Predicting match outcomes in professional Twenty20 cricket remains a challenging task due to the high variance of short-format games and the strong dependence on evolving team dynamics across seasons. Traditional rating-based approaches such as Elo provide a widely used baseline, but often fail to capture nuanced head-to-head competitiveness and season-specific context [1,2]. In this work, we propose a Dynamic Relative Probability Matrix (RPM) framework for forecasting outcomes in the Indian Premier League (IPL). The proposed approach represents team interactions through structured pairwise win-probability matrices that are updated over time, combining long-term historical tendencies with current-season adaptation. We evaluate the method on IPL match data from the 2018–2024 seasons and compare performance against rolling Elo baselines under a forward-chaining evaluation protocol. Across all seasons, the Dynamic RPM model achieves an average accuracy of approximately 54%, outperforming Elo by more than 3 percentage points. To assess robustness, we further conduct a McNemar significance test on paired match-level predictions, obtaining a p-value of 0.0034, which confirms that the improvement over Elo is statistically significant (p < 0.05) [3]. To assess generalizability, we further validate RPM on the English Premier League (EPL) using 9,380 matches across 24 seasons (2001/02–2024/25), where RPM achieves 67.71% average accuracy, remaining competitive with Elo (67.75%), Logistic Regression (67.53%), and XGBoost (67.32%) without any feature engineering. These results demonstrate that structured probability representations of head-to-head team dynamics offer a statistically reliable advantage over conventional rating systems for cricket match outcome prediction, highlighting the potential of RPM-based modelling in sports analytics.

Predicting which SARS-CoV-2 spike mutations enable antibody escape requires training labels whose biological relevance can be independently verified. We built a two-stage pipeline linking 905 Indian SARS-CoV-2 genome sequences to solved antibody-spike protein structures: an exhaustive similarity comparison against 311 candidate structures (281,455 pairwise comparisons), followed by three-dimensional contact-sphere analysis, producing 898 valid sequence-structure pairs. Validation revealed a mutation-calling artifact in 351 pairs (implausible substitution counts contradicting independently computed similarity scores), traced to an unhandled indel; we corrected this by indel-aware realignment rather than exclusion, recovering reliable labels for 844 of 898 pairs. On the corrected dataset, verified escape-relevant positions occurred in antibody-contact spheres at 3.29 times the rate expected by chance. Cross-referencing corrected hits against an independent deep mutational scanning dataset, hit positions showed significantly higher measured escape potential than background-drift mutations at the unique-position level (n = 54 versus 74 positions; rank-based p = 0.0054; permutation-on-median p = 0.0144; bootstrap 95% confidence interval on the median difference [0.006, 0.508]), corroborating the labels using data external to model construction. A graph attention network trained on the pre-correction dataset reached F1 0.98 and AUROC 0.998 on held-out strains of already-seen structures, but showed only a modest, size-dependent signal (Matthews correlation coefficient 0.21, AUROC 0.57 on adequately sampled folds) under a stricter leave-one-structure-out test simulating novel antibody targets; structural similarity between complexes did not predict this gap, and transfer instead depended on whether verified contact positions overlapped between structures. These model results predate the labeling correction and are reported with this caveat. These findings support structural contact verification as a meaningful, though not yet fully sufficient, proxy for functional antibody escape, and identify both structural representativeness of the reference set and label-pipeline correctness as primary determinants of reliable escape-mutation prediction.

Background: Ranking genes as CAR-T cell targets is limited by the small number of validated positives (168 of 3,220 candidate genes). Antibody-drug conjugates (ADCs), bispecific antibodies and monoclonal antibodies pursue the same kind of surface antigen, so evidence that another modality already targets a gene may help prioritize it for CAR-T therapy. Results: We built an audited evidence table for 3,220 genes that combines single-cell tumor expression for five cancers, protein topology, normal-tissue expression and CAR-T labels from three trial registries, a CAR-construct library and a curated list, together with ADC, bispecific and monoclonal antibody labels. A model with four outputs, one per modality (CAR-T, ADC, bispecific, monoclonal antibody), was scored in an evidence regime and a biology-only regime under nested, gene-grouped cross-validation with fresh-split re-checks. A label-free count of other-modality evidence reached an end-to-end Matthews correlation coefficient (MCC) of 0.516, matching the best learners. Biology alone ranked curated targets well above chance (ROC-AUC 0.78; best MCC 0.283), with logistic regression and XGBoost ahead of a multilayer perceptron (0.207). A two-stage screen-then-classify design reduced class imbalance from 1:16 to between 1:3.8 and 1:8.4 and produced a tiered gene list; with evidence inputs it reached MCC 0.503 with 32 false positives, against 68 for single-stage XGBoost. Conclusions: Other-modality evidence is the strongest available signal for prioritizing CAR-T target genes, and compact, interpretable learners are sufficient with limited data. The audited evidence table and the two-regime design give a transparent basis for ranking candidate genes.

Predicting match outcomes in professional Twenty20 cricket remains a challenging task due to the high variance of short-format games and the strong dependence on evolving team dynamics across seasons. Traditional rating-based approaches such as Elo provide a widely used baseline, but often fail to capture nuanced head-to-head competitiveness and season-specific context [1,2]. In this work, we propose a Dynamic Relative Probability Matrix (RPM) framework for forecasting outcomes in the Indian Premier League (IPL). The proposed approach represents team interactions through structured pairwise win-probability matrices that are updated over time, combining long-term historical tendencies with current-season adaptation.We evaluate the method on IPL match data from the 2018–2024 seasons and compare performance against rolling Elo baselines under a forward-chaining evaluation protocol. Across all seasons, the Dynamic RPM model achieves an average accuracy of approximately 54%, outperforming Elo by more than 3 percentage points. To assess robustness, we further conduct a McNemar significance test on paired match-level predictions, obtaining a p-value of 0.0034, which confirms that the improvement over Elo is statistically significant (p < 0.05) [3]. To assess generalizability, we further validate RPM on the English Premier League (EPL) using 9,380 matches across 24 seasons (2001/02–2024/25), where RPM achieves 67.71% average accuracy, remaining competitive with Elo (67.75%), Logistic Regression (67.53%), and XGBoost (67.32%) without any feature engineering.These results demonstrate that structured probability representations of head-to-head team dynamics offer a statistically reliable advantage over conventional rating systems for cricket match outcome prediction, highlighting the potential of RPM-based modelling in sports analytics.