities of regenerative therapy, especially in the facilities that produce cells for therapy. To address these such technological limitations in non-invasive cell evaluation, we propose the efficacy of computer-aided morphology-based prediction of potentials of stem cells by using multiple and time-course morphological parameters from phase-contrast microscopic images combined with experimentally determined differentiation potentials. In this work, we quantified the morphological parameters of NSCs during three types of differentiation culture and investigated two applications with NSCs: (i) evaluation of their differentiation type and (ii) early prediction of neural differentiation rate. Our data demonstrate that it is possible to non-invasively evaluate neural differentiation types and quantitatively predict future differentiation rates by using morphological information from the first 4 days. Our findings indicate the potential application of morphology-based non-invasive evaluation for optimizing effective differentiation protocols, screening of compounds to mediate NSC differentiation, and quality maintenance of regenerative medicine products.

New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach

The complexity associated with the in-homogeneous nature of concrete suggests the necessity of conducting more in-depth behavioral analysis of this material in terms of different loading configurations. Distinctive feature of Gene Expression Programming (GEP) has been employed to derive computer-aided prediction models for the multiaxial strength of concrete under true-triaxial loading. The proposed models correlate the concrete true-triaxial strength (σ1) to mix design parameters and principal stresses (σ2,σ3), needless of conducting any time-consuming laboratory experiments. A comprehensive true-triaxial database is obtained from the literature to build the proposed models, subsequently implemented for the verification purposes. External validations as well as sensitivity analysis are further carried out using several statistical criteria recommended by researchers. More, they demonstrate superior performance to the other existing empirical and analytical models. The proposed design equations can readily be used for pre-design purposes or may be used as a fast check on deterministic solutions.

Computer-aided prediction of extent of motor recovery following constraint-induced movement therapy in chronic stroke

Constraint-induced movement therapy (CI therapy) is a well-researched intervention for treatment of upper limb function. Overall, CI therapy yields clinically meaningful improvements in speed of task completion and greatly increases use of the more affected upper extremity for daily activities. However, individual improvements vary widely. It has been suggested that intrinsic feedback from somatosensation may influence motor recovery from CI therapy. To test this hypothesis, an enhanced probabilistic neural network (EPNN) prognostic computational model was developed to identify which baseline characteristics predict extent of motor recovery, as measured by the Wolf Motor Function Test (WMFT). Individual characteristics examined were: proprioceptive function via the brief kinesthesia test, tactile sensation via the Semmes-Weinstein touch monofilaments, motor performance captured via the 15 timed items of the Wolf Motor Function Test, stroke affected side. A highly accurate predictive classification was achieved (100% accuracy of EPNN based on available data), but facets of motor functioning alone were sufficient to predict outcome. Somatosensation, as quantified here, did not play a large role in determining the effectiveness of CI therapy.

The impact of in silico screening in the discovery of novel and safer drug candidates.

Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in silico screening tools have gained considerable importance to archive, analyze and exploit the vast and ever-increasing amount of experimental data generated throughout the process. The current review will focus on the computer-aided prediction of the numerous properties that need to be controlled during the discovery of a preliminary hit and its promotion to a viable clinical candidate. It does not pretend to the almost impossible task of an exhaustive report but will highlight a few key points that need to be collectively addressed both by chemists and biologists to fuel the drug discovery pipeline with innovative and safe drug candidates.